AWS News Blog

Build and optimize large-scale vector databases up to 10 times faster and at a quarter of the cost with new GPU acceleration and auto-optimization capabilities that automatically balance search quality, speed, and resource usage.

Scale vector storage and querying to new heights with S3 Vectors’ general availability—now supporting up to 1 billion vectors per index, 100ms query latencies, and expanded regional availability, while reducing costs up to 90% compared to specialized databases.

Access fully managed foundation models from leading providers like Google, Kimi AI, MiniMax AI, Mistral AI, NVIDIA, OpenAI, and Qwen, including the new Mistral Large 3 and Ministral 3 3B, 8B, and 14B models through Amazon Bedrock.

New memory-optimized instances deliver up to 5 GHz processor speeds and 3 TiB of memory—ideal for electronic design automation workloads and memory-intensive databases requiring high single-threaded performance.

New service acts as an always-on DevOps engineer, helping you respond to incidents, identify root causes, and prevent future issues through systematic analysis of incidents and operational patterns.

Simplify AI experimentation with zero-infrastructure MLflow that launches in minutes, scales automatically, and seamlessly integrates with SageMaker’s model customization and pipeline capabilities.

Access FSx for NetApp ONTAP file data through S3 to enable AI/ML workloads and analytics—letting you use enterprise file data with Bedrock, SageMaker, and analytics services while it remains in your file system.

New fast, cost-effective model supports extended thinking with adjustable reasoning depth, letting you control the balance between speed, intelligence, and cost while building AI applications for everyday workloads.