AI docs · Operations
Deploying AI to production
Turning a working prototype into a reliable, observable, and safe system real users depend on.
What it is
- Deployment is everything between a demo that works and a service that keeps working under real load and edge cases.
- It covers reliability, monitoring, safety, cost control, and the ability to change safely.
How it works
- Add observability (logging, tracing, metrics) so you can see what the system does.
- Put guardrails on inputs and outputs, rate limits, and fallbacks for failures.
- Roll out changes gradually with evals and the ability to roll back.
Trade-offs
- Production-grade systems cost more to build than prototypes, but prototypes break in the real world.
- More guardrails mean more safety but also more complexity.
When to use it
- Before exposing an AI feature to real users or critical workflows.
Common pitfalls
- Shipping a demo as a product with no monitoring or fallbacks.
- No way to measure quality or roll back a bad change.
Related concepts
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