2. Cost control & quotas. Per-team/per-app budgets, rate limits, and usage attribution — so you can answer "who spent what" and cap runaway agents.
3. Routing & fallback. Route by task to the right model (cheap model for classification, frontier model for hard reasoning) and fail over to a backup provider when one is down.
4. Caching. Cache identical/similar requests at the edge to cut cost and latency — especially valuable for repeated prompts.
5. Observability. Centralized logging, latency/token metrics, and tracing across every AI call — the data you need for reliability and cost reviews.
6. Security & compliance. PII redaction, prompt-injection filtering, content policy, and audit logs enforced once, at the boundary, not re-implemented per app.
Capabilities to look for
Whether you adopt a dedicated AI-gateway product, extend an existing API gateway (Apigee, Kong, etc.), or build a thin internal proxy, the checklist is similar:
- Provider-agnostic interface (swap/route models without app changes)
- Per-consumer authentication, quotas, and rate limiting
- Cost attribution and budget alerts
- Semantic/response caching
- Streaming passthrough (don't break token streaming)
- Guardrails: PII, injection, content filtering
- Full request/response observability with tracing
Build vs adopt
A thin proxy you own is fine to start — it gets you key centralization, logging, and basic rate limiting fast. Adopt a dedicated gateway when you need richer governance (multi-team quotas, semantic caching, guardrails) without building it all yourself. Either way, the architectural move is the same: stop letting apps talk to providers directly.
Wrap-up
An AI gateway is the enterprise control plane for LLM traffic — keys, cost, routing, caching, security, and observability in one place. Introduce it before the sprawl, not after: retrofitting governance across a dozen teams is far harder than routing through a gateway from the start.