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APIs & integration · LessonBy , SailPoint Architect · Published · IdentityIQ 8.4 · all levels

Rate limits

How SailPoint API rate limits work and how to stay within them.

Quick answer

SailPoint enforces per-tenant rate limits; exceeding them returns HTTP 429, and clients should back off and retry using the response headers.

Key takeaways
  • Limits are enforced per tenant
  • 429 Too Many Requests signals throttling
  • Respect Retry-After / limit headers
  • Batch and paginate to reduce calls

SailPoint enforces rate limits to protect the platform from overload. When a client exceeds its allowance the API responds with 429 Too Many Requests, and well-behaved clients back off and retry using the information in the response headers. Designing for rate limits from the start avoids brittle, failure-prone integrations.

How rate limiting works

Limits are applied per tenant over a time window. Responses include headers describing your remaining quota and, on a 429, how long to wait before retrying (for example a Retry-After header). Treat these headers as the source of truth rather than guessing.

Reacting to 429 correctly

  • Honor Retry-After when present, wait exactly that long before retrying.
  • Use exponential backoff with jitter for repeated 429s, so many clients do not retry in lockstep.
  • Never hot-loop retries, that amplifies the overload and can extend throttling.

Reducing how often you hit limits

  • Filter server-side so you request only needed records.
  • Paginate with sensible page sizes rather than many tiny calls.
  • Use bulk endpoints where available.
  • Avoid bursts of parallel calls, spread the work.

Common pitfalls

  • Bursty parallelism hammering the API and triggering 429s.
  • Ignoring Retry-After and retrying immediately.
  • No backoff, turning a brief limit into a sustained failure.

Practice challenge

+0 XPStreak ×0
Question 1 of 3
What status signals rate limiting?

Frequently asked questions

What does the term Rate limits refer to in SailPoint?
SailPoint enforces rate limits to protect the platform from overload. When a client exceeds its allowance the API responds with 429 Too Many Requests, and well-behaved clients back off and retry using the information in the response headers.
What is the practical takeaway on Rate limits?
Responses include headers describing your remaining quota and, on a 429, how long to wait before retrying (for example a Retry-After header).
What is worth remembering about Rate limits in practice?
Treat these headers as the source of truth rather than guessing.
What tends to go wrong with Rate limits?
Bursty parallelism hammering the API and triggering 429s. Ignoring Retry-After and retrying immediately. No backoff, turning a brief limit into a sustained failure.
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