LLM Rate Limit Capacity Planner
Turn your provider's RPM and TPM limits into real-world numbers: how many concurrent users you can sustain and how often callers will hit 429 Too Many Requests. Built for anyone sizing traffic against OpenAI, Anthropic, Google, or open-source inference endpoints.
How this LLM rate limit capacity planner works
An API rate limit is not an abstract number. It is a hard ceiling on the request volume and token throughput your application can push in a rolling 60-second window. This planner converts those ceilings into the metric product teams actually care about: how many simultaneous users the service supports, and how many requests will be rejected with a 429 Too Many Requests response.
For each concurrent user, the planner multiplies their per-minute request rate by the burst multiplier to estimate peak demand. It compares that demand against both the RPM limit and the TPM limit (requests times tokens per request). The binding constraint is whichever runs out first, and the maximum sustainable user count is derived from it. Expected 429s are estimated with a simple Poisson-style overflow model: when peak demand exceeds capacity, the rejected fraction approximates 1 - capacity/demand, scaled by the share of time the system is over its limit. The result is a percentage of requests that will fail and an absolute count of dropped calls per minute.
Use it before a launch, before onboarding a large customer, or when switching models or providers. If the verdict comes back red, your options are to raise limits with the provider, add request queuing and exponential-backoff retries, cache responses to cut token volume, reduce per-request prompt size, or shard traffic across multiple API keys. Pair the results with token-level budgeting and latency estimates to get a complete picture of inference capacity.