LLM Rate Limit and Throughput Planner
Will your workload fit inside your API provider's rate limits? Enter your traffic and your tier's limits to see peak RPM/TPM, utilization, and how to pace requests.
Your workload
Both input and output tokens count against TPM on most providers.
Evenly spread traffic = ~4%. Business apps often see 10-20% in the busiest hour.
Time from send to full response; used for the concurrency estimate.
Your provider limits
These presets are placeholder examples only - check your provider dashboard for your account's actual limits. They vary by provider, model, and tier.
0
Required RPM at peak
0
Required TPM at peak
0%
RPM utilization
0%
TPM utilization
0
Recommended concurrency
0
Inter-request delay
Verdict
The math, step by step:
1) Required RPM at peak = requests per day x peak hour share / 60. Example: 50,000 requests with 15% in the busiest hour is 7,500 requests in that hour, or 125 per minute.
2) Required TPM at peak = required RPM x avg tokens per request.
3) Utilization = required / limit x 100, for RPM and TPM separately. The tighter of the two is your real constraint. Under 80% is comfortable headroom; 80-100% is fragile (retries and traffic spikes will hit the ceiling); over 100% means requests will be rejected with 429 errors at peak.
4) Recommended concurrency = peak requests per second x avg latency (Little's Law), capped at what 80% of your RPM limit can sustain. This is how many requests are in flight at once.
5) Inter-request delay = 60 / (80% of the effective RPM limit) seconds. The effective limit is the smaller of your RPM limit and TPM limit / tokens per request. Pacing sends at this interval keeps you safely under both limits.
Batch and queue strategies: if you exceed limits at peak, you rarely need a bigger tier right away. Put requests in a queue and drain it at a steady rate (smoothing peak load into quieter hours), use exponential backoff with jitter on 429s, and move non-urgent work to your provider's batch API if it has one - batch jobs usually bypass live rate limits and cost around 50% less. Shrinking prompts also lowers TPM pressure directly; measure with the Token Counter.
1) Required RPM at peak = requests per day x peak hour share / 60. Example: 50,000 requests with 15% in the busiest hour is 7,500 requests in that hour, or 125 per minute.
2) Required TPM at peak = required RPM x avg tokens per request.
3) Utilization = required / limit x 100, for RPM and TPM separately. The tighter of the two is your real constraint. Under 80% is comfortable headroom; 80-100% is fragile (retries and traffic spikes will hit the ceiling); over 100% means requests will be rejected with 429 errors at peak.
4) Recommended concurrency = peak requests per second x avg latency (Little's Law), capped at what 80% of your RPM limit can sustain. This is how many requests are in flight at once.
5) Inter-request delay = 60 / (80% of the effective RPM limit) seconds. The effective limit is the smaller of your RPM limit and TPM limit / tokens per request. Pacing sends at this interval keeps you safely under both limits.
Batch and queue strategies: if you exceed limits at peak, you rarely need a bigger tier right away. Put requests in a queue and drain it at a steady rate (smoothing peak load into quieter hours), use exponential backoff with jitter on 429s, and move non-urgent work to your provider's batch API if it has one - batch jobs usually bypass live rate limits and cost around 50% less. Shrinking prompts also lowers TPM pressure directly; measure with the Token Counter.