LLM platforms behave like expensive shared infrastructure. Without quotas, one team’s spike can exhaust provider limits, trigger retries, and degrade experiences across the organisation. Rate limiting and quota management are core reliability controls—not just cost controls.
Define what you are limiting
Traditional rate limits (requests per second) are necessary but insufficient for LLMs. You often need to manage:
- Tokens per minute. A better proxy for provider usage and cost.
- Concurrent requests. Prevents queue blowouts and timeouts.
- Tool calls. Especially for agents that can loop or fan out.
Apply limits by identity and priority
Good platforms limit by:
- Tenant/team. Prevents noisy neighbours and supports chargeback (see FinOps).
- User role. Different limits for pilots vs production.
- Intent tier. Higher priority for operational workflows than exploratory prompts.
Design for graceful degradation
When limits are hit, the user experience should degrade safely:
- Queue with transparent messaging for low-priority traffic.
- Route to smaller models or reduced context for non-critical flows.
- Fail closed for high-risk tool actions rather than retrying blindly.
These behaviours should be aligned with your reliability objectives (see AI SLOs) and incident playbooks (see incident response).
Use quotas to shape behaviour
Quotas are incentives. If teams see clear unit economics and budget impact, they make better architectural choices:
- Adopt caching (see caching strategies).
- Improve retrieval and reduce token waste (see context engineering).
- Move to structured outputs to reduce retries (see structured outputs).
Rate limiting is one of the most practical controls to make AI platforms predictable at scale.