Running a self-hosted LLM takes a correctly sized GPU, an efficient inference server and an operational layer for auth, monitoring, updates and fallback, and that last part decides whether it survives.
For most businesses an LLM API wins on cost and speed to ship, and self-hosting an open-weight model only pays off with high steady volume, strict data control or tight latency needs.
For most businesses the answer is retrieval first: RAG fixes what a model doesn’t know, fine-tuning changes how it behaves, and plenty of use cases need neither.
LLM integration is mostly about the layers around the model: a gateway, context assembly, validation, evaluation, observability and guardrails. This guide covers how to build each one for production.
Consumer and enterprise software, built with taste and shipped fast.
AI agents, automation, and orchestration - production systems, not demos.
Hands-on engineering and strategy for teams shipping AI into production.