Amazon SageMaker AI Async Inference now supports inline request payloads
Amazon just removed a friction point from AI inference. Inline payloads cut S3 uploads. Smaller latency win, bigger adoption play.
Why it matters
SageMaker's inline payload support reduces operational friction for async AI inference workloads, lowering the barrier to adoption for teams building production ML systems on AWS. This is a developer experience improvement that removes a required S3 upload step, making async inference more accessible for latency-tolerant applications.
The key facts
9 to knowFeature: Inline request payload support for SageMaker Async Inference
Benefit: Removes requirement to upload input data to S3 before each invocation
API affected: InvokeEndpointAsync
Use case: Asynchronous inference workflows with reduced operational overhead
Audience: AWS customers building batch/async ML inference pipelines
Amazon SageMaker AI Async Inference adds inline payload support
Customers can now send inference payloads directly in InvokeEndpointAsync API request body
Removes requirement to upload input data to S3 before each invocation
Feature announcement published June 17, 2026
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.