API Rate Limit FAQ

Practical Ways to Stay Within Rate Limits

Knowing that limits exist is only half the picture. The other half is designing your integration so it behaves gracefully when it approaches them. The techniques below are widely used across most HTTP APIs and apply whether you are polling for topology updates or pulling batches of records for analysis.

Read the response headers

Many APIs return headers alongside each response that describe your current allowance, such as how many requests remain in the window and when the window resets. Common names include X-RateLimit-Remaining, X-RateLimit-Reset and, after a rejected call, Retry-After. Logging these values gives you a live view of consumption that is often more precise than a dashboard snapshot.

Handle HTTP 429 properly

The standard status code for too many requests is 429. When your client receives it, stop sending further calls immediately rather than retrying in a tight loop, which only extends the block. Honour any Retry-After value, and if none is provided, use exponential backoff: wait one second, then two, then four, adding a small random delay (jitter) so that multiple workers do not retry in lockstep.

Reduce the number of calls you make

• Cache responses that change rarely, such as reference data or configuration, instead of fetching them on every run.
• Batch requests where an endpoint accepts multiple items per call.
• Paginate sensibly, requesting larger pages where allowed rather than many small ones.
• Prefer events over polling if webhooks or push notifications are available.

Spread load over time

Bursty traffic is the most common cause of hitting limits. A simple client-side queue or token-bucket throttle lets scheduled jobs run steadily instead of firing hundreds of requests at the top of each hour. If several services share one set of credentials, remember they draw from the same allowance, so coordinate them or separate their keys where permitted.

Test before going live

Simulate limit responses in a staging environment to confirm your retry logic works and that failures surface clearly in your logs. For broader context on how request patterns affect infrastructure, the material on our Network page and the briefs in Research offer useful background.