Rate limit on FieldPulse API and handling 429 errors

I have been reviewing the API Rate Limits and Best Practices documentation, but I would like to clarify several points regarding the exact semantics of the throttling mechanism.

Specifically:

  1. Is the rate limit enforced per API key, per account, or per IP address? I.e., if multiple services share a single account's API credentials, do they share a single quota?
  2. What are the exact header values returned on a 429 response? I have observed X-RateLimit-Remaining and X-RateLimit-Reset in other systems, but I want to confirm the FieldPulse implementation before implementing client-side logic.
  3. Is the reset window a fixed calendar window (e.g., top of minute) or a rolling window from first request? This materially affects backoff calculations for bursty traffic patterns.
  4. Are there different tiers or limits for read vs. write operations? I.e., does GET /work_orders consume quota identically to POST /work_orders?

I am also interested in any edge cases or failure modes: e.g., if a request is rate-limited, are idempotent retries safe for non-idempotent methods (POST, PATCH) given potential server-side partial processing?

Finally, has anyone benchmarked the actual vs. documented limits? I prefer empirical data over specification when designing resilient systems.

Parents
  • Following up on your question about benchmarks, Omar: we don't publish formal throughput tests, but what Carlos said matches what I've heard from the infrastructure team. The 100 req/min limit is enforced at the edge and is fairly rigid — you won't see 105 sustained.

    For high-volume use cases, the recommended pattern is:

    1. Use the since parameter on list endpoints to fetch only changed records
    2. Implement a local cache of IDs to avoid re-fetching
    3. Consider webhooks for real-time updates rather than polling

    If you're genuinely hitting limits on legitimate use cases, open a support ticket — there are elevated tiers for enterprise accounts that aren't self-serve documented.

Reply
  • Following up on your question about benchmarks, Omar: we don't publish formal throughput tests, but what Carlos said matches what I've heard from the infrastructure team. The 100 req/min limit is enforced at the edge and is fairly rigid — you won't see 105 sustained.

    For high-volume use cases, the recommended pattern is:

    1. Use the since parameter on list endpoints to fetch only changed records
    2. Implement a local cache of IDs to avoid re-fetching
    3. Consider webhooks for real-time updates rather than polling

    If you're genuinely hitting limits on legitimate use cases, open a support ticket — there are elevated tiers for enterprise accounts that aren't self-serve documented.

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