Amazon SageMaker · Rate Limits

Amazon Sagemaker Rate Limits

Amazon SageMaker exposes a control-plane API (CreateTrainingJob, CreateEndpoint, etc.) that follows AWS API throttling per account/region, plus a runtime InvokeEndpoint surface whose throughput scales with the underlying instance count and instance type. Endpoint-specific quotas (concurrent invocations, payload size, timeout) are configurable. ServiceQuotas governs the maximum number and type of instances per account.

Amazon Sagemaker Rate Limits is the machine-readable rate-limit profile for Amazon SageMaker on the APIs.io network, conforming to the API Commons Rate Limits specification.

It captures 5 rate-limit definitions, measuring varies, requests_per_second, bytes, seconds, and count.

The profile also includes 4 backoff/retry policies defined and response codes documented for throttled, quotaExceeded, and serviceUnavailable.

Tagged areas include Rate Limiting, Machine Learning, and SageMaker.

5 Limits Throttle: 400 Quota: 400
Rate LimitingMachine LearningSageMaker

Limits

SageMaker control-plane API account/region
varies
see Service Quotas console for SageMaker
Standard AWS API throttling envelope.
InvokeEndpoint (real-time) endpoint
requests_per_second
scales with instance count and type
Default soft limit per endpoint; configure auto-scaling on the production variant. Payload up to 6 MB synchronous, 1 GB asynchronous.
InvokeEndpoint payload size endpoint
bytes
6291456
6 MB max synchronous payload; use AsynchronousInferenceConfig for larger payloads (up to 1 GB).
Synchronous invocation timeout endpoint
seconds · second
60
Default 60s; can be raised on async endpoints up to 1 hour.
ML instances per type per region account/region
count
see Service Quotas console for SageMaker
Soft limits; raise via Service Quotas before training/deploying at scale.

Policies

Backoff with jitter
AWS SDKs default to standard retry mode (truncated exponential backoff with jitter, max 20s, 3 attempts).
Auto-scaling
Configure target-tracking scaling on production variants (InvocationsPerInstance) to absorb load.
Quota increases
ML instance counts, training-job concurrency, and notebook quotas are all soft limits; raise via Service Quotas before campaigns.
Async inference for large payloads
Use SageMaker Asynchronous Inference for payloads >6 MB or processing >60s, queuing requests to a SageMaker-managed S3 location.

Sources

Work with this as data

Every rate limit here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for rate limits

4 MCP tools reach this
  • find_rate_limitsBrowse and filter every rate limit in the catalog.
  • apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
  • resolveTurn a domain, URL or GitHub org into the provider it belongs to.
  • find_cohortsEvery scored population of providers in the catalog.
All 92 tools →

Call it yourself

curl for this page
This rate limit 
curl "https://apis.io/api/v1/rate-limits/amazon-sagemaker-rate-limits"
All rate limits 
curl "https://apis.io/api/v1/rate-limits?limit=25"

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