Big Tech
SageMaker Async Inference Now Supports 128 KB Inline Payloads
Dr. Adrian Vale
LLM, AI Agents & AI Infrastructure Specialist
4 min read
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LLM, AI Agents & AI Infrastructure Specialist
Amazon SageMaker Async Inference now supports inline payloads up to 128 KB via the InvokeEndpointAsync API. This enhancement eliminates the need for Amazon S3 uploads, significantly reducing latency and simplifying workflows. AWS strengthens its competitive edge in ML services, especially for industries like finance and healthcare.
Amazon SageMaker Async Inference is designed to handle asynchronous machine learning (ML) inference workloads. Unlike real-time inference, which prioritizes immediate responses, async inference queues requests for background processing, making it ideal for scenarios requiring high throughput or longer processing times. Key use cases include:
Async Inference is widely adopted for scalable and efficient ML workflows that handle diverse enterprise demands.
AWS has introduced inline payload support for SageMaker Async Inference. This enhancement allows users to send data directly in the request body via the InvokeEndpointAsync API, bypassing the need to upload input files to Amazon S3.
Body parameter in the API now supports direct data input.This update simplifies workflows for applications that utilize smaller payloads, reducing dependency on S3 storage and speeding up the process.
The inline payload capability brings multiple advantages:
Industries like e-commerce, healthcare, and banking are expected to benefit most from these improvements.
AWS’s inline payload feature gives SageMaker a competitive edge over platforms like Google Vertex AI and Microsoft Azure ML, which rely on intermediate storage for similar tasks. This advantage is particularly pronounced for:
This feature aligns with AWS’s strategy to address common pain points in ML workflows while improving efficiency.
AWS’s decision to support inline payloads could accelerate the adoption of async inference in various industries. While the 128 KB limit covers many use cases, larger payload sizes might still require S3 uploads. Future updates could include expanded payload limits, enabling broader applications such as video analysis or large-scale genomic data processing.
Competitors like Google and Microsoft may need to respond promptly to maintain relevance in the fast-evolving ML services market.
AWS’s inline payload support for SageMaker Async Inference is a meaningful step forward in simplifying ML workflows. By reducing latency and operational overhead, this feature enhances developer productivity and business efficiency. However, the 128 KB limit may restrict adoption for certain data-heavy use cases.
As AWS continues to innovate, the industry will closely watch how competitors react and how businesses leverage this feature to achieve faster, more cost-effective machine learning solutions.
The maximum payload size is 128 KB (128,000 bytes) for inline payloads in SageMaker Async Inference.
No, S3 is still required for payloads exceeding 128 KB. Inline payload support simplifies workflows for smaller payloads only.
By bypassing the need for S3 uploads, this feature significantly reduces latency for payloads under 128 KB.
💡 Dica Pro: For optimal use of inline payloads, batch your requests to stay within the 128 KB limit. This ensures low latency while avoiding the need for external storage like Amazon S3.
