Who this is for: platform and infrastructure leads, ML engineers, and AI engineering managers responsible for inference serving, observability, RAG stores, and production AI reliability. You are choosing inference engines, wiring eval and drift monitoring into production, sizing RAG and vector stores, and owning the cost and reliability of AI in production — and you need analysis that maps to those decisions, not vendor listicles.
How this layer is organized
Gen α AI sorts its coverage into five layers of the AI stack — Energy, Chips, Infrastructure, Models, and Applications — using a computed taxonomy applied to every article at render time. This hub collects every piece the taxonomy classifies into the Infrastructure layer: inference engines and serving, observability and telemetry, LLMops and MLOps, RAG stores and retrieval, eval stacks and harnesses, capacity and FinOps, latency and failover, and production hardening. Infrastructure is the highest-commercial-priority layer in that taxonomy — it is where the largest concentration of buyer-intent decisions sits — which is why it gets the first dedicated hub.
The article list and the count above are computed at render time from the same taxonomy rules in taxonomy.js that tag each article — there is no hand-curated selection and no traffic or popularity ranking behind the order. Pillars surface first, then pieces sort by editorial quality and recency. If a piece is missing, the taxonomy rules did not classify it here; the rules are iteratively refined.
The Infrastructure library
57 articles in this layer. The grid below renders every one of them.
