Amazon published a review of 13 SageMaker AI inference capabilities launched in 2026 across managed endpoints and HyperPod Inference. The launches target the hardest parts of generative AI serving: cold starts, GPU capacity, OpenAI compatibility, observability, and token-level latency. Practitioners get concrete options for both fully managed and Kubernetes-native deployment paths.
Amazon announced SageMaker HyperPod Inference Gateway, a Kubernetes-native addon that routes LLM inference requests using real-time GPU signals instead of round-robin load balancing. Benchmarks show P95 first-token latency cuts up to 98% and throughput gains up to 50% under mixed or bursty workloads. It deploys as a single EKS addon with no application or SDK changes.
Amazon announced SageMaker HyperPod Inference Gateway, a Kubernetes-native addon that routes LLM inference requests using real-time GPU signals instead of round-robin load balancing. Benchmarks show P95 first-token latency cuts up to 98% and throughput gains up to 50% under mixed or bursty workloads. It deploys as a single EKS addon with no application or SDK changes.
AWS launched PREFIX_AWARE routing on SageMaker real-time inference endpoints, sending requests with the same prompt prefix to the same instance so KV caches stay warm. Benchmarks with Llama 3.1 70B on vLLM show up to 77% lower P50 time-to-first-token and KV cache hit rates rising from ~25% to over 80%. The routing requires no model container changes and adds only 1.3–1.9 ms of routing overhead per request.
AWS launched PREFIX_AWARE routing on SageMaker real-time inference endpoints, sending requests with the same prompt prefix to the same instance so KV caches stay warm. Benchmarks with Llama 3.1 70B on vLLM show up to 77% lower P50 time-to-first-token and KV cache hit rates rising from ~25% to over 80%. The routing requires no model container changes and adds only 1.3–1.9 ms of routing overhead per request.
Amazon SageMaker AI introduced a user interface for its generative AI inference recommendations feature, which was previously available only via APIs. The UI helps developers quickly find optimal instance types, serving containers, and optimization strategies for deploying generative AI models, reducing the manual benchmarking cycle.
Amazon SageMaker AI introduced a user interface for its generative AI inference recommendations feature, which was previously available only via APIs. The UI helps developers quickly find optimal instance types, serving containers, and optimization strategies for deploying generative AI models, reducing the manual benchmarking cycle.
AWS announced a new inference meta-monitoring system for Amazon SageMaker AI endpoints, built with Amazon Quick, to continuously track prediction and data quality metrics. This governance layer sits above production ML pipelines, helping organizations detect model issues before customers complain. The post provides design and implementation guidance for predictive models.
AWS announced a new inference meta-monitoring system for Amazon SageMaker AI endpoints, built with Amazon Quick, to continuously track prediction and data quality metrics. This governance layer sits above production ML pipelines, helping organizations detect model issues before customers complain. The post provides design and implementation guidance for predictive models.
Smartsheet built a remote Model Context Protocol (MCP) server on AWS, enabling AI assistants like Amazon Quick and Claude Desktop to directly access Smartsheet's enterprise work management data. This bridges the gap between AI agents and structured data in enterprise systems.
Smartsheet built a remote Model Context Protocol (MCP) server on AWS, enabling AI assistants like Amazon Quick and Claude Desktop to directly access Smartsheet's enterprise work management data. This bridges the gap between AI agents and structured data in enterprise systems.