Self-Hosting Your AI Stack: A Practical 2026 Guide
From model choice to serving and monitoring, here is a sane blueprint for running AI on your own infrastructure.
Self-hosting is no longer exotic. With open weights and mature tooling, a small team can run a capable AI stack for a fraction of API costs.
A sane blueprint
- Pick the model — start with an open-weight model that passes your evals.
- Serve it — vLLM for throughput, llama.cpp for edge.
- Observe it — log latency, tokens, and error rates from day one.
The honest trade-off
You trade convenience for control and cost savings. For privacy-sensitive or high-volume workloads, that trade is increasingly worth it.
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