AI Infrastructure Competitive KPIs Analysis

Multi-Factor Screening for "AI Infra" Companies

Executive Summary

This analysis compares key financial and operational metrics across the AI infrastructure landscape to inform CODITECT's competitive positioning. Palantir leads in Rule of 40 (127%), while Nvidia dominates absolute scale. The mid-market compliance automation space remains underserved.

Company Profiles

Palantir Technologies (PLTR)

Category: AI Operations Software
Platforms: Foundry, Gotham, AIP, Apollo
Target: Enterprise/Government
Moat: Ontology architecture, government trust, FDE model

Snowflake (SNOW)

Category: Cloud Data Platform
Products: Data Cloud, Snowpark, Cortex AI
Target: Enterprise analytics
Moat: Data sharing network, SQL ecosystem

Databricks

Category: Data + AI Platform
Products: Lakehouse, MLflow, DBRX
Target: Data science teams
Moat: Spark heritage, open-source ecosystem

Nvidia (NVDA)

Category: AI Hardware/Software
Products: GPUs, CUDA, AI Enterprise
Target: AI infrastructure
Moat: Silicon dominance, CUDA ecosystem

AMD

Category: CPU/GPU Hardware
Products: MI300, EPYC, ROCm
Target: AI/HPC infrastructure
Moat: Price/performance, Intel alternative

Financial Comparison Table

Q4 2025 / FY 2025 Metrics

Company	Revenue	Rev Growth YoY	GAAP Op Margin	Net Margin	Rule of 40
Palantir	$1.41B (Q4)	70%	41%	43%	127%
Snowflake	~$1.8B (FY)	30%	-40%	N/A	~46% (FCF)
Databricks	$4.8B ARR	>55%	N/A (private)	N/A	>105% (implied)
Nvidia	$130.5B (FY)	114%	~62%	~56%	~176%
AMD	$34.6B (FY)	~22%	~11%	~12%	~33%

Key Observations

Palantir 127% Rule of 40: Exceptional; software-like margins with hardware-like growth
Nvidia 176% Implied: Once-in-generation AI hardware demand
Snowflake Struggles: Negative GAAP margins despite 30% growth
Databricks Growing Fast: Private, but ~55% growth + positive FCF signals strength
AMD Challenged: Lower margins due to competitive pressure

Detailed KPIs by Company

Palantir (Q4 2025)

{
  "company": "Palantir",
  "period": "Q4 2025",
  "financials": {
    "revenue_musd": 1407,
    "revenue_growth_yoy_pct": 70,
    "revenue_growth_qoq_pct": 19,
    "gaap_operating_margin_pct": 41,
    "adj_operating_margin_pct": 57,
    "gaap_net_margin_pct": 43,
    "cfo_margin_pct": 55,
    "fcf_margin_pct": 56
  },
  "efficiency": {
    "rule_of_40_adj_op": 127,
    "rule_of_40_fcf": 126
  },
  "segments": {
    "us_commercial_revenue_musd": 507,
    "us_commercial_growth_yoy_pct": 137,
    "us_government_revenue_musd": 570,
    "us_government_growth_yoy_pct": 66
  },
  "deals": {
    "deals_ge_1m": 180,
    "deals_ge_5m": 84,
    "deals_ge_10m": 61
  }
}

Snowflake (FY 2025)

{
  "company": "Snowflake",
  "period": "FY 2025",
  "financials": {
    "revenue_mbusd": 3.4,
    "product_revenue_growth_yoy_pct": 30,
    "gaap_operating_margin_pct": -40,
    "adj_operating_margin_pct": 15,
    "fcf_margin_pct": 16
  },
  "efficiency": {
    "rule_of_40_adj_op": 45,
    "rule_of_40_fcf": 46
  },
  "retention": {
    "nrr_pct": 131
  }
}

Databricks (Dec 2025)

{
  "company": "Databricks",
  "period": "LTM Dec 2025",
  "financials": {
    "arr_busd": 4.8,
    "arr_growth_yoy_pct": 55,
    "fcf_positive": true
  },
  "efficiency": {
    "rule_of_40_implied": 105
  },
  "retention": {
    "nrr_pct": 140
  },
  "customers": {
    "customers_ge_1m_arr": 700
  }
}

Nvidia (FY 2025)

{
  "company": "Nvidia",
  "period": "FY 2025",
  "financials": {
    "revenue_busd": 130.5,
    "revenue_growth_yoy_pct": 114,
    "gross_margin_pct": 75,
    "operating_income_busd": 81.5,
    "operating_margin_pct": 62,
    "net_income_busd": 72.9,
    "net_margin_pct": 56
  },
  "efficiency": {
    "rule_of_40_implied": 176
  },
  "segments": {
    "data_center_revenue_busd": 115,
    "data_center_growth_yoy_pct": 142
  }
}

AMD (FY 2025)

{
  "company": "AMD",
  "period": "FY 2025",
  "financials": {
    "revenue_busd": 34.6,
    "revenue_growth_yoy_pct": 22,
    "gross_margin_pct": 50,
    "operating_income_busd": 3.7,
    "operating_margin_pct": 11,
    "net_income_busd": 4.3,
    "net_margin_pct": 12
  },
  "efficiency": {
    "rule_of_40_implied": 33
  }
}

Rule of 40 Visual Comparison

┌─────────────────────────────────────────────────────────────────────────────┐
│                        RULE OF 40 COMPARISON                                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Nvidia (FY25)      ████████████████████████████████████████████ 176%      │
│                     [114% growth + 62% margin]                              │
│                                                                             │
│  Palantir (Q4)      ████████████████████████████████ 127%                  │
│                     [70% growth + 57% margin]                               │
│                                                                             │
│  Databricks (LTM)   ██████████████████████████ 105%                        │
│                     [55% growth + ~50% implied]                             │
│                                                                             │
│  Snowflake (FY25)   ████████████ 46%                                       │
│                     [30% growth + 16% FCF margin]                           │
│                                                                             │
│  AMD (FY25)         ████████ 33%                                           │
│                     [22% growth + 11% margin]                               │
│                                                                             │
│  ─────────────────────────────────────────────────────────────────────────  │
│  Threshold          ████████ 40% (minimum healthy)                         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Platform Architecture Comparison

AI Platform Positioning

Dimension	Palantir	Snowflake	Databricks
Core Value	Data fusion + AI ops	Data warehousing	Data lakehouse + ML
AI Approach	Governed agents on ontology	Cortex AI, Snowpark	MLflow, DBRX models
Deployment	Cloud/on-prem/air-gapped	Cloud-only	Cloud-primary
Best For	Operational decisioning	Structured analytics	Model experimentation
Integration	Deep operational systems	BI ecosystem	Data science tools

Competitive Positioning Matrix

                    DATA FOCUS
                         │
                         │
    Snowflake ────────●  │
    (Warehouse)           │
                         │
                    ─────┼───── AI OPERATIONS
                         │
              Databricks │
              (Lakehouse)●────────● Palantir
                         │         (AI Ops)
                         │
                         │
                 EXPERIMENTATION

Go-to-Market Comparison

GTM Model Analysis

Dimension	Palantir	Snowflake	Databricks
Primary Motion	FDE + Bootcamp	Self-serve + SE	Field sales + CS
Sales Cycle	5 days (bootcamp)	30-90 days	60-180 days
Land Deal	$5M+	$100K+	$500K+
Expansion	FDE-driven	Consumption	Seats + compute
NRR	~140%+ implied	131%	140%

Bootcamp Model Economics (Palantir)

Day 1-2: Data Integration
Day 3: Workflow Design
Day 4-5: Deploy + ROI Demo
        ↓
   60% Conversion Rate
        ↓
   180 deals ≥$1M/quarter

CODITECT Competitive Positioning

Market Gap Analysis

Player	Strength	Gap	CODITECT Opportunity
Palantir	Enterprise scale, government trust	$5M+ deals, long cycles	Mid-market ($50K-$500K)
Snowflake	Data warehouse, SQL ecosystem	Not AI-operations native	Compliance automation
Databricks	ML experimentation, lakehouse	Not deployment-focused	Production agents
Nvidia	Hardware dominance	No application layer	Software differentiation

CODITECT Differentiation Strategy

┌─────────────────────────────────────────────────────────────────────────────┐
│                    CODITECT POSITIONING                                     │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────┐        ┌─────────────┐        ┌─────────────┐             │
│  │  Palantir   │        │  CODITECT   │        │ Snowflake/  │             │
│  │             │        │             │        │ Databricks  │             │
│  │ $10M+ deals │   →    │ $50K-$500K  │   →    │ Self-serve  │             │
│  │ 6-18 month  │        │ 2-day boot  │        │ Consumption │             │
│  │ Government  │        │ Healthcare/ │        │ Analytics   │             │
│  │ focus       │        │ Financial   │        │ focus       │             │
│  └─────────────┘        └─────────────┘        └─────────────┘             │
│                                                                             │
│  ENTERPRISE ←────────────────────────────────────────────→ SELF-SERVE      │
│                                                                             │
│                              CODITECT                                       │
│                         "Compliance-First                                   │
│                          Mid-Market AI"                                     │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Financial Targets for CODITECT

Benchmarking Against Leaders

Metric	Palantir	Industry Avg	CODITECT 18-Mo Target
Rule of 40	127%	40%	>50%
Revenue Growth	70% YoY	30%	100%+
GAAP Op Margin	41%	5%	>20%
FCF Margin	56%	15%	>15%
NRR	~140%	110%	>115%
Deals ≥$100K	180+/Q	—	10+/Q

Unit Economics Targets

Metric	Palantir	CODITECT Target
CAC Payback	<6 months	<3 months
LTV:CAC	>5:1	>3:1
Time to Value	5 days	2 days (20x ROI in 20 days)
Bootcamp Conversion	~60%	>50%

Multi-Factor Screening Framework

Factor Categories

{
  "screening_factors": {
    "growth_and_profitability": {
      "revenue_growth_yoy": "weight: 25%",
      "gaap_operating_margin": "weight: 15%",
      "adj_operating_margin": "weight: 10%",
      "rule_of_40": "weight: 20%"
    },
    "cash_and_capital": {
      "operating_cash_flow_margin": "weight: 10%",
      "fcf_margin": "weight: 10%",
      "net_cash_position": "weight: 5%"
    },
    "ai_leverage": {
      "pct_revenue_ai_segments": "weight: 15%",
      "net_revenue_retention": "weight: 15%"
    },
    "customer_structure": {
      "customers_ge_1m_arr": "weight: 10%",
      "rdv_growth": "weight: 5%"
    },
    "risk_factors": {
      "customer_concentration": "penalty: -10%",
      "sbc_as_pct_revenue": "penalty: -5%"
    }
  }
}

Composite Score Calculation

def calculate_ai_infra_score(company_metrics):
    score = 0
    
    # Growth & Profitability (70% weight)
    score += normalize(company_metrics['revenue_growth_yoy']) * 0.25
    score += normalize(company_metrics['gaap_op_margin']) * 0.15
    score += normalize(company_metrics['adj_op_margin']) * 0.10
    score += normalize(company_metrics['rule_of_40']) * 0.20
    
    # Cash (15% weight)
    score += normalize(company_metrics['cfo_margin']) * 0.10
    score += normalize(company_metrics['fcf_margin']) * 0.05
    
    # AI Leverage (15% weight)
    score += normalize(company_metrics['ai_revenue_pct']) * 0.10
    score += normalize(company_metrics['nrr']) * 0.05
    
    # Penalties
    if company_metrics['customer_concentration'] > 0.20:
        score -= 0.10
    if company_metrics['sbc_pct_revenue'] > 0.15:
        score -= 0.05
    
    return score

Sources

Palantir Q4 2025 Investor Presentation & Earnings Release
Snowflake FY 2025 Financial Results
Databricks Series L Funding Announcement
Nvidia FY 2025 Earnings
AMD FY 2025 Annual Report
Industry analyst reports and coverage

Document Version: 1.0 | Date: February 2026

Multi-Factor Screening for "AI Infra" Companies​

Executive Summary​

Company Profiles​

Palantir Technologies (PLTR)​

Snowflake (SNOW)​

Databricks​

Nvidia (NVDA)​

AMD​

Financial Comparison Table​

Q4 2025 / FY 2025 Metrics​

Key Observations​

Detailed KPIs by Company​

Palantir (Q4 2025)​

Snowflake (FY 2025)​

Databricks (Dec 2025)​

Nvidia (FY 2025)​

AMD (FY 2025)​

Rule of 40 Visual Comparison​

Platform Architecture Comparison​

AI Platform Positioning​

Competitive Positioning Matrix​

Go-to-Market Comparison​

GTM Model Analysis​

Bootcamp Model Economics (Palantir)​

CODITECT Competitive Positioning​

Market Gap Analysis​

CODITECT Differentiation Strategy​

Financial Targets for CODITECT​

Benchmarking Against Leaders​

Unit Economics Targets​

Multi-Factor Screening Framework​

Factor Categories​

Composite Score Calculation​

Sources​