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https://www.palantir.com/ find the latest 10-k and 10-q list their customers , products, industries served

Palantir’s latest full-year 10‑K is for FY 2024 (filed Feb 18, 2025), and the latest available 10‑Q is for Q1 2025 (filed May 6, 2025).123456

Below is what those filings and related investor materials say about customers, products, and industries; note that SEC filings generally do not enumerate all customers by name, but instead give examples and aggregate stats.

Latest 10‑K and 10‑Q​

  • FY 2024 Form 10‑K (year ended Dec 31, 2024), filed Feb 18, 2025.4561
  • Q1 2025 Form 10‑Q (quarter ended Mar 31, 2025), filed May 6, 2025.236

The 10‑Q states there were “no material changes outside the ordinary course of business” to commitments and risk profile relative to the 2024 10‑K.2

Customers (from latest filings / investor commentary)​

The 2024 10‑K and related investor commentary highlight customer count, mix, and concentration, but list only a few customers by name (and mostly in narrative, not as a comprehensive list).7814

Key points:

  • Total customers: 711 customers across 90 industries as of FY 2024.4
  • Revenue mix 2024: ~55–56% government, ~44–45% commercial.74
  • Geography: ~66% of revenue from U.S., 34% international.4

Named / exemplar customers and user organizations (from filings, public product descriptions, and investor commentary, not an exhaustive list):

  • Government and public sector:
    • U.S. Department of Defense and U.S. Intelligence Community (USIC).817
    • U.K. National Health Service (NHS).87
    • Various law‑enforcement and defense agencies in Europe and allied countries (described at aggregate level).178
  • Commercial (illustrative examples):
    • Airbus (manufacturing and aerospace).7
    • Merck (pharmaceuticals).7
    • Oil and gas firms and other critical infrastructure operators (energy, utilities).7

Customer structure / deal profile:

  • Palantir targets large, high‑value, long‑cycle contracts, with many deals ≥1M, ≥5M, and ≥10M closed per quarter (e.g., 129 ≥1M, 58 ≥5M, 32 ≥10M deals in a recent quarter referenced in the 2024 10‑K investor summary).4
  • 10‑K describes customer concentration and reliance on large government contracts as a key risk factor (no full name list, but they emphasize large U.S. government and allied government entities).14

Because of security and confidentiality constraints, the 10‑K/10‑Q do not publish a full customer list; instead they provide counts, segments, and a few named exemplars.214

Products / Platforms​

The 2024 10‑K states that Palantir has four principal software platforms: Gotham, Foundry, Apollo, and the Artificial Intelligence Platform (AIP).814

  • Palantir Gotham
    • Described as an intelligence and operations platform used by militaries, intelligence, and law‑enforcement agencies for counterterrorism and defense analysis.18
    • Originated in counterterrorism and is used for investigations, operations, and mission planning.81
  • Palantir Foundry
    • A data integration and operations platform aimed largely at commercial enterprises and some government use cases.187
    • Integrates heterogeneous operational data, supports modeling and analytics, and serves as an “operating system” for data‑driven operations across industries such as airlines, pharma, and energy.417
  • Palantir Apollo
    • A cloud‑agnostic, single control layer that orchestrates deployment and continuous delivery of Palantir software across cloud, on‑prem, edge, and classified environments.817
    • Used for mission‑critical software deployment scenarios, including defense and other high‑availability contexts.17
  • Palantir Artificial Intelligence Platform (AIP)
    • Introduced as a platform to embed AI (including large language models) into Gotham and Foundry workflows, enabling AI‑driven decision‑making “to every decision.”9481
    • 10‑K commentary emphasizes AIP as a core growth driver, especially in U.S. commercial business, and as Palantir’s response to commoditization of LLMs.94

The 10‑K characterizes Gotham and Foundry as central operating systems for customers, with AIP integrated into them and backed by Apollo as the deployment fabric.481

Industries Served​

The FY 2024 10‑K and associated investor summary reference usage across 90 industries, with examples but not a full enumerated taxonomy.14

Representative industries and sectors:

  • Government / public sector:
    • Defense and national security (militaries, DoD, intelligence community, NATO‑aligned forces).781
    • Law enforcement and public safety.81
    • Healthcare and public health (e.g., NHS, U.S. health agencies).78
    • Disaster relief and emergency management.1
  • Commercial sectors (examples called out in product and investor materials):
    • Aerospace and aviation (e.g., Airbus for manufacturing optimization).7
    • Pharmaceuticals and life sciences (e.g., Merck, drug development and manufacturing).7
    • Oil and gas, energy, and critical infrastructure operators.17
    • Manufacturing and industrials (supply‑chain and operations optimization).417
    • Financial services and other large enterprises (discussed at a high level in filings as part of “commercial” segment).41

The filings emphasize that Foundry is becoming a central operating system “for entire industries,” and that Palantir’s platforms are used across both government and commercial sectors globally.41

If you want to dig directly into the filings​

For primary‑source reading and your own parsing:

Suggestion for a focused follow‑up prompt​

If you want a machine‑readable mapping from Palantir’s latest 10‑K/10‑Q to a structured list you can feed into your own agents, you could ask:

“Parse Palantir’s FY 2024 10‑K and Q1 2025 10‑Q and produce a structured JSON with: (1) products/platforms and descriptions; (2) each explicitly named customer and its sector; (3) all industry verticals mentioned, normalized; (4) revenue by segment and geography; and (5) risk factors specifically referencing customer concentration or industry dependence. Keep keys stable and machine‑friendly.” 101112131415

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we need the Q4 2025 and Q1 2026

For Q4 2025 and Q1 2026, Palantir has released earnings materials and guidance, but as of early Feb 2026 the corresponding 10‑K (covering FY 2025, including Q4) and the first 10‑Q that would cover Q1 2026 are not yet visible in SEC/IR feeds.1617181920

What exists for Q4 2025​

Public documents currently available for Q4 2025:

From these:

  • Q4 2025 revenue growth ~70–82% year‑over‑year, with U.S. commercial revenue up ~137% Y/Y and 28% Q/Q to about 507M.212220
  • Q4 2025 U.S. government revenue grew ~66% Y/Y and 17% Q/Q to ~570M.22
  • FY 2025 adjusted free cash flow ~2.27B (51% margin), Q4 FCF ~791M (56% margin).1822

However, these are earnings materials (8‑K exhibits, shareholder letter, presentation), not the full Form 10‑K for FY 2025.1918

Status of the FY 2025 10‑K (covers Q4 2025)​

  • Palantir’s EDGAR company page currently shows 10‑K filings through FY 2024, with Q1/Q2/Q3 2025 10‑Qs, but the FY 2025 10‑K (which would include Q4 2025 detail) is not yet listed as of the latest index snapshot.2319
  • Secondary aggregators (Nasdaq filings page, StockTitan, etc.) similarly show 10‑K only up to FY 2024 and latest 10‑Qs up to 2025, but nothing yet labeled as a 10‑K for period ending Dec 31, 2025.242516

Given the normal SEC timeline, that FY 2025 10‑K should appear on EDGAR and Palantir IR once filed; it just doesn’t appear in the feeds I can see yet.1619

Status of Q1 2026 10‑Q​

  • The Q1 2026 quarter would end Mar 31, 2026, with a corresponding 10‑Q typically filed in early May 2026.
  • As of the current date (early Feb 2026), Q1 2026 has not closed, so no Q1 2026 10‑Q exists yet.19

What we do have regarding Q1 2026 comes from guidance in the Q4 2025 earnings communication:

  • The Q4 2025 earnings news release / IR page gives Q1 2026 revenue guidance of approximately 1.532–1.536B.17
  • The same page references risk factors and prior filings (including the 10‑Q for the quarter ended Sep 30, 2025) but does not provide a filed Q1 2026 10‑Q, which is not yet due.1723

How to grab them once they are filed​

Once the FY 2025 10‑K and Q1 2026 10‑Q are available, you can pull them from:

If you want, I can next help you draft a parsing spec / scraper pipeline that assumes those URLs and extracts customers, industries, products, and any AIP‑specific disclosures once the forms post. 26272829

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What drove Palantir's 137% US commercial growth in Q4 2025

Palantir Q1 2026 earnings call date and details How does Q4 2025 compare to previous quarters Palantir full year 2026 revenue guidance Breakdown of Palantir's major deals in Q4 2025

Palantir’s 137% U.S. commercial growth in Q4 2025 was driven by rapid adoption of AIP, a surge in large commercial deals, and broad-based expansion across industries, and the company paired that with aggressive 2026 guidance and a Q1 2026 earnings call scheduled for early May 2026.30313233343536

Drivers of 137% U.S. Commercial Growth (Q4 2025)​

Key contributors, as described in the Q4 2025 release, deck, and call:

  • AIP-led demand: Management repeatedly attributes U.S. commercial acceleration to demand for the Artificial Intelligence Platform (AIP), highlighting “explosive demand” and AIP-based pilots converting into scaled production deals.32353630
  • Large deal volume and up-sell: In Q4 2025 Palantir closed 180 contracts ≥1M, 84 ≥5M, and 61 ≥10M, pushing remaining commercial deal value up ~145% Y/Y to about 4.38B, a large part of which stems from U.S. commercial.343032
  • Customer and seat expansion: The customer base grew ~34% Y/Y and 5% Q/Q, and commentary stresses expansion within existing U.S. enterprises (more production workloads, more users) rather than just logo adds.303234
  • Industry breadth: The call and commentary point to AIP usage spreading in financial services, healthcare, manufacturing, and critical infrastructure, with AIP becoming an “operating system for AI” for many U.S. enterprises.353630

Net effect: U.S. commercial revenue reached ~507M in Q4 2025, up 137% Y/Y and ~28–29% sequentially.323430

Q1 2026 Earnings Call Date and Details​

  • Q4 2025 earnings (covering FY 2025) were held on 2 February 2026.373532
  • Palantir’s IR “Events” calendar shows the standard cadence: Q1 earnings around early May (e.g., Q1 2025 was 5 May 2025, Q2 2025 was 4 August 2025, Q3 2025 was 3 November 2025, Q4 2025 on 2 February 2026).37
  • Extrapolating from this pattern and third-party earnings calendars (Wall Street Horizon, TipRanks), the Q1 2026 earnings call is expected in the first week of May 2026 (around 4–6 May 2026), but the exact date has not yet been formally posted on the IR events page.383937

Guidance given for that call:

  • Q1 2026 revenue guidance: 1.532–1.536B.333632
  • Q1 2026 adjusted operating income guidance: 870–874M.333432

How Q4 2025 Compares to Prior Quarters​

Core financial comparison vs Q3 2025:

MetricQ3 2025Q4 2025Notes
Total revenue~1.004B 401.407B 32+40% Q/Q approx., +70% Y/Y in Q4. 3240
U.S. commercial revenue397M 4142507M 343032+29% Q/Q, +137% Y/Y in Q4. 303241
U.S. government revenue486M 41570M 343235+17% Q/Q, +66% Y/Y in Q4. 343235
GAAP operating margin33% 414241% 3234Margin expansion Q/Q.
Adjusted operating margin51% 414257% 3234Record profitability.
Cash from operations508M 41777M 32+~53% Q/Q; 55% margin in Q4. 3241
Adjusted free cash flow540M 41791M 323446% → 56% margin.

Directional takeaways:

  • Growth re-accelerated: Revenue growth Y/Y stepped up from 63% in Q3 to 70% in Q4, largely on commercial momentum.414232
  • Commercial mix: U.S. commercial growth (137% Y/Y) outpaced government (66% Y/Y), shifting more weight toward commercial while improving profitability.343532
  • Profitability: Both GAAP and adjusted operating margins rose, with Q4 2025 delivering record margin plus record FCF.423234

Full-Year 2026 Revenue and Profitability Guidance​

From the Q4 2025 earnings release and investor deck:

  • FY 2026 total revenue: 7.182–7.198B (≈61% Y/Y growth vs 4.48B in 2025).30323334
  • FY 2026 U.S. commercial revenue: expected to exceed 3.144B, implying ≥115% Y/Y growth.323430
  • FY 2026 adjusted operating income: 4.126–4.142B.3432
  • FY 2026 adjusted free cash flow: 3.925–4.125B.3234
  • Management also guides to GAAP operating income and net income every quarter of 2026.32

A separate analysis piece cites the same guidance range and notes that it sits materially above Street consensus (around 6.22B), underscoring how aggressively Palantir is leaning into AIP-driven growth.363330

Major Deals and Deal Breakdown in Q4 2025​

Disclosed deal metrics in Q4 2025:

  • 180 contracts ≥1M, 84 contracts ≥5M, 61 contracts ≥10M in the quarter.3432
  • Record total contract value (TCV) of 4.262B, up 138% Y/Y.32
  • Remaining commercial deal value up 145% Y/Y to ~4.38B (commentary attributes much of this to U.S. commercial AIP deals).3032

The company does not enumerate all large contracts by customer in the release, but qualitative disclosures emphasize:

  • Multiple nine-figure and high eight-figure AIP agreements with large U.S. enterprises across finance, healthcare, industrials, and critical infrastructure.353032
  • Ongoing expansion of existing government programs (DoD, intelligence, allied defense) but with commercial deals now constituting a larger proportion of incremental TCV than in prior years.3532
  • Broad participation in AIP “bootcamps” that convert into pilots and then multi-year contracts, described as an important part of the Q4 deal pipeline conversion.3635

Because the 8‑K and IR materials aggregate deals by size band rather than naming counterparties, you get a deal-size breakdown but not a public, customer-by-customer list.353432

If you want a next-step prompt for deeper, machine-usable analysis:

“Using Palantir’s Q3 and Q4 2025 earnings releases, investor presentations, and the Q4 2025 earnings call transcript, build a JSON dataset capturing by quarter: total revenue, U.S. commercial and U.S. government revenue, deal-count buckets (≥1M/≥5M/≥10M), remaining commercial deal value, and all explicit 2026 guidance points (revenue, U.S. commercial, adj. op income, FCF). Normalize keys and compute Q/Q and Y/Y deltas for each metric.” 4344

⁂

what is AIP? Using Palantir’s Q3 and Q4 2025 earnings releases, investor presentations, and the Q4 2025 earnings call transcript, build a JSON dataset capturing by quarter: total revenue, U.S. commercial and U.S. government revenue, deal-count buckets (≥1M/≥5M/≥10M), remaining commercial deal value, and all explicit 2026 guidance points (revenue, U.S. commercial, adj. op income, FCF). Normalize keys and compute Q/Q and Y/Y deltas for each metric.” What key customers or sectors drove Q4 2025 US commercial deals

Details on Palantirs AI platforms contributing to Q4 growth Q1 2026 earnings call date and time How did Palantir stock react to Q4 2025 earnings Breakdown of Q4 2025 US government revenue growth

Palantir’s AIP (Artificial Intelligence Platform) is the AI layer that sits on top of Gotham/Foundry to orchestrate and govern LLMs and other models on operational data, and it is the main driver of the 137% U.S. commercial growth in Q4 2025.45464748

What is AIP?​

  • AIP is described in Palantir’s Q3/Q4 2025 materials as the platform that lets enterprises safely plug LLMs and other AI models into their real operational data and workflows (Gotham/Foundry), with strong security, governance, and auditability.464745
  • Management explicitly frames AIP as the “operating system for AI” for customers, enabling use cases like AI copilots for analysts, automated decision-making in operations, and AI agents acting on data and applications built in Foundry/Gotham.474845
  • They highlight AIP “bootcamps” where customers rapidly prototype AI use cases that often convert into multi‑million‑dollar, multi‑year contracts, and they tie Q4’s deal surge directly to these AIP bootcamps and production deployments.4847

Key Q4 2025 US Commercial Drivers (Customers/Sectors)​

Palantir does not list individual Q4 U.S. commercial customers by name in the release or slides, but the Q4 2025 materials and commentary point to specific sectors:

  • Coverage and analysis pieces plus the deck emphasize strong AIP traction in:
    • Financial services and insurance (risk, fraud, underwriting, trading, compliance).494547
    • Healthcare and life sciences (clinical operations, R&D, pharma manufacturing).454749
    • Industrial/manufacturing and supply chain (optimization, quality, predictive maintenance).464745
    • Critical infrastructure/energy (grid operators, utilities, O&G, logistics).474546
  • Q4 commentary describes “multiple nine‑figure and high eight‑figure AIP deals” in U.S. commercial, but keeps the counterparties confidential; remaining commercial deal value rose ~145% Y/Y to ~4.38B largely from these AIP‑led U.S. commercial wins.49454647
  • U.S. commercial customer count increased ~49% Y/Y to 571, and Palantir closed 180 deals ≥1M, 84 ≥5M, and 61 ≥10M in Q4, showing that the growth is broad‑based, not a single customer.454647

Palantir AI Platforms and Q4 2025 Growth​

How each platform contributed, per Q3/Q4 releases, slides, and call:

  • AIP (AI layer):
    • Primary growth engine for U.S. commercial; management repeatedly links 137% Y/Y U.S. commercial growth and 28% Q/Q growth to AIP adoption.484745
    • Drives “bootcamp → pilot → production” funnels, producing multi‑year contracts and large expansions in seat count, particularly in U.S. financials, healthcare, and industrials.4748
  • Foundry (data/operating system):
    • Provides the data integration, ontology, and application substrate on which AIP is deployed; much of the commercial growth is Foundry + AIP workloads.464547
    • Q3 and Q4 materials describe Foundry as the “operating system for the modern enterprise,” increasingly embedded into core operations across those sectors.505147
  • Gotham (defense/intel) and Apollo (deployment fabric):
    • Gotham continues to drive defense and government use cases, while AIP is being brought to those users as well.4546
    • Apollo underpins deployment of Foundry/Gotham/AIP across cloud, on‑prem, and edge, enabling large government and commercial customers to run these workloads in highly regulated and classified environments.4645

Net effect: AI platforms (AIP on top of Foundry/Gotham, deployed via Apollo) underpin both the U.S. commercial surge and strong U.S. government growth in Q4 2025.48474546

Q1 2026 Earnings Call Date and Time​

  • Wall Street Horizon’s earnings calendar lists Palantir’s next earnings date as Monday, May 4, 2026, After Market (UNCONFIRMED) for Q1 2026.52
  • This fits the historical pattern (Q1 2025 was 5 May 2025, after market close).5352
  • As of now, Palantir’s own IR “Events” page does not yet show a confirmed date, so May 4, 2026, 5:00 PM ET (typical time) should be treated as indicative, not final.5452

Stock Reaction to Q4 2025 Earnings​

  • Coverage after the 2 February 2026 print describes Palantir stock “rallying” on the results, with a strong positive move driven by the 137% U.S. commercial growth and 61% 2026 revenue guidance.554947
  • One reaction piece notes that shares spiked in after‑hours trading immediately following the release and call; it characterizes the results as “stellar Q4 earnings” with revenue and margins beating expectations.5547
  • Articles also highlight that Palantir’s Rule of 40 score hit 127% in Q4 2025, underpinning bullish sentiment about AI‑driven growth in 2026.47

(Exact percentage price moves vary by source and intraday data; the qualitative consensus is a strong positive reaction.)554947

Q4 2025 U.S. Government Revenue Growth​

From the Q4 2025 release and slides:

  • U.S. government revenue was 570M in Q4 2025, up 66% Y/Y and 17% Q/Q.4546
  • A slide in the Q4 deck compares U.S. government revenue of 343M in Q4 2024 versus 570M in Q4 2025, explicitly labeling the 66% Y/Y increase.46
  • Combined U.S. revenue (government + commercial) exceeded 1.076B for the first time in a quarter, with U.S. revenue up 93% Y/Y and 22% Q/Q.564745

The company does not break U.S. government revenue further by agency/sector in public materials; they describe broad strength in defense and national security programs, including expansion of existing DoD and intel programs, plus continued adoption of AI capabilities in those settings.484745

About the JSON dataset request​

To build the JSON dataset you described (Q3 and Q4 2025 by quarter with revenue, deal buckets, remaining commercial deal value, and 2026 guidance plus Q/Q and Y/Y deltas), the necessary figures are in:

  • Q3 2025 release and slides (revenue, U.S. commercial/government, deal count buckets, RPO/remaining deal value).575150
  • Q4 2025 release, slides, and transcript (same metrics plus 2026 guidance).47484546

If you want, I can next produce a concrete JSON schema plus an example partially‑filled object with these Q3/Q4 2025 numbers and computed deltas, ready to drop into your agent tooling. 585960616263

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What is Palantir AIP and how does it drive commercial growth

Palantir FY 2026 revenue guidance details Breakdown of Palantir Q4 2025 deal sizes and customer count How does AIP compare to competitors like Snowflake or Nvidia integrations Palantir Q1 2026 earnings preview and expectations. build the JSON dataset you described (Q3 and Q4 2025 by quarter with revenue, deal buckets, remaining commercial deal value, and 2026 guidance plus Q/Q and Y/Y deltas), the necessary figures are in: Q3 2025 release and slides (revenue, U.S. commercial/government, deal count buckets, RPO/remaining deal value). Q4 2025 release, slides, and transcript (same metrics plus 2026 guidance). If you want, I can next produce a concrete JSON schema plus an example partially‑filled object with these Q3/Q4 2025 numbers and computed deltas, ready to drop into your agent tooling

Palantir’s AIP (Artificial Intelligence Platform) is the AI “operating system” that sits on top of Foundry/Gotham and orchestrates LLMs and other models over enterprise and government data; it is the primary driver of Palantir’s recent U.S. commercial acceleration.64656667

1. What is AIP and how it drives commercial growth​

  • AIP is positioned in Q3/Q4 2025 materials as the layer that lets customers safely bind LLMs and other AI models to their operational data and applications, with security, governance, and auditability inherited from Foundry/Gotham.656664
  • AIP “bootcamps” are highlighted as a core go‑to‑market motion: customers stand up AI use cases in days, then convert into multi‑million, multi‑year contracts, which Palantir cites as the main engine behind 137% Y/Y and 28% Q/Q U.S. commercial revenue growth in Q4 2025.666765
  • Management repeatedly describes AIP as compounding “AI leverage” and being central to record U.S. commercial TCV and remaining deal value (RDV), especially in financial services, healthcare, industrials, and critical infrastructure.676566

2. FY 2026 revenue and profitability guidance​

From the Q4 2025 release:6466

  • FY 2026 total revenue: 7.182–7.198B (≈61% Y/Y growth vs 4.475B in 2025).66
  • FY 2026 U.S. commercial revenue: expected to exceed 3.144B, implying ≥115% Y/Y growth.66
  • FY 2026 adjusted operating income: 4.126–4.142B.6466
  • FY 2026 adjusted free cash flow: 3.925–4.125B.6466
  • They guide to GAAP operating income and GAAP net income in every quarter of 2026.66

These figures underpin the bullish 2026 AI narrative and are explicitly tied to continued AIP adoption.686566

3. Q4 2025 deal sizes and customer counts​

From Q4 2025 investor presentation and press release:6466

  • Deals closed in Q4 2025:
    • 180 deals ≥1M.6664
    • 84 deals ≥5M.6466
    • 61 deals ≥10M.6664
  • Total contract value (TCV):
    • Record TCV of 4.26B, up 138% Y/Y.6964
  • U.S. commercial deal metrics:
    • U.S. commercial TCV of 1.344B, up 67% Y/Y.66
    • U.S. commercial remaining deal value (RDV) 4.38B, up 145% Y/Y and 21% Q/Q.6466
  • Customer count:
    • Global customer base up 34% Y/Y and 5% Q/Q (U.S. commercial customers called out as particularly strong).656766

Palantir does not publish a name‑by‑name list of customers for these deals; disclosures are aggregated by size bands and segment.6466

4. AIP vs competitors (Snowflake, Nvidia‑centric stacks)​

Conceptual positioning​

  • AIP vs Snowflake + LLMs:
    • Snowflake is mainly a cloud data platform; AI/ML is brought in via Snowpark, Cortex, or external LLMs.70
    • AIP is framed as an end‑to‑end AI operations layer on top of Palantir’s ontology and application stack (Foundry/Gotham), focused on actioning AI inside decision workflows, with strong policy controls, not just model hosting or retrieval.656664
    • Q3/Q4 2025 commentary emphasizes AIP’s ability to bind model actions to role‑based access, approvals, and audit trails across mission‑critical processes — Palantir’s differentiation vs pure data or model platforms.716765
  • AIP vs Nvidia‑centric integrations:
    • Q3 2025 materials highlight partnerships and integrations with Nvidia (e.g., using Nvidia GPUs, accelerated compute, and reference architectures), but AIP is the software control plane orchestrating models and workflows on top of that hardware stack.7271
    • Whereas Nvidia focuses on silicon, CUDA, frameworks, and model stacks, Palantir pitches AIP as the orchestration and governance environment enterprises actually use to deploy AI across lines of business.7271

In short, AIP is marketed less as a raw AI infra product and more as a governed AI operating system wired into operational data and apps, sitting “above” data clouds and model/hardware providers.72656664

5. Q1 2026 earnings preview & expectations​

From Q4 2025 release and Q4 preview coverage:687266

  • Q1 2026 guidance:
    • Revenue: 1.532–1.536B.66
    • Adjusted operating income: 870–874M.66
  • Narrative:
    • Analysts highlight this as continued high‑growth trajectory, with ongoing AIP demand expected to keep U.S. commercial growth elevated into 2026.736872
    • Preview pieces emphasize focus areas for Q1 2026: sustained deal flow in U.S. commercial, evidence of AIP adoption outside early‑adopter verticals, and durability of U.S. government growth given tough comps.6872

Earnings call date/time:

  • Third‑party calendars list Monday, May 4, 2026, after market close (around 5:00 PM ET) as the expected Q1 2026 earnings conference call, but IR has not yet formally confirmed this.747576

6. JSON dataset for Q3 & Q4 2025 (schema + example)​

Below is a compact JSON structure you can drop into your pipeline. Values are based on Q3/Q4 2025 releases, slides, and summaries; some Q3 segment splits are approximated where sources round, but they match cited figures.777871676466

Schema

{
  "quarters": [
    {
      "quarter": "2025-Q3",
      "total_revenue": {
        "value_musd": 1181,
        "yoy_growth_pct": 63,
        "qoq_growth_pct": null
      },
      "us_commercial_revenue": {
        "value_musd": 397,
        "yoy_growth_pct": 121,
        "qoq_growth_pct": null
      },
      "us_government_revenue": {
        "value_musd": 486,
        "yoy_growth_pct": 52,
        "qoq_growth_pct": null
      },
      "deal_counts": {
        "ge_1m": 204,
        "ge_5m": 91,
        "ge_10m": 53
      },
      "us_commercial_tcv": {
        "value_musd": 1310,
        "yoy_growth_pct": 342,
        "qoq_growth_pct": null
      },
      "us_commercial_remaining_deal_value": {
        "value_musd": 3630,
        "yoy_growth_pct": 199,
        "qoq_growth_pct": 30
      }
    },
    {
      "quarter": "2025-Q4",
      "total_revenue": {
        "value_musd": 1407,
        "yoy_growth_pct": 70,
        "qoq_growth_pct": 19
      },
      "us_commercial_revenue": {
        "value_musd": 507,
        "yoy_growth_pct": 137,
        "qoq_growth_pct": 28
      },
      "us_government_revenue": {
        "value_musd": 570,
        "yoy_growth_pct": 66,
        "qoq_growth_pct": 17
      },
      "deal_counts": {
        "ge_1m": 180,
        "ge_5m": 84,
        "ge_10m": 61
      },
      "us_commercial_tcv": {
        "value_musd": 1344,
        "yoy_growth_pct": 67,
        "qoq_growth_pct": 3
      },
      "us_commercial_remaining_deal_value": {
        "value_musd": 4380,
        "yoy_growth_pct": 145,
        "qoq_growth_pct": 21
      }
    }
  ],
  "fy_2026_guidance": {
    "revenue": {
      "low_musd": 7182,
      "high_musd": 7198,
      "yoy_growth_pct": 61
    },
    "us_commercial_revenue": {
      "min_musd": 3144,
      "min_yoy_growth_pct": 115
    },
    "adjusted_operating_income": {
      "low_musd": 4126,
      "high_musd": 4142
    },
    "adjusted_free_cash_flow": {
      "low_musd": 3925,
      "high_musd": 4125
    },
    "q1_2026": {
      "revenue": {
        "low_musd": 1532,
        "high_musd": 1536
      },
      "adjusted_operating_income": {
        "low_musd": 870,
        "high_musd": 874
      }
    }
  }
}

Key data sources:

  • Q3 2025 revenue, segment splits, deal counts, TCV, RDV, and guidance:78777167
  • Q4 2025 revenue, segment splits, deal counts, TCV, RDV, and 2026 guidance:69656466

If you’d like, I can next refine this into a more normalized, multi‑table JSON (e.g., metrics_by_quarter, guidance, segments) suitable for direct ingestion into your agentic analytics workflows. 7980818283

⁂

Palantir FY 2026 full year revenue guidance range

Breakdown of Palantir US commercial customer growth Q4 2025 Key features and capabilities of Palantir AIP Palantir AIP case studies in commercial sectors How Palantir AIP differentiates from Snowflake or Databricks. refine this into a more normalized, multi‑table JSON (e.g., metrics_by_quarter, guidance, segments) suitable for direct ingestion into your agentic analytics workflows.

Palantir guides FY 2026 revenue to 7.182–7.198B, driven heavily by AIP‑led U.S. commercial expansion and large multi‑year deals across multiple sectors.8485868788

FY 2026 full‑year revenue guidance​

  • Total FY 2026 revenue: 7.182–7.198B (≈61% Y/Y growth vs 4.48B in 2025).868788
  • FY 2026 U.S. commercial revenue: at least 3.144B, implying ≥115% Y/Y growth.858788
  • FY 2026 adjusted operating income: 4.126–4.142B.8788
  • FY 2026 adjusted free cash flow: 3.925–4.125B.8887

These ranges are explicitly tied to continued AIP adoption and strong commercial TCV/RDV exiting Q4 2025.848788

Q4 2025 U.S. commercial customer growth​

  • U.S. commercial revenue Q4 2025: 507M, +137% Y/Y and +28% Q/Q.868884
  • U.S. commercial customer count: 571, +49% Y/Y and +?% Q/Q (Q/Q cited as “5% overall customer count,” with commentary that U.S. commercial is a major contributor).8584
  • U.S. commercial TCV: 1.344B, +67% Y/Y.85
  • U.S. commercial remaining deal value (RDV): 4.38B, +145% Y/Y and +21% Q/Q.8685

The pattern: many more customers, but even more growth in deal size and remaining value, reflecting AIP‑driven expansions and multi‑year contracts rather than just logo adds.848586

Key AIP features and capabilities​

From Palantir materials and AIP‑focused content:89909192

  • Governed AI on operational data: AIP connects LLMs and other models to live operational data in Foundry/Gotham with fine‑grained access control, encryption, and audit trails, preserving enterprise‑grade security and compliance.919289
  • AI agents and copilots: Lets organizations build AI agents and copilots that can read, reason, and act inside workflows (e.g., supply chain decisions, underwriting, maintenance scheduling) rather than just generate text.929391
  • Contextual architecture and ontologies: Uses Foundry’s ontologies to give models business context, so outputs are aligned with domain semantics and existing applications.9489
  • Bootcamp‑driven deployment model: AIP “bootcamps” allow teams to prototype production‑grade use cases in days to weeks, then scale to enterprise rollouts, directly feeding Palantir’s large‑deal pipeline.959184
  • Cloud‑integrated but cloud‑agnostic: Integrations with hyperscalers (e.g., Google Cloud with BigQuery, others) so customers can keep their data stack while AIP orchestrates AI workflows on top.9194

Overall, AIP is positioned as a governed AI orchestration layer tightly bound to operational systems rather than a generic model hosting or RAG toolkit.89949291

AIP commercial case studies (illustrative)​

Examples from public write‑ups (not exhaustive):9291

  • Heineken (CPG / supply chain):
    • Used AIP agents to optimize delivery and shipping in its supply chain.
    • Built and deployed capabilities in three months that previously took three years, indicating AIP’s acceleration of development and deployment cycles.9192
  • AIG (insurance / financial services):
    • Adopting AIP for AI‑powered underwriting and risk assessment, with an internal target to double its 5‑year revenue growth trajectory from 10% to 20%.91
  • Rio Tinto (mining / logistics):
    • Uses Foundry + AIP to orchestrate and optimize routes and maintenance for dozens of unmanned trains running 24/7, turning AI decisions into live operational actions.91

These illustrate the pattern Palantir emphasizes: AI agents embedded in mission‑critical operations with measurable top‑line or efficiency outcomes.939291

AIP vs Snowflake vs Databricks​

Focused on role in the stack rather than direct feature‑by‑feature parity:

  • Palantir (Foundry + AIP)
    • Strengths: data fusion, governance, workflow automation, AI agents and operational decisioning.94
    • Best when: you need to unify messy operational data and drive decisions/actions across teams and processes, not just analytics or experimentation.9694
  • Snowflake
    • Strengths: cloud‑native data warehousing, scalable SQL analytics, BI ecosystem.94
    • Best when: you need structured storage and fast querying; AI is typically layered on via Snowflake’s own features (Snowpark, Cortex) or external LLM/RAG tools.9694
    • Relationship to AIP: often complementary; Snowflake can store and query, while Foundry/AIP orchestrate workflows and agents over that data.94
  • Databricks
    • Strengths: “lakehouse” architecture, notebook‑centric workflow, strong support for ML/LLM experimentation (DBRX, MLflow, etc.).979694
    • Best when: you need flexible data science and model development at scale.9794
    • Relationship to AIP: Databricks is the builder’s playground, AIP is the operational deployment and governance layer for AI agents in production workflows.969794

Differentiation summary: Palantir markets AIP as the governed AI operations layer on top of an enterprise ontology, while Snowflake focuses on data warehousing and Databricks on model building; many organizations will use them together.979694

Normalized multi‑table JSON for your agents​

Below is a more normalized structure with three logical “tables”: metrics_by_quarter, guidance, and segments. Numbers are drawn from Q3/Q4 2025 releases and slide summaries; growth rates are those cited in the sources.989910088848586

{
  "metrics_by_quarter": [
    {
      "quarter": "2025-Q3",
      "total_revenue_musd": 1181,
      "total_revenue_yoy_pct": 63,
      "total_revenue_qoq_pct": null,
      "us_commercial_revenue_musd": 397,
      "us_commercial_revenue_yoy_pct": 121,
      "us_commercial_revenue_qoq_pct": null,
      "us_government_revenue_musd": 486,
      "us_government_revenue_yoy_pct": 52,
      "us_government_revenue_qoq_pct": null,
      "deals_ge_1m_count": 204,
      "deals_ge_5m_count": 91,
      "deals_ge_10m_count": 53,
      "us_commercial_tcv_musd": 1310,
      "us_commercial_tcv_yoy_pct": 342,
      "us_commercial_tcv_qoq_pct": null,
      "us_commercial_rdv_musd": 3630,
      "us_commercial_rdv_yoy_pct": 199,
      "us_commercial_rdv_qoq_pct": 30
    },
    {
      "quarter": "2025-Q4",
      "total_revenue_musd": 1407,
      "total_revenue_yoy_pct": 70,
      "total_revenue_qoq_pct": 19,
      "us_commercial_revenue_musd": 507,
      "us_commercial_revenue_yoy_pct": 137,
      "us_commercial_revenue_qoq_pct": 28,
      "us_government_revenue_musd": 570,
      "us_government_revenue_yoy_pct": 66,
      "us_government_revenue_qoq_pct": 17,
      "deals_ge_1m_count": 180,
      "deals_ge_5m_count": 84,
      "deals_ge_10m_count": 61,
      "us_commercial_tcv_musd": 1344,
      "us_commercial_tcv_yoy_pct": 67,
      "us_commercial_tcv_qoq_pct": 3,
      "us_commercial_rdv_musd": 4380,
      "us_commercial_rdv_yoy_pct": 145,
      "us_commercial_rdv_qoq_pct": 21
    }
  ],
  "guidance": {
    "fy_2026": {
      "revenue_low_musd": 7182,
      "revenue_high_musd": 7198,
      "revenue_yoy_pct": 61,
      "us_commercial_revenue_min_musd": 3144,
      "us_commercial_revenue_min_yoy_pct": 115,
      "adjusted_operating_income_low_musd": 4126,
      "adjusted_operating_income_high_musd": 4142,
      "adjusted_fcf_low_musd": 3925,
      "adjusted_fcf_high_musd": 4125,
      "gaap_profitable_all_quarters": true
    },
    "q1_2026": {
      "revenue_low_musd": 1532,
      "revenue_high_musd": 1536,
      "adjusted_operating_income_low_musd": 870,
      "adjusted_operating_income_high_musd": 874
    }
  },
  "segments": {
    "us_commercial": {
      "q4_2025_revenue_musd": 507,
      "q4_2025_yoy_pct": 137,
      "q4_2025_qoq_pct": 28,
      "q4_2025_customer_count": 571,
      "q4_2025_customer_count_yoy_pct": 49
    },
    "us_government": {
      "q4_2025_revenue_musd": 570,
      "q4_2025_yoy_pct": 66,
      "q4_2025_qoq_pct": 17
    }
  }
}
  • Q3 metrics: Q3 2025 earnings release and slide deck.9910010198
  • Q4 metrics and FY 2026 guidance: Q4 2025 release, investor presentation, and coverage.8788848586

If you want to push this further, a natural next step would be to add a kpis table (Rule of 40, GAAP vs non‑GAAP margins, cash from operations) keyed by quarter for multi‑factor screening of “AI infra” names. 102103

⁂

add a kpis table (Rule of 40, GAAP vs non‑GAAP margins, cash from operations) keyed by quarter for multi‑factor screening of “AI infra” names. Palantir FY 2026 revenue guidance details from earnings call

Breakdown of Palantir Q4 2025 deal sizes over $1M $5M $10M Palantir AIP integrations with Google Cloud BigQuery Heineken Palantir AIP supply chain optimization details Palantir US commercial remaining deal value RDV growth drivers

Palantir’s FY 2026 guide, Q4 2025 deal stats, and AIP‑driven commercial pipeline all point to an aggressively scaled, high‑margin “AI infra” profile, with Rule of 40 >120% and very high incremental margins.104105106107108

FY 2026 guidance from Q4 2025 call​

From the Q4 2025 release and earnings commentary:105106107

  • FY 2026 total revenue: 7.182–7.198B (≈61% Y/Y vs 4.48B in 2025).106107105
  • FY 2026 U.S. commercial revenue: at least 3.144B (≥115% Y/Y).105106
  • FY 2026 adjusted operating income: 4.126–4.142B (≈57% margin).107106105
  • FY 2026 adjusted free cash flow: 3.925–4.125B.106107105
  • Management highlights a guided Rule of 40 ≈118% for FY 2026 (61% growth + ~57% adj. op margin).107

Q4 2025 deal sizes and RDV​

Deal buckets and value from Q4 2025:109104106

  • Q4 2025 deal counts:
  • Remaining deal value (RDV), U.S. commercial:
  • Growth drivers for U.S. commercial RDV:
    • Multiple large AIP‑led multi‑year contracts (high 8‑figure to 9‑figure) across financial services, healthcare, industrials, and critical infrastructure.111110
    • Strong conversion from AIP bootcamps to production deployments and expansions at existing customers.112111107

AIP + Google Cloud / BigQuery integrations​

Palantir exposes BigQuery connectivity through Foundry connectors, which AIP then operates over:113114115

  • Foundry–BigQuery connector:
    • Supports reading and syncing data between BigQuery tables and Foundry datasets.114115113
    • Uses Google APIs (bigquery.googleapis.com, bigquerystorage.googleapis.com, storage.googleapis.com, www.googleapis.com) with optional VPC Service Controls when both Foundry and BigQuery run on GCP.115113114
    • Requires project‑level configuration, service account credentials, and optional GCS staging bucket.113114
  • AIP on top of this:
    • AIP reasons over Foundry’s ontology; when data is landed or federated via BigQuery connectors, AIP agents can act on data that originated in BigQuery while preserving Foundry’s governance.116117113

Third‑party tools (e.g., Portable) also offer Foundry→BigQuery sync, but that’s more for analytics than operations.118

Heineken + AIP supply chain details​

Heineken USA is Palantir’s canonical AIP supply‑chain case:119120117

  • Scope and scale:
    • AIP agents manage 66,000 loads, 33,000 containers, 450 distributors, and 9 demand points across Heineken USA’s network.120119
    • Focus is on optimizing delivery, movement, and shipping in near real time.119120
  • Time‑to‑value:
    • COO Laurens van de Rotte: “In three months, the team built what took us three years before,” referring to AI‑powered capabilities co‑built with Palantir.120119
  • AIP role:
    • Foundry builds a digital twin of the supply chain (demand, logistics, production), then AIP agents trigger alerts, recommend actions (reroute, reallocate inventory, adjust plans), and automate parts of workflows.121119120
    • Benefits cited include enhanced end‑to‑end visibility, proactive risk mitigation, and improved efficiency and service levels.121

This is representative of the “AI agent in core operations” narrative Palantir uses to explain AIP‑driven commercial growth.117121119

KPIs table for Q3/Q4 2025 (Rule of 40, margins, cash)​

Key profitability and quality metrics:122108123124125104107106

  • Q4 2025:
    • Revenue growth: 70% Y/Y.104106
    • GAAP operating margin: 41%.108104109106
    • Adjusted operating margin: 57%.104107109106
    • GAAP net income margin: ~43% (609M GAAP NI on 1.41B revenue).107104
    • Rule of 40: 127% (70% growth + 57% adj. op margin).122108104107
    • Cash from operations (FY 2025): 2.134B, 48% margin; Q4 CFO ≈ 777M (55% margin).105104106107
  • Q3 2025 (for context):
    • Revenue growth: 63% Y/Y.123124125
    • Adjusted operating margin: 51%.125123
    • Rule of 40: 114% (63% + 51%).123125

Extended JSON with kpis table​

Here is the prior normalized JSON extended with a KPI table:

{
  "metrics_by_quarter": [
    {
      "quarter": "2025-Q3",
      "total_revenue_musd": 1181,
      "total_revenue_yoy_pct": 63,
      "total_revenue_qoq_pct": null,
      "us_commercial_revenue_musd": 397,
      "us_commercial_revenue_yoy_pct": 121,
      "us_commercial_revenue_qoq_pct": null,
      "us_government_revenue_musd": 486,
      "us_government_revenue_yoy_pct": 52,
      "us_government_revenue_qoq_pct": null,
      "deals_ge_1m_count": 204,
      "deals_ge_5m_count": 91,
      "deals_ge_10m_count": 53,
      "us_commercial_tcv_musd": 1310,
      "us_commercial_tcv_yoy_pct": 342,
      "us_commercial_tcv_qoq_pct": null,
      "us_commercial_rdv_musd": 3630,
      "us_commercial_rdv_yoy_pct": 199,
      "us_commercial_rdv_qoq_pct": 30
    },
    {
      "quarter": "2025-Q4",
      "total_revenue_musd": 1407,
      "total_revenue_yoy_pct": 70,
      "total_revenue_qoq_pct": 19,
      "us_commercial_revenue_musd": 507,
      "us_commercial_revenue_yoy_pct": 137,
      "us_commercial_revenue_qoq_pct": 28,
      "us_government_revenue_musd": 570,
      "us_government_revenue_yoy_pct": 66,
      "us_government_revenue_qoq_pct": 17,
      "deals_ge_1m_count": 180,
      "deals_ge_5m_count": 84,
      "deals_ge_10m_count": 61,
      "us_commercial_tcv_musd": 1344,
      "us_commercial_tcv_yoy_pct": 67,
      "us_commercial_tcv_qoq_pct": 3,
      "us_commercial_rdv_musd": 4380,
      "us_commercial_rdv_yoy_pct": 145,
      "us_commercial_rdv_qoq_pct": 21
    }
  ],
  "guidance": {
    "fy_2026": {
      "revenue_low_musd": 7182,
      "revenue_high_musd": 7198,
      "revenue_yoy_pct": 61,
      "us_commercial_revenue_min_musd": 3144,
      "us_commercial_revenue_min_yoy_pct": 115,
      "adjusted_operating_income_low_musd": 4126,
      "adjusted_operating_income_high_musd": 4142,
      "adjusted_fcf_low_musd": 3925,
      "adjusted_fcf_high_musd": 4125,
      "gaap_profitable_all_quarters": true,
      "rule_of_40_guided_pct": 118
    },
    "q1_2026": {
      "revenue_low_musd": 1532,
      "revenue_high_musd": 1536,
      "adjusted_operating_income_low_musd": 870,
      "adjusted_operating_income_high_musd": 874
    }
  },
  "segments": {
    "us_commercial": {
      "q4_2025_revenue_musd": 507,
      "q4_2025_yoy_pct": 137,
      "q4_2025_qoq_pct": 28,
      "q4_2025_customer_count": 571,
      "q4_2025_customer_count_yoy_pct": 49
    },
    "us_government": {
      "q4_2025_revenue_musd": 570,
      "q4_2025_yoy_pct": 66,
      "q4_2025_qoq_pct": 17
    }
  },
  "kpis": [
    {
      "quarter": "2025-Q3",
      "revenue_growth_yoy_pct": 63,
      "gaap_operating_margin_pct": 33,
      "adjusted_operating_margin_pct": 51,
      "gaap_net_income_margin_pct": 25,
      "rule_of_40_pct": 114,
      "cash_from_operations_musd": 508,
      "cash_from_operations_margin_pct": 43
    },
    {
      "quarter": "2025-Q4",
      "revenue_growth_yoy_pct": 70,
      "gaap_operating_margin_pct": 41,
      "adjusted_operating_margin_pct": 57,
      "gaap_net_income_margin_pct": 43,
      "rule_of_40_pct": 127,
      "cash_from_operations_musd": 777,
      "cash_from_operations_margin_pct": 55
    }
  ]
}

You can now apply the same schema across other “AI infra” names (Snowflake, Databricks if public later, Nvidia, etc.) to screen on growth + profitability + deal structure.

⁂

Rule of 40 scores and margins for Snowflake in recent quarters

GAAP vs non-GAAP metrics table for Databricks Q4 2025 Cash from operations and Rule of 40 for CoreWeave FY2026 guidance Multi-factor screening criteria for AI infra stocks beyond Rule of 40 Compare Palantir Q4 2025 KPIs to NVIDIA and AMD metrics

Snowflake, Databricks, Nvidia, AMD, and Palantir all screen well on “AI infra” metrics, but they do it in very different ways: Palantir and Databricks show high Rule‑of‑40 style profiles with strong FCF, while Nvidia/AMD ride hardware leverage and Snowflake is still GAAP‑loss‑making despite solid FCF margins.126127128129130131132133

Snowflake Rule of 40 and margins (recent)​

Using FY 2025 (year ended Jan 31, 2025) plus recent commentary:

  • FY 2025 product revenue growth: ~30% Y/Y.128
  • FY 2025 GAAP operating margin: about ‑40%.128
  • FY 2025 operating cash flow margin: 26% (959.8M CFO on 3.7B total revenue).128
  • FY 2025 FCF margin: 24%.128
  • LTM Rule of 40 (Finbox): ≈46%, down from historical ~80–90% when growth was higher.134135

Quarterly example (Q1 FY 2025):

  • Revenue growth Y/Y: ~34–35%.127
  • GAAP operating margin: ‑42%, non‑GAAP operating margin 4%.127
  • Operating cash flow margin: 43%, FCF margin 40%.127

So Snowflake has respectable cash/FCF‑based Rule‑of‑40, but GAAP profitability is still strongly negative due to SBC and investments.135127128

Databricks financial KPIs (no public GAAP Q4 2025)​

Databricks is private; there is no public GAAP/non‑GAAP Q4 2025 10‑Q style table. Instead we have run‑rate and unit‑economics style disclosures:

  • Series L (Dec 2025) update:
    • Revenue run‑rate: >4.8B, growing >55% Y/Y.130
    • Positive free cash flow over the last 12 months.130
    • Net retention: >140%.130

    • 700 customers at >1M ARR.130

Earlier 2025 disclosure:

  • Revenue run‑rate: ≈4B, growing >50% Y/Y, >650 customers at >1M ARR, positive FCF.129136

This yields an implicit “Rule of 40” style score of >105% (55% growth + ~50%+ FCF‑ish margin implied by “positive FCF at that scale”), but without GAAP detail you cannot build a precise GAAP vs non‑GAAP table.136129130

CoreWeave FY 2026 guidance (cash & Rule of 40)​

CoreWeave is also private and does not publish full 10‑K style guidance. Public info is sparse; most discussion is about capacity, contracts, and funding, not official FY 2026 revenue/FCF guidance. I do not see a credible, numeric FY 2026 guidance with revenue, margins, and CFO that would let us compute a Rule of 40 or cash from operations with the same rigor as Palantir/Snowflake.126128130

Given that, any Rule‑of‑40 style score for CoreWeave would be speculative; for a serious screen, I’d omit CoreWeave until they publish more detailed guidance.

Multi‑factor screening for “AI infra” beyond Rule of 40​

For your agentic screening, a more robust factor set (per name, per quarter/year):

  1. Growth & profitability
    • Revenue growth Y/Y (total + AI‑specific segment).
    • GAAP operating margin, GAAP net margin.
    • Non‑GAAP operating/FCF margins.
  2. Cash and capital intensity
    • Operating cash flow margin.
    • FCF margin and capex as % of revenue.
    • Net cash / debt position.
  3. AI leverage / quality of revenue
    • % revenue from AI‑related segments (e.g., Nvidia data center, Palantir AIP‑linked commercial, Databricks AI products).137132130
    • Net expansion/retention (where disclosed: Databricks >140%, Snowflake >130% historically).138136130
  4. Customer and deal structure
    • Customers >1M ARR (Snowflake, Databricks, Palantir’s large deals).133136128130
    • Remaining performance obligations / RDV growth (Palantir, Snowflake).133128
  5. Risk & durability
    • Customer concentration and exposure to a few hyperscalers.
    • Stock‑based comp as % of revenue.
    • Cyclicality (semis vs software).

You can then create an aggregate “AI infra quality score” built from normalized z‑scores or percentile ranks across these factors.

Palantir Q4 2025 KPIs vs Nvidia & AMD​

Palantir Q4 2025 (software / AI ops)

  • Revenue: 1.407B, +70% Y/Y.133
  • GAAP operating margin: 41%; adjusted operating margin: 57%.139140133
  • GAAP net margin: ≈43%.141
  • Rule of 40 (growth + adj. op margin): 127%.140141
  • Cash from operations: ≈777M (55% margin); FY 2025 CFO 2.134B (48% margin).132141133

Nvidia FY 2025 / Q4 FY 2025 (AI hardware)

  • FY 2025 revenue: 130.5B, 114% Y/Y.137
  • Gross margin: 75%.137
  • Operating income: 81.5B, +147% Y/Y (≈62% operating margin).137
  • Net income: 72.9B, +145% Y/Y (≈56% net margin).137

Nvidia doesn’t publish a simple “Rule of 40,” but combining 114% growth with ~62% operating margin gives a notional 176%, far above Palantir’s 127%, driven by once‑in‑a‑generation AI hardware demand.142137

AMD FY 2025 (CPU/GPU, including AI)

  • FY 2025 revenue: 34.6B, record.131
  • Gross margin: 50%.131
  • Operating income: 3.7B (~11% operating margin).131
  • Net income: 4.3B (~12% net margin).131

Assuming ~20–25% Y/Y revenue growth (varies by segment; MI‑300 AI uplift vs PC softness), AMD’s implied Rule of 40 is in the 30–35% range, well below Palantir and Nvidia but respectable for a cyclical semiconductor firm.142131

Quick comparison table​

CompanyPeriodRev growth Y/YGAAP op marginNet marginRule‑of‑40 style
PalantirQ4 202570% 13341% 13343% 141127% (70+57 adj) 140
NvidiaFY 2025114% 137~62% 137~56% 137~176% (114+62)
AMDFY 2025~20–25% 142131~11% 131~12% 131~30–35%
SnowflakeFY 202530% 128‑40% 128n/a~46% FCF‑based 134128
DatabricksLTM (Dec 2025)>55% 130n/a (private)n/a>105% implied (growth + positive FCF) 130

Palantir sits in a rare bucket: software‑like, strongly GAAP‑profitable, high‑growth, with Rule‑of‑40 numbers closer to hyperscale AI hardware than typical SaaS.140133137

If you want a next step for tooling, a concise prompt you can reuse for other names:

“For ticker X, pull the last two reported quarters plus latest full year. For each, compute: revenue growth Y/Y, GAAP and non‑GAAP operating margins, net margin, operating cash flow margin, FCF margin, and Rule of 40 using both adj. operating margin and FCF margin. Return normalized JSON tables: metrics_by_period, kpis, and segments, compatible with the Palantir schema we built.” 143144145

⁂

Rule of 40 explain the formula and how it is applied

The Rule of 40 is a simple heuristic for judging whether a growth‑stage software (especially SaaS) business has a good balance between growth and profitability.146147148149150

Basic formula​

Most commonly:

$$ \text{Rule of 40 score} = \text{Revenue growth rate (%)} + \text{Profit margin (%)} $$

Where:147148149150146

  • Revenue growth rate = year‑over‑year revenue growth (e.g., last 12 months vs prior 12 months).
  • Profit margin = usually EBITDA margin, operating margin, or sometimes free cash flow (FCF) margin for the same period.148151152146

A company is said to “meet” the Rule of 40 if this sum is ≥ 40%.149150146147148

How to calculate (step by step)​

  1. Compute revenue growth Y/Y:

$$ \text{Growth} = \frac{\text{Revenue}{\text{current}} - \text{Revenue}{\text{prior}}}{\text{Revenue}_{\text{prior}}} \times 100% $$

Example: revenue goes from 100 to 130 → growth = 30%.152148 2. Choose a profit metric for the same period (EBITDA margin, operating margin, or FCF margin):

$$ \text{Margin} = \frac{\text{EBITDA (or FCF, etc.)}}{\text{Revenue}} \times 100% $$

Example: EBITDA 10 on revenue 100 → margin = 10%.146148152 3. Add them: - Growth 30% + margin 10% → Rule of 40 = 40% (passes). - Growth 20% + margin 15% → Rule of 40 = 35% (below target).150147152

Interpretation​

  • ≥ 40%: Generally viewed as an efficient, high‑quality SaaS/infra business given its stage.147148149152146
  • < 40%: Suggests the company should either grow faster or be more profitable to be competitive with peers.148150152147

It explicitly allows trade‑offs: a company can run negative margins if growth is very high, or modest growth with high margins, as long as the sum is ≥40. Example:149152146

  • 60% growth, ‑20% margin → 40% (fast‑growing, unprofitable).
  • 10% growth, 35% margin → 45% (slow growth, very profitable).

Variants in practice​

Different investors/operators modify the formula:151153152146148

  • Metric choice: use
    • EBITDA margin (classic), or
    • Operating margin, or
    • Free cash flow margin (common for mature SaaS).
  • Weighted Rule of 40: give more weight to growth than margin, e.g.:

$$ \text{Weighted score} = 1.33 \times \text{Growth} + 0.67 \times \text{Margin} $$

This emphasizes growth for earlier‑stage names.153151152

  • “Rule of X”: some investors raise the bar (e.g., Rule of 50) for top‑tier assets in hot markets.153152

How it’s applied in screening​

For public “AI infra” or SaaS stocks, the typical workflow is:151152146148

  • Compute trailing 12‑month (or last fiscal year) revenue growth.
  • Pick a margin (often FCF margin or non‑GAAP op margin).
  • Sum them to get Rule of 40; compare across a peer set (e.g., Palantir, Snowflake, Databricks, etc.).
  • Use it as one factor among others (net retention, SBC %, balance sheet, AI exposure) to rank names.

In your context, you can store both GAAP‑based and FCF‑based Rule‑of‑40 scores per quarter/year in the JSON schema and let agents reason about quality vs capital intensity per name. 154155

⁂

Follow-ups

What is the weighted Rule of 40 formula and when to use it How do Palantir and Snowflake compare on Rule of 40 scores What Rule of 40 benchmarks for mature vs early stage SaaS Can Rule of 40 be negative and still meet the threshold Alternatives to Rule of 40 for SaaS performance metrics

Weighted Rule of 40 adjusts the classic Rule of 40 to emphasize growth more than profitability, which is often useful for earlier‑stage or high‑opportunity SaaS/AI infra names.156157158159

Weighted Rule of 40: formula and when to use​

A common weighted version:157158159

$$ \text{Weighted Rule of 40} = w_g \times \text{Growth} + w_m \times \text{Margin} $$

with $w_g > w_m$ and $w_g + w_m = 2$ if you want to keep a “Rule of 40‑like” scale. One example used in practice:158157

  • $w_g = 1.33$
  • $w_m = 0.67$

So:

$$ \text{Weighted score} = 1.33 \times \text{Revenue growth (%)} + 0.67 \times \text{Margin (%)} $$

When to use:

  • Early‑stage or high‑growth infra where growth potential is more important than near‑term margins.159157158
  • Markets with very high dispersion in growth (e.g., AI infra now) where a flat 1:1 trade‑off under‑prices truly exceptional growth.

Palantir vs Snowflake on Rule of 40​

Using latest disclosed periods (approximate, for directional comparison):

Palantir – Q4 2025

  • Revenue growth Y/Y: 70%.160
  • Adjusted operating margin: 57%.161160
  • Rule of 40 (growth + adj. op margin): 127%.162161

Snowflake – FY 2025

  • Product revenue growth: 30% Y/Y.163
  • Non‑GAAP operating margin: 6–8% (company has guided low‑mid single digits).164163
  • FCF margin: 24%.163

Resulting Rule‑of‑40 style scores:

  • Using non‑GAAP op margin: ~36–38% (30 + 6–8) → just below the 40% “bar.”
  • Using FCF margin: 54% (30 + 24) → above 40%, good but far below Palantir’s 127%.165166163

Interpretation:

  • Palantir: hyper‑“Rule of 40” with both very high growth and very high margins.
  • Snowflake: strong on cash/FCF‑based Rule of 40, but weaker on GAAP/non‑GAAP op margin; still solid but not in Palantir’s tier.160161165163

Benchmarks for mature vs early‑stage SaaS​

Guidelines from SaaS/VC literature:167168156157158159

  • Early stage (very high growth)
    • Growth: 40–80%+ Y/Y.
    • Acceptable margins: often negative (‑20% to ‑40%) so long as the Rule of 40 ≥ 40%.
    • Weighted Rule of 40 often used to emphasize growth.
  • Mid stage
    • Growth: 20–40% Y/Y.
    • Margin: 0–20% (EBITDA/op or FCF).
    • Target Rule of 40: ≥ 40%, with some growth‑equity shops wanting 50–60% for top‑tier names.156157159
  • Mature SaaS / infra
    • Growth: 10–20% Y/Y.
    • Margin: 20–30%+.
    • Rule of 40: often > 40% and sometimes targeted to 50–60% as a “compounder” bar.169157159

In practice, investors ratchet expectations up (e.g., “Rule of 50 or 60”) for best‑in‑class, especially in hot segments like AI infra.158159

Can Rule of 40 be negative and still “meet” the threshold?​

No. By definition, Rule of 40 is the sum of growth and margin; to “meet” the Rule of 40 you want:170159167156

$$ \text{Growth} + \text{Margin} \ge 40% $$

If that sum is negative (e.g., ‑10%), the company is both shrinking or barely growing and losing money; it cannot meet the 40% threshold.159167170

What can happen:

  • One component negative, total still ≥40%:
    • Example: 80% growth, ‑30% margin → 50% Rule of 40 (passes).
  • Both components positive but total <40%:
    • Example: 15% growth, 10% margin → 25% Rule of 40 (fails).

A negative Rule‑of‑40 score is a strong red flag in growth investing terms.157167159

Alternatives / complements to Rule of 40​

Commonly used additional metrics in SaaS/AI infra analysis:169156157158159

  1. Net dollar retention (NDR) / net revenue retention (NRR)
    • Measures expansion vs churn within the cohort.
    • NRR ≥120–130% is top‑tier for infra; complements Rule of 40 by focusing on customer‑level durability.
  2. Magic Number / Sales Efficiency
    • Revenue growth relative to sales & marketing spend (e.g., ARR growth / last‑quarter S&M).
    • Indicates how efficiently growth is being bought.
  3. LTV/CAC and payback period
    • Customer lifetime value vs acquisition cost; CAC payback in months.
    • Short payback + high NRR can justify lower margins today.
  4. Gross margin & unit economics
    • Important for infra where infra/compute costs can erode margins (e.g., heavy GPU spend, data egress costs).
  5. FCF yield and SBC as % of revenue
    • For public names: FCF yield vs valuation, and stock‑based comp intensity.
  6. Segment‑specific AI metrics
    • % of revenue from AI‑related products (e.g., data‑center for Nvidia, AIP for Palantir).
    • Growth and margins of AI segments vs legacy lines.

For your agents, Rule of 40 becomes one feature in a multi‑factor model, alongside NRR, FCF margin, SBC%, and AI‑segment growth, rather than a single decision rule.

⁂

CREATE a GLOSSARY of every acronym

Here’s a focused glossary of the acronyms that have appeared in this thread and the referenced materials.

Financial & Market Metrics​

  • Y/Y – Year‑over‑Year (comparison to the same period in the prior year).171
  • Q/Q – Quarter‑over‑Quarter (comparison to the immediately preceding quarter).171
  • FY – Fiscal Year.172
  • Q1, Q2, Q3, Q4 – First, Second, Third, Fourth fiscal quarter.172
  • TCV – Total Contract Value (sum of the expected value of a contract over its full term).173
  • RDV – Remaining Deal Value (remaining contracted value not yet recognized as revenue; Palantir term, similar in spirit to RPO).174173
  • RPO – Remaining Performance Obligations (GAAP term for contracted but not yet recognized revenue).172
  • ARR – Annual Recurring Revenue.172
  • NRR / NDR – Net Revenue Retention / Net Dollar Retention (expansion + churn within existing customers).175172
  • GAAP – Generally Accepted Accounting Principles (standard U.S. accounting rules).172
  • Non‑GAAP – Adjusted financial metric excluding items like SBC, amortization, etc.172
  • EBITDA – Earnings Before Interest, Taxes, Depreciation, and Amortization.172
  • FCF – Free Cash Flow (operating cash flow minus capital expenditures).176172
  • CFO (metric) – Cash From Operations.172
  • Op Margin – Operating Margin (operating income ÷ revenue).172
  • Rule of 40 (R40) – Revenue growth rate (%) + profit margin (%) heuristic for SaaS quality.177171
  • SBC – Stock‑Based Compensation.172
  • LTV – Lifetime Value (expected net profit from a customer over its lifetime).175
  • CAC – Customer Acquisition Cost.175

Palantir‑Specific​

  • AIP – Artificial Intelligence Platform (Palantir’s AI orchestration and agents layer on top of Foundry/Gotham).178179173
  • Gotham – Palantir’s platform primarily for defense, intelligence, and law‑enforcement use cases.180181
  • Foundry – Palantir’s data integration and operations platform for enterprises (the “operating system” for data).181182180
  • Apollo – Palantir’s continuous delivery / deployment and orchestration platform across cloud, on‑prem, and edge.182180
  • AIPCon – Palantir’s conference focused on AIP and AI customers.183184

Technology / Cloud / Data​

  • LLM – Large Language Model (e.g., GPT‑class models).176172
  • AI – Artificial Intelligence.177172
  • GPU – Graphics Processing Unit (key hardware for AI workloads).185186
  • VPC – Virtual Private Cloud.187188
  • API – Application Programming Interface.188187
  • GCP – Google Cloud Platform.187188
  • GCS – Google Cloud Storage.188187
  • SQL – Structured Query Language (for relational data).189

Companies and Tickers​

  • PLTR – Palantir Technologies Inc. (ticker).173
  • SNOW – Snowflake Inc. (ticker).190191
  • NVDA – Nvidia Corporation (ticker).185
  • AMD – Advanced Micro Devices, Inc. (ticker).192

Documents & Regulatory​

  • 10‑K – Annual report filed with the U.S. SEC (comprehensive financials and risk factors).193172
  • 10‑Q – Quarterly report filed with the U.S. SEC.193172
  • 8‑K – Current report filed with the SEC for material events (e.g., earnings releases).172
  • SEC – U.S. Securities and Exchange Commission.193

If you want, I can next produce a machine‑ready JSON glossary (key → definition, plus optional categories) you can plug into agents for acronym expansion.

⁂

FDE?

In this context, FDE almost certainly means Forward Deployed Engineer.

  • Forward Deployed Engineer (FDE) – A software engineer embedded directly with customers (often on‑site or very close to their operations) to: design and build solutions on top of a complex platform, integrate with customer systems, and iterate quickly based on real‑world feedback.194195196197198

This term is heavily associated with Palantir and similar AI/SaaS infra companies; Palantir effectively popularized the FDE model for deploying Gotham/Foundry/AIP into enterprises.199196194 200201202203204205206207208

⁂

How do Palantir Forward Deployed Engineers work with AIP

What skills are required for Palantir FDE roles How has the FDE model impacted Palantir commercial growth Differences between Palantir FDEs and traditional engineers Examples of FDE projects at Palantir clients

Palantir’s Forward Deployed Engineers (FDEs) are essentially the human interface between AIP/Foundry and the customer’s actual operations; they co‑design, build, and productionize AIP workflows on‑site, and that’s a big part of why AIP is monetizing so fast.209210211212213214

How FDEs work with AIP​

  • Embedded with customers: FDEs (FDSEs / Forward Deployed AI Engineers) sit directly with a single customer or a small set, owning AI strategy and implementation on Palantir platforms (Foundry + AIP, Gotham where relevant).210211213214
  • AIP workflows and agents: They build end‑to‑end LLM/AI workflows, wire them into the Foundry ontology and applications, and take them to production (not just PoCs), including AIP “agents” that read, reason, and act on operational data.211213209210
  • Feedback loop into the product: FDEs feed field learnings back into the AIP product suite so platform capabilities reflect real customer use cases, which Palantir credits as a key engine for rapid AIP evolution.212214210211

Palantir’s own docs even have “AI FDE” features in Foundry, tying FDE workflows to AIP‑enabled ontologies and branching.209

Skills required for Palantir FDE roles​

Job descriptions and profile write‑ups emphasize a hybrid of deep engineering plus product/consulting skills:215213214210211

  • Technical
    • Strong software engineering (Java/Python/TypeScript, data structures, distributed systems).213214215
    • Data integration + SQL/ETL/ELT on large, messy datasets; experience with Spark/Hadoop or similar.214215213
    • AI/ML/LLM workflows for Forward Deployed AI Engineer roles (LLM orchestration, prompt design, retrieval, evaluation, MLOps fundamentals).210211215
  • Product / stakeholder
    • Ability to own “Gen AI strategy and implementation” with customers; think like a hands‑on startup CTO.211210
    • Requirements discovery, talking to execs and front‑line users, translating business goals into technical workflows.216215213214
  • Delivery mindset
    • Comfortable owning high‑stakes projects end‑to‑end, from problem definition through deployment and iteration.215214210211
    • Bias toward “how can I get this to work” vs “why this won’t work,” with high tolerance for ambiguity.217218219216

Impact of the FDE model on Palantir’s commercial growth​

Analyses and industry commentary consistently tie Palantir’s services‑led growth and AIP adoption to the FDE model:220218221216217212215

  • Services‑led growth engine: FDEs drive “services‑led growth” by embedding, solving a hard operational problem using the platform, and then expanding usage; those deployments generate large AIP/Foundry contracts and follow‑on expansions.218212215
  • Faster integration and stickiness: By owning integration and custom solution building, FDEs reduce time‑to‑value vs customers trying to self‑integrate, and produce deep embedding of Palantir in the customer’s workflows, which supports very high RDV/TCV and net expansion.221216217218
  • Platform learning loop: Each FDE project helps identify reusable capabilities; Palantir generalizes them into the platform, so the product surface area grows in directions proven by real customers.212221

Everest Group and others explicitly frame Palantir as “inside the category of one” because it ships platforms and embeds its own engineers and strategists (FDEs) to ensure those platforms drive outcomes, which they credit as central to commercial success.220221212

FDEs vs traditional engineers​

Palantir’s own language makes the contrast explicit:219216213214

  • Scope
    • Traditional “Dev”: builds a single capability to serve many customers (core product features).213219
    • FDE/FDSE: enables many capabilities for a single customer, heavily customized, deployment‑focused work.219213
  • Location & context
    • Dev: sits inside the product/org team, works from HQ.
    • FDE: embedded with the customer (often on‑site or closely coupled), deep exposure to domain context and politics.216217213
  • Day‑to‑day
    • Dev: sprints on product roadmaps, PRs, internal APIs.
    • FDE: mixes coding, data wrangling, configuration, stakeholder meetings, workshops, demos, and production deployments.214216215213
  • Success metric
    • Dev: product KPIs (feature usage, latency, NPS, etc.).
    • FDE: customer outcome metrics (reduced defects, fewer outages, faster logistics, higher throughput, etc.).217216215213

So FDEs are closer to a hybrid of senior engineer, solutions architect, product manager, and consultant, but with deep ownership of shipping code on Palantir’s stack.216217215

Examples of FDE‑style projects at Palantir clients​

Most concrete examples in the wild are described at a “what” level rather than naming specific FDEs, but they map to FDE work patterns:222223224225226220215213214

  • Supply chain optimization (Heineken USA)
    • Building a digital twin of the supply chain in Foundry, with AIP agents managing tens of thousands of loads/containers and hundreds of distributors.
    • FDE‑type work: modeling entities/flows, integrating ERP/logistics data, building operational UIs, and wiring AIP agents into planners’ workflows.224225226215
  • Industrial / logistics (e.g., mining & rail)
    • Projects like orchestrating unmanned trains and optimizing maintenance and routing using Foundry + AIP.
    • FDE‑style tasks: ingestion of sensor/telemetry data, building optimization apps, and surfacing AIP recommendations into control rooms.223215
  • Government / defense / intel
    • Cyber, healthcare, defense and intelligence analysis, where FDSEs configure Gotham/Foundry to support investigations, mission planning, and operations.220217213214
  • Data migration and AI enablement
    • “How Palantir AIP Accelerates Data Migration” describes FDEs using AIP to assist in mapping, transforming, and validating complex enterprise data migrations while building new AI workflows on top.222209215

Palantir’s “Day in the life of a FDSE” blog walks through work across cyber, healthcare, and defense, all featuring: integrating messy datasets, configuring ontologies, building custom apps, and iterating with end users — archetypal FDE projects.215213

If you want to operationalize this in your own stack, a prompt you can re‑use for analysis:

“Given a SaaS/AI infra company’s public case studies and job descriptions for forward‑deployed or customer‑embedded engineers, extract: (1) core responsibilities; (2) required skills; (3) how these roles interface with the product (e.g., AIP/Foundry equivalents); and (4) how this deployment model contributes to revenue growth, retention, and product roadmap. Return a JSON schema compatible with my Palantir FDE model (fields: role_type, skills, activities, growth_impact, example_projects).” 227

⁂

Evolution of FDE role into Agent Deployment Engineers

Agent Deployment Engineers are best understood as the FDE role updated for an agentic stack: instead of deploying single applications, they deploy, configure, and govern networks of AI agents across customer workflows.228229230231232233

Conceptual evolution: FDE → Agent Deployment Engineer​

  • Classic FDE (Palantir model): Embedded engineer who configures and extends a platform (Foundry, AIP, Gotham, etc.) for one customer, integrating data and building bespoke apps.234235236237238
  • Agent Deployment Engineer: Same embedded pattern, but the deployment unit is a fleet of AI agents, not a monolithic app—agents invoke tools, act on systems, and collaborate with humans.229239232228

Beam’s “Agent Deployment Engineers” piece explicitly calls this “a natural evolution of the FDE concept,” driven by:233228229

  • Explosion of fragmented systems & data.
  • Rise of autonomous / semi‑autonomous AI agents as the main abstraction for automation.

What changes in the role with agents​

Key shifts highlighted in the agent‑deployment literature:240239241232228229

  • From app deployment → agent configuration & orchestration
    • FDE: configure and extend a platform; build UIs, pipelines, and some automation.
    • Agent Deployment Engineer: design agent graphs, toolchains, and routing logic; tune prompts, policies, and memory; deploy agents into production workflows.
  • From integration scripts → evaluation and guardrails
    • Heavy emphasis on evaluation pipelines, safety constraints, and monitoring to handle non‑deterministic behavior (hallucinations, unsafe actions, failure modes).230232228229
  • From single system → fleet management / AgentOps
    • Articles point to an emerging “AgentOps” discipline analogous to SRE for agents: monitoring, rollout strategies, rollback, versioning, and compliance for entire fleets of agents.231232228229230

Skill stack for Agent Deployment Engineers​

Compared to FDEs, agent‑deployment roles layer AI‑native skills on top of the FDE baseline:239241232228229240

  • FDE baseline
    • Full‑stack or backend engineering.
    • Data engineering / integration.
    • Strong customer‑facing and product instincts.242236243234
  • Added for agentic work
    • LLM/agent frameworks (LangChain, similar orchestration stacks).241228229239
    • Prompt design, policy & tool routing, retrieval‑augmented pipelines.228229240241
    • Automated evaluation (offline/online tests, red‑teaming, regression suites for agents).232229230228
    • Safety & compliance patterns: constraints, approval workflows, least‑privilege tool access.244245229228

Forward‑Deployed AI Engineer postings and Agent Deployment roles show almost identical structure: end‑to‑end ownership of AI automation deployment with close customer collaboration.246247240241

How this ties back to Palantir’s FDE model​

  • Palantir’s FDE is the reference template: “enabling many capabilities for a single customer” via embedded engineers; Beam and others explicitly cite Palantir FDEs as the model being extended to agents.245234229230231228
  • As AIP and similar platforms move from “AI copilots” to more autonomous agents, FDE‑type roles naturally evolve into Agent Deployment Engineers who:
    • Map business processes to agent graphs and tools.
    • Operationalize those agents with guardrails.
    • Maintain them over time under an AgentOps discipline.229230232228

If you want to push this into your own stack, a prompt you can reuse:

“Given Palantir‑style FDE responsibilities and modern ‘Agent Deployment Engineer’ job descriptions, design a role spec and competency matrix for AgentOps in my organization, including: skills, responsibilities, interfaces with product/infra teams, and KPIs (time‑to‑value, safe‑deployment rate, agent uptime). Return as structured JSON suitable for role‑definition and hiring agents.” 248249

⁂

“Given a SaaS/AI infra company’s public case studies and job descriptions for forward‑deployed or customer‑embedded engineers, extract: (1) core responsibilities; (2) required skills; (3) how these roles interface with the product (e.g., AIP/Foundry equivalents); and (4) how this deployment model contributes to revenue growth, retention, and product roadmap. Return a JSON schema compatible with my Palantir FDE model (fields: role_type, skills, activities, growth_impact, example_projects).”

Follow-ups What specific technical skills do Palantir FDEs need like programming languages How do FDEs contribute code back to AIP and Foundry Compare Palantir FDEs to traditional software consultants Examples of FDE projects reducing customer defects or costs Evolution of FDE role into Agent Deployment Engineers. Given Palantir‑style FDE responsibilities and modern ‘Agent Deployment Engineer’ job descriptions, design a role spec and competency matrix for AgentOps in my organization, including: skills, responsibilities, interfaces with product/infra teams, and KPIs (time‑to‑value, safe‑deployment rate, agent uptime). Return as structured JSON suitable for role‑definition and hiring agents.” Key skills required for Agent Deployment Engineers How Palantir trains Agent Deployment Engineers Differences between FDE and traditional DevOps roles Examples of AI agent deployments by FDEs Challenges in transitioning FDE to agentic workflows

Palantir FDEs are converging toward an Agent Deployment Engineer / AgentOps pattern: embedded, full‑stack engineers who design, deploy, and maintain AI/agent workflows on AIP/Foundry, while feeding learnings back into the core platform.250251252253254255256

1) JSON schema for FDE‑style deployment roles​

You can use this schema to ingest public case studies and job descriptions (Palantir FDE, Forward Deployed AI Engineer, Beam AI Agent Forward Deployment, etc.).254255256257250

{
  "roles": [
    {
      "role_type": "string",
      "skills": {
        "technical": ["string"],
        "domain": ["string"],
        "consultative": ["string"],
        "ai_agent_specific": ["string"]
      },
      "activities": {
        "discovery": ["string"],
        "solution_design": ["string"],
        "implementation": ["string"],
        "deployment": ["string"],
        "evaluation_and_monitoring": ["string"],
        "customer_enablement": ["string"]
      },
      "growth_impact": {
        "revenue": ["string"],
        "retention_and_expansion": ["string"],
        "product_feedback": ["string"]
      },
      "example_projects": [
        {
          "name": "string",
          "customer_sector": "string",
          "problem": "string",
          "solution": "string",
          "outcomes": ["string"],
          "stack": ["string"]
        }
      ]
    }
  ]
}

Populate role_type as "FDE", "Forward Deployed AI Engineer", "Agent Deployment Engineer", etc., depending on the source.255256258259254

2) Specific technical skills for Palantir FDEs​

Palantir FDSE / FDE postings explicitly call out languages and tools.260261262250

Core programming:

  • Python – data wrangling, app logic, scripting.261260250
  • Java – backend services and platform extensions.260250261
  • C++ – performance‑critical components (less common for day‑to‑day FDE but present in stack).261260
  • TypeScript / JavaScript – UIs and custom Foundry/AIP front‑end extensions.262250
  • SQL – querying and modeling large datasets in Foundry/underlying warehouses.260

Preferred background includes CS/Math/Physics/Data Science, strong algorithms, and ability to learn new languages as needed.250262

3) How FDEs contribute code back to AIP/Foundry​

While job posts don’t name specific repos, Palantir materials and FDE write‑ups describe a bidirectional flow:251253263264265250

  • Customer‑specific solutions: FDEs primarily build ontologies, pipelines, apps, and AIP workflows within customer environments (Foundry projects, code repos, low‑code pipelines).253265266
  • Platform feedback: Patterns that recur across customers are generalized into new product features (e.g., AIP integration into Pipeline Builder, AI FDE “operate Foundry via conversational commands,” AIP Assist features).263264266253
  • Contribution modes include: refining internal APIs, surfacing feature gaps, and collaborating with core product teams to harden and productize what started as field code.265253263250

Think: FDEs validate and stress‑test AIP/Foundry capabilities in the wild; core teams turn those into first‑class features.251253

4) Palantir FDEs vs traditional software consultants​

Comparative axes from Palantir/industry commentary:267268269270271272273

  • Ownership
    • Consultants: advise, prototype, sometimes build; platform evolution often separate.
    • FDEs: own solutions end‑to‑end, run them in production, and influence platform roadmap.268270271
  • Depth of product integration
    • Consultants: may integrate multiple products but are not usually embedded in vendor core engineering.
    • FDEs: deep in a single vendor stack (Foundry/AIP/Gotham), with direct links to product/infra teams.271272268
  • Day‑to‑day work
    • Consultants: heavy time in slideware, stakeholder workshops, and integration planning.
    • FDEs: substantial time in code/data pipelines + direct user time; fewer slide decks, more working systems.269270267265
  • Incentives
    • Consultants: billable hours, project count.
    • FDEs: successful deployments, expansions, and reusable product improvements.272268271

5) Examples of FDE‑style projects reducing defects or costs​

Public examples often omit “FDE” wording but clearly map to this role:274275276277278251

  • Heineken supply chain (AIP + Foundry)
    • Built a supply chain control layer with AIP agents managing tens of thousands of loads/containers and hundreds of distributors in a few months vs years.276277278
    • Outcomes: reduced delays and stock‑outs, more efficient logistics; LinkedIn posts highlight “three months to build what took three years before.”277278
  • Manufacturing & inventory automation (Foundry + AIP)
    • Case write‑ups describe a manufacturer using AIP to auto‑assign orders to shipments based on proximity and cost, processing hundreds of orders per minute that previously required a large manual team.251
    • Outcomes: fewer fulfillment errors, lower labor costs, better service levels.251
  • Retail supply chain control tower
    • A global retailer using Foundry reported ~50% reduction in out‑of‑stock levels via integrated demand/logistics modeling—typical FDE work.251

FDEs are the ones building those ontologies, pipelines, and agent workflows on‑site and iterating until these cost/defect reductions show up in the KPIs.270265251

6) Role spec + competency matrix for Agent Deployment Engineers (AgentOps)​

Here’s a structured JSON spec you can drop into your system design / hiring agents, derived from Beam’s Agent Deployment roles and FDE patterns.252257259279254255

{
  "role_type": "Agent Deployment Engineer",
  "skills": {
    "technical": [
      "Proficient in at least one general-purpose language (Python, Node.js/TypeScript, or similar) for agent tooling and integrations",
      "Experience building and consuming REST/GraphQL APIs",
      "Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) for deploying agents",
      "Data modeling and SQL for integrating with warehouses/lakes and operational stores",
      "Basic observability skills (logs, metrics, traces) for monitoring agent workflows"
    ],
    "ai_agent_specific": [
      "Hands-on experience with LLMs and agent frameworks (e.g., LangChain, internal orchestration stack)",
      "Prompt engineering (writing, testing, and iterating prompts and policies)",
      "Designing tool-using agents (tool schemas, routing, constrained execution)",
      "Implementing RAG-style pipelines (retrievers, vector stores, evaluation)",
      "Designing and maintaining evaluation pipelines for agents (offline test suites, red-teaming, regression checks)",
      "Understanding of AI safety patterns (guardrails, approval steps, least-privilege access)"
    ],
    "domain_and_consultative": [
      "Ability to map real business workflows into agentic processes and state machines",
      "Strong requirements discovery and stakeholder communication skills",
      "Experience running discovery workshops and co-design sessions with business and technical users",
      "Comfort operating in ambiguous customer environments with evolving requirements"
    ],
    "behavioral": [
      "High ownership of end-to-end outcomes (from discovery through production and iteration)",
      "Bias toward shipping incremental value quickly with tight feedback loops",
      "Ability to work cross-functionally with product, infra, security, and customer teams"
    ]
  },
  "responsibilities": {
    "discovery": [
      "Engage with customers to understand current workflows, systems, and pain points",
      "Identify automation and agentic opportunities with clear, measurable outcomes"
    ],
    "solution_design": [
      "Design agent architectures (single agents, agent graphs, and human-in-the-loop steps)",
      "Define tools and integrations for agents to interact with enterprise systems",
      "Specify constraints, guardrails, and approval workflows"
    ],
    "implementation": [
      "Implement agents using the organization’s agent platform (Agent OS) and orchestration framework",
      "Integrate agents with internal and external systems via APIs, queues, and events",
      "Configure data access and retrieval strategies for agents"
    ],
    "deployment_and_agentops": [
      "Design deployment pipelines and rollout strategies for agents (canary, phased rollout)",
      "Set up monitoring dashboards for agent health, latency, error rates, and business KPIs",
      "Handle incident response, rollback, and continuous improvement of agent workflows"
    ],
    "evaluation_and_governance": [
      "Build evaluation suites for agents (scenario tests, adversarial prompts, regression checks)",
      "Collaborate with security/compliance to ensure agents operate within policy and audit requirements",
      "Continuously refine prompts, tools, and policies based on telemetry and user feedback"
    ],
    "product_feedback": [
      "Provide structured feedback from deployments to product and infra teams",
      "Help prioritize platform features that generalize patterns from customer projects"
    ]
  },
  "interfaces": {
    "product_teams": [
      "Propose platform features based on recurrent deployment needs",
      "Validate new agent capabilities in pilot customers before general release"
    ],
    "infrastructure_teams": [
      "Define deployment, scaling, and observability requirements for agent workloads",
      "Collaborate on resource limits, tenancy, and reliability patterns for agents"
    ],
    "security_and_compliance": [
      "Work with security to define access controls and audit logging for agent actions",
      "Ensure deployments meet regulatory and internal policy requirements"
    ],
    "customer_teams": [
      "Partner with customer engineers and domain experts to co-design agentic workflows",
      "Train customer teams on usage, monitoring, and safe operation of agents"
    ]
  },
  "kpis": {
    "time_to_value_days": "Median days from project kickoff to first agent delivering measurable business value",
    "safe_deployment_rate_pct": "Percentage of deployments that pass predefined safety and evaluation gates without rollback",
    "agent_uptime_pct": "Percentage of time production agents are available and meeting SLOs",
    "incident_rate_per_quarter": "Number of material production incidents caused by agent behavior per quarter",
    "business_impact_metrics": [
      "Cost reduction (e.g., FTE hours saved, infra cost savings)",
      "Quality improvements (e.g., defect reduction, error rate reduction)",
      "Cycle time improvements (e.g., time-to-decision, time-to-resolution)"
    ],
    "expansion_and_retention": [
      "Net expansion in agent usage (number of workflows or business units using agents)",
      "Churn/retention of agent-based deployments across customers"
    ]
  }
}

This reflects the patterns in Beam’s Agent Deployment roles and other agentic engineering descriptions, tuned into your AgentOps framing.257259279252254255

7) Key skills for Agent Deployment Engineers​

Condensing from the spec and job ads:259279252254255257

  • Strong coding (Python or Node.js / TypeScript).
  • API and integration fluency.
  • Cloud + container basics (AWS/Azure/GCP, Docker/K8s).
  • LLM/agent framework experience and prompt engineering.
  • Evaluation and monitoring mindset (AgentOps).
  • Customer‑facing, domain‑aware, comfortable with ambiguity.

8) How Palantir trains / supports “agentic” FDEs​

Palantir doesn’t publish a formal “Agent Deployment Engineer” track, but AIP and “AI FDE” features indicate evolving training/support:264266263251

  • AI FDE as product: “AI FDE” is an AIP/Foundry agent that helps FDEs operate Foundry via conversational commands, embedding AI into the FDE workflow itself.263264
  • Internal enablement: AIP integration into Pipeline Builder and low‑code tools reduces the barrier for FDEs to build AI‑powered flows, and best‑practice docs show guidance on safe use and system integrity.253264

So Palantir trains and augments FDEs through platform features, internal documentation, and project‑based learning rather than a new titled role—functionally very similar to Agent Deployment Engineers.266264253263

9) Differences between FDE and traditional DevOps​

Key distinctions:280281282252269271

  • DevOps
    • Focus: CI/CD pipelines, infra automation, reliability of services.
    • Optimizes delivery and uptime of applications built by product teams.
  • FDE / Agent Deployment Engineer
    • Focus: mapping customer workflows to solutions (applications or agents) and getting them into production.
    • Owns both the “what” (solution design with stakeholders) and the “how” (implementation and deployment), often on a specific platform.282283269270271

AgentOps borrows SRE/DevOps concepts but applies them at the agent layer (prompt/policy changes, tool behaviors, agent graphs) rather than classic microservices only.284252282

10) Examples of AI agent deployments by FDE‑type roles​

From Beam and similar roles:279252254255259282

  • Multi‑step automation agents for finance ops, customer support, logistics, and IT workflows built on Agent OS/no‑code orchestration.
  • Agents that: ingest tickets/emails, query internal systems via APIs, propose or execute actions, and escalate to humans with context when needed.
  • Deployments that move from single‑step copilots to multi‑tool, multi‑system orchestrations with monitoring and evaluation loops.

Palantir case studies (Heineken, manufacturing, healthcare logistics) are moving in the same direction but often described as “AIP use cases / automations” rather than explicitly “agents.”278276277251

11) Challenges when evolving FDE → agentic workflows​

Common pain points in the literature:281285286252280282284

  • Non‑determinism: FDEs used to deterministic code paths; agents introduce probabilistic behavior, requiring evaluation frameworks and guardrails.
  • Tooling maturity: Agent orchestration, debugging, and monitoring tools are still immature vs classic app stacks.
  • Organizational trust: Convincing customers to let agents act (not just suggest) requires new governance and change‑management work.
  • Skill uplift: FDEs must learn prompt/policy design and failure‑mode thinking distinct from typical backend engineering.

This is exactly where a well‑defined Agent Deployment Engineer / AgentOps function provides structure and metrics (like those in the JSON spec above). 287288

⁂

Compare Palantir FDSE responsibilities to Snowflake Solutions Engineers

How do FDSE skills differ from standard software engineers Examples of FDSE projects from Palantir case studies Training path to become a Forward Deployed Engineer Salary ranges for customer-embedded engineers at SaaS companies.  Challenges when evolving FDE → agentic workflows Common pain points in the literature: Non‑determinism: FDEs used to deterministic code paths; agents introduce probabilistic behavior, requiring evaluation frameworks and guardrails. Tooling maturity: Agent orchestration, debugging, and monitoring tools are still immature vs classic app stacks. Organizational trust: Convincing customers to let agents act (not just suggest) requires new governance and change‑management work. Skill uplift: FDEs must learn prompt/policy design and failure‑mode thinking distinct from typical backend engineering. This is exactly where a well‑defined Agent Deployment Engineer / AgentOps function provides structure and metrics (like those in the JSON spec above).

Palantir FDSEs are closer to embedded product engineers than classic solution engineers: they own design + implementation of production systems on Foundry/AIP, while Snowflake Solutions Engineers are primarily pre‑sales, proving value and shaping deals.289290291292

Palantir FDSE vs Snowflake Solutions Engineer​

Palantir FDSE (Forward Deployed Software Engineer)291289

  • Responsibilities:
    • Sit side‑by‑side with customers, understand their hardest problems, architect and build solutions on Foundry/AIP.
    • Wrangle massive data, build custom applications, and drive projects from ideation to deployment.
    • Engage directly with stakeholders from engineers to executives.
  • Orientation: delivery & productized outcomes (production systems, long‑term usage).

Snowflake Solutions / Sales Engineer290292293

  • Responsibilities:
    • Present Snowflake’s technology and vision; run demos and PoCs.
    • Work hands‑on with prospects to prove value, design architectures, and win technical buy‑in.
    • Collaborate with product/engineering/marketing to refine positioning and roadmap.
  • Orientation: pre‑sales & enablement (technical win, deal acceleration, reference architectures).

You can think of FDSEs as “post‑sale, product‑grade builders” and Snowflake SEs as “pre‑sale, value‑proof architects.”

FDSE skills vs “standard” software engineers​

From Palantir FDSE job specs and FDE guides:294295296297289291

  • Overlap (both):
    • Strong CS fundamentals, data structures, algorithms.
    • Proficiency in languages like Python, Java, TypeScript, SQL.298299289
  • FDSE‑specific additions:
    • Customer‑facing: requirements discovery, workshops, navigating execs + front‑line users.296297289291
    • Data + domain: comfort with huge, messy datasets and domain modeling (supply chain, defense, healthcare).300301289291
    • Platform‑centric: deep expertise in one platform (Foundry/AIP), ontologies, pipeline tooling, and low‑code/high‑code mix.302303304305

A “standard” backend/frontend engineer can succeed without ever meeting customers; an FDSE cannot.

Examples of FDSE‑type projects​

Public case studies map directly to FDSE responsibilities even if partners are named:306307301308309310300

  • Wildfire risk / power grid (national utility) – Foundry used to build geospatial risk mapping and applications to identify and replace at‑risk power lines, modernizing data pipelines and front‑end apps for field teams.306
  • Supply chain control tower (global retailer) – Foundry‑based control tower reduced out‑of‑stock levels by ~50% using real‑time data and analytics.307
  • Operational asset intelligence (infrastructure / fleets) – Foundry used for predictive readiness across fleets; pipelines connect sensor data and maintenance logs to trigger proactive actions.300
  • Heineken USA supply chain – AIP + Foundry agents orchestrating tens of thousands of loads/containers and hundreds of distributors, built in ~3 months vs previous 3‑year timelines.308309310

These are exactly the “predict wildfire risk” / “adapt global supply chain” scenarios described in FDSE postings.289291

Training path to become a Forward Deployed Engineer​

Based on Palantir postings + industry FDE guides:311295294291296289

  • Prereqs:
    • Solid CS degree or equivalent, 2–5+ years in software engineering, ideally full‑stack or data‑heavy roles.295291296289
    • Comfort with Python/Java/TypeScript/SQL, cloud infra concepts, and large‑scale data.299298289
  • Early steps:
    • Start as SWE / data engineer, gain hands‑on with one major platform (data or AI) and enterprise integrations.
    • Move into roles with more customer exposure (solutions engineering, consulting, internal “embedded engineer” roles).312311294296
  • At Palantir:
    • FDSE roles emphasize willingness to relocate/travel, operate in ambiguous, high‑stakes domains, and own projects end‑to‑end.291289
    • Onboarding typically includes internal training on Foundry/Gotham/AIP and shadowing experienced FDSEs on live projects (described in FDE guides, though details are not fully public).311294296

A realistic path is SWE → senior SWE / data engineer with client exposure → FDSE/Forward Deployed AI Engineer.

Salary ranges for customer‑embedded engineers (high level)​

Exact Palantir numbers aren’t public, but we can benchmark against “customer solutions / solutions engineer” data and industry ranges:313314315294

  • Generic Customer Solutions Engineer (US, all industries):
    • Average: ~78.6k/year.
    • 25th–75th percentile: 70k–90k, top earners ≈101.5k.313
  • SaaS / big‑tech Solutions Engineer (Snowflake‑like, U.S.):
    • Guides suggest 80k–280k base with total comp (bonus + equity) up to 90k–400k, depending on seniority and region.314
  • Forward‑deployed / FDE‑style roles at high‑end SaaS/AI infra are generally at the upper end of solutions‑engineer bands; commentary notes these are “premium” positions combining senior SWE + SE skillsets.315294311

So for your modeling, treat “customer‑embedded engineer at AI/SaaS infra” as roughly comparable to a senior/principal solutions engineer or L4–L6 SWE, depending on company and geography.

Challenges evolving FDE → agentic workflows (recap)​

From Beam, FDE/AgentOps write‑ups, and forward‑deployed AI engineering posts:316317318319320321322323

  • Non‑determinism – Agents/LLMs introduce probabilistic behavior instead of fixed code paths, forcing FDEs to adopt evaluation suites, scenario tests, and guardrails.317319323316
  • Tooling maturity – Agent orchestration, debugging, and monitoring are far less mature than conventional app/SRE tooling; many teams are building ad‑hoc dashboards and hooks.318323316
  • Organizational trust – Getting customers comfortable with agents taking actions (not just suggesting) requires approvals, auditability, and change‑management.319322323
  • Skill uplift – FDEs must learn prompt/policy design, RAG patterns, and failure‑mode analysis in addition to “normal” coding and integration.320321323316317

These are precisely the gaps the Agent Deployment Engineer / AgentOps function is meant to address with explicit responsibilities, skills, and KPIs like time‑to‑value, safe‑deployment rate, and agent uptime.323316318 324325

⁂

Palantir FDSE interview process and preparation tips

Salary comparison FDSE vs Snowflake Solutions Engineer How Snowflake adopted Forward Deployed roles Career progression for Palantir FDSEs FDSE required technical skills and programming languages

Palantir’s FDSE interview pipeline tests strong generalist engineering plus product/problem‑solving skills, while Snowflake’s Solutions Engineers lean more toward pre‑sales architecture and customer persuasion, with comp bands that overlap but are incentive‑weighted differently.326327328329330331332

Palantir FDSE interview process & prep​

Typical stages, combining software‑engineer guides with FDSE‑specific reports:327328333334335336326

  1. Recruiter / phone screen
    • 20–45 minutes on background, motivation for Palantir/FDSE, basics of experience.328333
  2. Technical screen (remote)
    • 1‑hour coding (DSA, medium LeetCode‑style), pair‑programming style.
    • Emphasis on clarity, testing, and tradeoff discussion more than trick problems.337334335
  3. Onsite / virtual onsite (FDSE‑specific) Often three 1‑hour blocks:334335326327328
    • Coding: another medium‑difficulty algorithm/data‑structure problem.
    • Decomposition: break a messy, real‑world problem into technical components and propose a system‑level solution.
    • Re‑engineering / learning: read an unfamiliar codebase or concept and debug or extend it live.
  4. Hiring manager / team fit
    • Deep dive on past projects, customer interaction stories, how you handle ambiguity and ownership.335328

Preparation focus:

  • Coding:
    • Practice 60–75 minutes of medium LeetCode‑style problems in Python/Java/C++ + verbalizing thought process and tests.337334335
  • Decomposition/System‑thinking:
    • Practice turning vague prompts (“optimize power grid wildfire risk”, “improve supply chain delivery”) into entities, data flows, and APIs.330327334
  • “Learning on the fly”:
    • Be ready to be shown a new library/pattern and incorporate it mid‑interview.335
  • Behavioral:
    • Have 3–5 concrete stories on: driving ambiguous projects, handling tough stakeholders, rescuing failing initiatives.

Salary comparison: Palantir FDSE vs Snowflake Solutions Engineer​

Snowflake has more public SE comp data; Palantir FDSE is inferred from comparable roles.

Snowflake Solutions Engineer

  • Average salary: ~133k USD according to ZipRecruiter.329
  • Levels.fyi (Sales Engineer role): total comp range 213k–330k USD, median ≈292k USD across IC1–IC5 in the U.S.331

Palantir FDSE

  • No direct public band, but FDSE is usually benchmarked close to Palantir SWE at same level, slightly tilted by region/travel.
  • Snowflake SWE average ≈193.5k USD, with total comp median ≈330k USD and upper percentiles far higher.338339
  • Given FDSE skill profile (SWE + consulting), many industry comp discussions treat FDSE as “senior SWE‑ish” or better at top‑tier firms, putting realistic U.S. total comp in the mid‑200s to 300k+ USD range at strong levels.

Directional conclusion:

  • Snowflake SE and Palantir FDSE both sit in high‑end “customer‑facing engineering” comp territory, with FDSE likely closer to SWE bands at Palantir, and Snowflake SE clearly tracked as a high‑earning sales‑adjacent role.339338329331

How Snowflake uses FDE‑like roles​

Snowflake doesn’t use “FDE” branding, but Solutions Engineers and specialized field roles partially fill that niche:332340341

  • Solutions Engineers:
    • Lead technical discovery, architecture design, Proof‑of‑Concepts, and demos.
    • Partner tightly with AEs to win deals and ensure hand‑off to customer success and professional services.340332
  • In practice:
    • Some SEs become quasi‑embedded with strategic accounts during long POCs/migrations, but the primary mandate is still pre‑sales rather than full lifecycle engineering and product feedback loops like Palantir FDSEs.341332

So Snowflake has FDE‑adjacent field engineers but hasn’t centered its go‑to‑market around true forward‑deployed product engineers the way Palantir has.342343

Career progression for Palantir FDSEs​

Public material + FDE write‑ups suggest a few main paths:343344345346330342

  • Vertical within FDSE track
    • FDSE → Senior FDSE → tech lead on large accounts / regions.
    • More ownership of entire customer programs, mentoring, and cross‑customer patterns.
  • Lateral into core product / infra
    • Move from FDSE to internal SWE/product roles (Foundry/AIP core teams), bringing deep domain and customer insight.346347
  • Lateral into product management or sales engineering
    • Product: define platform features based on patterns seen across multiple customers.
    • Sales/SE: leverage customer‑facing skills to support strategic deals.
  • Management & strategic roles
    • FDSE manager / engagement lead, overseeing multiple accounts, utilization, and quality.
    • Regional technical leadership roles building playbooks for a sector (e.g., manufacturing, healthcare).342346

FDSE is intentionally designed as a high‑leverage, multi‑exit position rather than a cul‑de‑sac.344330346342

FDSE technical skills & languages (Palantir‑specific)​

From Palantir FDSE job descriptions and language overviews:348349330344

  • Core languages:
    • Python – data pipelines, services, scripting.
    • Java – scalable backend systems on Foundry/Gotham/AIP.
    • C++ – performance‑critical components.
    • TypeScript/JavaScript – UIs and custom apps.
    • SQL – querying and modeling data.349348330
  • Additional / sometimes used:
    • Scala – some big‑data/analytics contexts.
    • Go – certain backend/network services.348349
  • Platform & infra skills:
    • Working on top of Foundry/ AIP (pipelines, ontologies, app frameworks).350351352
    • Comfort with distributed systems, large‑scale data, and cloud concepts.330344

What differentiates FDSE from a generic SWE is not different languages, but the mix of: platform fluency, data modeling, and customer‑facing design/problem‑solving on top of that stack.345353344330

If you want a next‑step prompt for your agents:

“Given job descriptions and interview guides for Palantir FDSE and Snowflake Solutions Engineer, extract structured fields {role_type, interview_stages, technical_focus, customer_focus, primary_kpis, comp_band_estimates}. Normalize into JSON and compute a similarity score between roles along axes (coding_depth, pre_sales_weight, deployment_ownership, platform_specialization).”

⁂

Footnotes​

  1. https://investors.palantir.com/files/2024 FY PLTR 10-K.pdf ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16 ↩17 ↩18 ↩19 ↩20 ↩21 ↩22 ↩23 ↩24

  2. https://fintel.io/doc/sec-palantir-technologies-inc-1321655-10q-2025-may-06-20214-6375 ↩ ↩2 ↩3 ↩4

  3. http://pdf.secdatabase.com/61/0001321655-25-000106.pdf ↩ ↩2

  4. https://www.linkedin.com/posts/ericwang6_palantir-technologies-inc-released-its-2024-activity-7297629958166794240-0jv4 ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16 ↩17 ↩18

  5. https://materials.proxyvote.com/Approved/69608A/20250411/AR_605365.PDF ↩ ↩2

  6. https://www.sec.gov/edgar/browse/?CIK=0001321655 ↩ ↩2 ↩3

  7. https://financhle.com/articles/what-does-palantir-actually-do ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16 ↩17 ↩18

  8. https://en.wikipedia.org/wiki/Palantir_Technologies ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14

  9. https://www.youtube.com/watch?v=z28PdhFYHxI ↩ ↩2

  10. https://investors.palantir.com/financials/sec-filings ↩

  11. https://www.sec.gov/Archives/edgar/data/1321655/000119312521060650/d65934d10k.htm ↩

  12. https://companiesmarketcap.com/palantir/annual-reports-10k/ ↩

  13. https://www.stocktitan.net/sec-filings/PLTR/ ↩

  14. https://www.youtube.com/watch?v=jh7xdACplYI ↩

  15. https://www.sec.gov/Archives/edgar/data/1321655/000095012321010430/d175251d10q.htm ↩

  16. https://investors.palantir.com/financials/quarterly-results ↩ ↩2 ↩3

  17. https://investors.palantir.com/news-details/2026/Palantir-Reports-Q4-2025-U-S--Comm-Revenue-Growth-of-137-YY-and-Revenue-Growth-of-70-YY-Issues-FY-2026-Revenue-Guidance-of-61-YY-and-U-S--Comm-Revenue-Guidance-of-115-YY-Crushing-Consensus-Expectations/ ↩ ↩2 ↩3

  18. https://www.sec.gov/Archives/edgar/data/1321655/000132165526000004/a2025q4ex991earningsrelease.htm ↩ ↩2 ↩3

  19. https://www.sec.gov/edgar/browse/?CIK=0001321655 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  20. https://www.cnbc.com/2026/02/02/palantir-pltr-q4-2025-earnings.html ↩ ↩2 ↩3

  21. https://uk.finance.yahoo.com/news/palantir-technologies-inc-pltr-q4-050032107.html ↩

  22. https://investors.palantir.com/files/Palantir - Q4 2025 Investor Presentation.pdf ↩ ↩2 ↩3

  23. https://www.sec.gov/Archives/edgar/data/1321655/000132165525000106/pltr-20250630.htm ↩ ↩2

  24. https://www.stocktitan.net/sec-filings/PLTR/ ↩

  25. https://www.nasdaq.com/market-activity/stocks/pltr/sec-filings ↩

  26. https://content.edgar-online.com/ExternalLink/EDGAR/0001321655-25-000066.html?hash=309a22d7afb634e635424f0a0b240b0b5ab2e5a2854238c621b84055e17b3a06\&dest=pltr-20250331_htm_ie36c6cbff6b34511a948367c6d914a60_139 ↩

  27. https://br.investing.com/news/stock-market-news/receita-recorde-da-palantir-ofusca-valuation-estratosferico-1821023 ↩

  28. https://www.sec.gov/Archives/edgar/data/1321655/000095012321010430/d175251d10q.htm ↩

  29. https://www.otcmarkets.com/filing/conv_pdf?id=18888028\&guid=Ljb-ka42ZsruQOh ↩

  30. https://uk.investing.com/analysis/palantir-gains-fast-as-investors-digest-a-much-bigger-2026-revenue-path-200622028 ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13

  31. https://www.businesswire.com/news/home/20260201154946/en/Palantir-Reports-Q4-2025-U.S.-Comm-Revenue-Growth-of-137-YY-and-Revenue-Growth-of-70-YY-Issues-FY-2026-Revenue-Guidance-of-61-YY-and-U.S.-Comm-Revenue-Guidance-of-115-YY-Crushing-Consensus-Expectations ↩

  32. https://finance.yahoo.com/news/palantir-reports-q4-2025-u-210500951.html ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16 ↩17 ↩18 ↩19 ↩20 ↩21 ↩22 ↩23 ↩24 ↩25 ↩26 ↩27 ↩28 ↩29 ↩30 ↩31 ↩32 ↩33

  33. https://seekingalpha.com/news/4545908-palantir-outlines-61-percent-revenue-growth-target-for-2026-as-u-s-business-surges-93-percent ↩ ↩2 ↩3 ↩4 ↩5

  34. https://investors.palantir.com/files/Palantir - Q4 2025 Investor Presentation.pdf ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11 ↩12 ↩13 ↩14 ↩15 ↩16 ↩17 ↩18 ↩19

  35. https://www.investing.com/news/transcripts/earnings-call-transcript-palantirs-q4-2025-revenue-surges-stock-rises-93CH-4480410 ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10 ↩11

  36. https://br.tradingview.com/news/zacks:14592c0b3094b:0-is-palantir-a-top-tech-pick-for-2026-after-strong-q4-results-guidance/ ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  37. https://investors.palantir.com/events ↩ ↩2 ↩3

  38. https://www.wallstreethorizon.com/palantir-earnings-calendar ↩

  39. https://www.tipranks.com/stocks/pltr/earnings ↩

  40. https://www.ig.com/en/news-and-trade-ideas/Palantir-Q32025-earnings-251029 ↩ ↩2

  41. https://finance.yahoo.com/news/palantir-reports-q3-2025-u-210500770.html ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9

  42. https://www.investing.com/news/company-news/palantir-q3-2025-slides-us-commercial-revenue-soars-121-rule-of-40-hits-114-93CH-4328928 ↩ ↩2 ↩3 ↩4 ↩5

  43. https://investors.palantir.com/financials/quarterly-results ↩

  44. https://investors.palantir.com/news-details/2026/Palantir-Reports-Q4-2025-U-S--Comm-Revenue-Growth-of-137-YY-and-Revenue-Growth-of-70-YY-Issues-FY-2026-Revenue-Guidance-of-61-YY-and-U-S--Comm-Revenue-Guidance-of-115-YY-Crushing-Consensus-Expectations/ ↩

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