What is API Rate Limiting?
API rate limiting restricts the requests a client may make within a defined period or resource budget. A service can apply limits by account, credential, endpoint, address, or another usage boundary.
How API Rate Limiting works
A rate limiter accounts for requests or resource cost against a policy assigned to a client identity or shared scope. Fixed windows, sliding windows, token buckets, and leaky-bucket designs differ in how they tolerate bursts and smooth usage. Transloadit applies limits of this kind to Assembly creation, to the number of concurrently running Assemblies, and to status polling, and it reports an authoritative retry delay in the error body so clients can schedule work without creating synchronized retry storms.
Key facts
- 1HTTP 429 conventionally signals that a request exceeded a limit, and a
Retry-Afterheader can tell the client when to retry when a service supplies one. Transloadit’s rate-limit errors instead carry the RATE_LIMIT_REACHED code with aninfo.retryInvalue in seconds in the JSON body, so clients should schedule retries from that field rather than expect aRetry-Afterheader. - 2Token-bucket policies permit controlled bursts while enforcing a longer-term refill rate; fixed-window counters can allow sharp boundary bursts around a window change.
- 3A distributed limiter needs coordinated or intentionally approximate counters across service instances, or clients may receive inconsistent quotas depending on which instance handles a request.
When API Rate Limiting matters
Design clients to batch work, cache responses, monitor quotas, and retry with bounded backoff driven by the service’s own retry hints. Ignoring limit responses can amplify traffic, delay recovery, and repeatedly fail otherwise valid operations.
Common use cases for platform workflows
These examples cover platform workflows broadly, not specifically API Rate Limiting.
- Running repeatable upload, import, processing, AI, storage, and notification pipelines.
- Tracking long-running media work independently from an application request.
- Referencing centrally stored credentials by name instead of sending storage secrets with each request.
Working with platform workflows
This guidance covers platform workflows broadly, not just API Rate Limiting.
A client authenticates and submits files or references together with workflow instructions. The platform validates the request, schedules dependent operations, records state transitions, and exposes results through a response, polling endpoint, or notification.
Platform concepts become reliable only when their lifecycle is explicit. Authentication, idempotency, retries, timeouts, observability, quotas, and terminal states should be designed together rather than added after failures occur.
What you gain
- Reusable workflows separate application intent from processing infrastructure.
- Stable job identifiers and lifecycle events improve observability and recovery.
- Managed queues and workers let products scale without embedding every media tool.
What it costs
- Synchronous responses are simple but keep connections open while long work executes.
- Aggressive retries improve recovery from transient faults but can duplicate work or overload a dependency.
- Higher concurrency reduces queue time until resource contention or a downstream limit becomes the bottleneck.
Before production
- 1Define authentication, authorization, idempotency, retries, and terminal error behavior.
- 2Observe queue time, execution time, callbacks, and partial results with stable identifiers.
- 3Exercise malformed, duplicate, interrupted, and unauthorized requests before launch.