Build vs Buy ML Infrastructure - Interactive Visualization

Every ML team faces the same recurring question: build the infrastructure in-house, buy a SaaS solution, or adopt an open-source tool? The answer depends on team size, scale, data privacy requirements, and how much the component differentiates your product. SaaS is cheapest and fastest at small scale (under $50k/year). Open-source tools give you control without full build cost at medium scale. Building in-house only makes economic sense at large scale or when the capability is a genuine product differentiator. This calculator quantifies the total cost of ownership across feature store, model serving, experiment tracking, data pipeline, vector database, and monitoring - adjusted for your team size and scale.

• Feature store: SaaS (Tecton) costs $3.5k/mo vs 16 weeks of eng time to build; OSS (Feast) is the middle path
• Model serving: building a production-grade serving system takes 20+ eng-weeks - rarely justified below 1M req/day
• Vendor lock-in cost: switching a feature store after 2 years costs 3–6 months of migration eng time
• Data privacy forces build or OSS: regulated industries (healthcare, finance) cannot send data to SaaS vendors

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