The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

The infrastructure, utilities, and integration layers built around large language models, spanning local inference runtimes, context management, MCP servers, knowledge organization, and provider-agnostic design patterns.

Platform strategy governs how products, companies, and infrastructure define their foundational layer, control access to it, and build durable advantage — a question that runs from cloud architecture to AI tooling to startup positioning.

The startup ecosystem is shaped by failure rates, infrastructure bets, pricing dynamics, and product philosophy — sources here trace how dead companies, AI cost shifts, and great-product thinking collectively define the terrain.