Control token budgets, cap GPU concurrency, auto-downgrade models, and guard agent tool calls — through policies, not code changes.
AI cost and abuse incidents almost always trace back to the same root cause — rate limiting was an afterthought.
Each scenario links to a dedicated page with a side-by-side Without RLAAS / With RLAAS comparison, policy config, and SDK code.
Enforce daily or monthly token limits per user, per org, or per pricing tier — scoped to a specific model. Deducts real token cost, not just request count.
When a premium model's RPM is exhausted, return action: "downgrade" and the caller routes to a cheaper model — before the expensive call is made.
Cap concurrent GPU training jobs per org. The slot is held for the full job duration with acquire/release — auto-released via TTL if the job crashes.
Autonomous agents can spiral — a single planning loop may make thousands of tool calls in minutes. Rate-limit each tool type per session with a sliding window.
Map API cost to RLAAS units and enforce a hard daily spend cap per tenant. No surprise bills. Change the budget live — zero redeploy required.
Embedding calls for RAG pipelines can flood vector DB APIs. Apply per-service sliding-window limits — prevent upstream 429s before they happen.
You don't know output token count until the stream ends. Two-phase pattern: pre-check on input, deduct output after the stream completes.
One client.check() call. One policy. No redeploy.