AI infrastructure is the technical foundation that supports AI workloads inside an organization. It includes model serving infrastructure, vector stores, embedding pipelines, orchestration runtimes, evaluation tooling, and the observability layer required to operate any of it in production.
AI infrastructure is distinct from AI applications. The infrastructure is the substrate; the applications are what you build on top of it. Most organizations consume infrastructure from providers — OpenAI, Anthropic, cloud platforms — and build applications and orchestration on top.
The infrastructure choice has outsized downstream consequences. Vendor lock-in, cost trajectory, latency, and the ceiling on what the organization can build are all set at the infrastructure layer.
Why AI infrastructure Matters for Distributed Teams
Underinvesting in AI infrastructure produces fragile demos. Overinvesting produces wasted engineering effort. The right level depends on how core AI is to the product strategy.
Frequently Asked Questions
What is AI infrastructure?
AI infrastructure is the technical foundation that supports AI workloads — model serving, vector stores, embedding pipelines, orchestration runtimes, and observability tooling. Most organizations consume infrastructure from providers and build applications on top.
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