BUILD VS BUY DECISION FRAMEWORK
The build vs buy decision for AI infrastructure spans three dimensions: compute (GPU hardware), platform (software stack), and model (AI capabilities). Each dimension has distinct breakpoints. Compute: build when utilization exceeds 70 percent predictably for 2+ years and scale exceeds 500 GPUs. Platform: buy (use open-source or vendor) when requirements are standard. Model: buy (use API) for general capabilities, build for proprietary differentiation.
Strategic value matrix: low strategic value + low differentiation = buy (inference API, standard GPUs on cloud). High strategic value + high differentiation = build (proprietary models, specialized hardware). The matrix changes over time: as GPU technology commoditizes, compute shifts from build to buy. As models become differentiated IP, model development shifts toward build.
| Dimension | Build When... | Buy When... | 3-Year Cost Impact | Velocity Impact |
|---|---|---|---|---|
| GPU Compute | 500+ GPUs, 70%+ util | < 500 GPUs, variable | Build saves 35-50% | Buy faster by weeks |
| Training Platform | Custom framework needs | Standard PyTorch/TF | Build adds 2-5 FTE/yr | Buy saves 3-6 months |
| Model Development | Core business IP | General capability | Build costs $1-20M | Buy saves 6-18 months |
| Model Serving | 1,000+ QPS steady | < 1,000 QPS variable | Build saves 40-60% | Buy faster by months |
| Data Pipeline | Unique data sources | Standard preprocessing | Build costs 1-3 FTE/yr | Buy saves 2-4 months |
COMPUTE: GPU CLUSTER BUILD VS BUY ANALYSIS
GPU compute build vs buy analysis at 1,024 H100 scale: cloud on-demand $46.2M over 3 years, cloud reserved $25.8M, colocate $18.5M, on-premise $14.2M. The 3.3x delta between on-demand and on-premise represents the build premium for flexibility. Breakeven analysis: on-premise vs reserved breaks even at 14 months at 85 percent utilization, 22 months at 60 percent utilization.
Risk adjustments: GPU generation risk favors cloud. If H100 cluster becomes obsolete in 2 years due to B200 efficiency gains, on-premise TCO doubles per effective compute unit. Cloud absorbs this risk. Financing structure: cloud is operating expense (OpEx) with monthly billing, on-premise requires $9-12M capital expenditure (CapEx) at 3-year MACRS depreciation.
PLATFORM: TRAINING AND INFERENCE SOFTWARE
Platform build vs buy: custom training platform requires 3-8 engineers, 6-12 months, and $600K-$2M initial cost, plus $300K-$800K annual maintenance. Vendor platforms (Weights and Biases, Neptune, MLflow managed) cost $15K-$100K annually. Open-source platforms (MLflow OSS, Kubeflow) offer middle ground: $100K-$300K implementation cost and 1-2 FTE for maintenance.
The build decision is warranted when: integrating proprietary hardware schedulers, supporting custom model architectures not in standard frameworks, requiring special data governance for regulated industries, or needing deep integration with existing engineering toolchain. For 80 percent of organizations, buying or using open-source platforms is the correct decision.
MODEL: BUILD VS API CONSUMPTION
Model build vs API is the most consequential AI infrastructure decision. API consumption (OpenAI, Anthropic, Google) costs $2-$15 per million tokens but provides instant access to frontier capabilities. Proprietary model development costs $1M-$20M for training and requires 3-18 months. Breakeven: at 500 million tokens daily, API costs $300K-$2.2M monthly, making model ownership potentially cost-effective.
Hybrid approach is winning strategy: general capabilities via API, fine-tuned models for domain-specific tasks, and proprietary foundation models only when API cannot provide differentiation. Companies with hybrid model strategy spend 60 percent API, 30 percent fine-tuned, 10 percent proprietary by compute budget, achieving best cost-performance balance.