LLMOps & production
Ship, watch, roll back — the AI gateway is your paved road.
Getting an AI feature to a demo is easy; running it reliably is the job. LLMOps is the discipline of deploying, versioning, observing and governing AI in production — and the AI gateway is the single highest-leverage platform thing you can champion.
Key ideas
- 1
Version everything: prompts, models, retrieval configs and tools — so you can deploy, compare and roll back deliberately.
- 2
Observe in production: trace every request (tokens, latency, cost, tool calls), watch quality drift, and capture user feedback for your eval sets.
- 3
Build an AI gateway: one governed path for all LLM calls — routing, model allow-list, rate limits, cost caps, logging, PII redaction, data-residency.
- 4
Roll out safely: canary/A-B prompt & model changes; have a kill switch and fallbacks.
- 5
Close the loop: production signals feed evals, which gate the next change. Ops and evals are two halves of one system.
The AI gateway (paved road)
- Every team's LLM call goes through one governed proxy → consistent logging, cost control and safety.
- Central place to enforce model allow-lists, redact PII, and pin data residency (key for an EU insurer).
- Makes the compliant path the easy path — adoption follows defaults.
Operate it
- Dashboards for quality, cost, latency and abuse; alerts on spikes/drift.
- Prompt/model registry with versions and rollback.
- Feedback capture → labeled data → eval sets → CI gate.
Watch
Do the work
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