You can now build billion-scale vector databases in under an hour on Amazon OpenSearch Service with GPU-acceleration, and auto-optimize vector indexes for optimal trade-offs between search quality, speed and cost.
Previously, large-scale vector indexes took days to build, and optimizing them required experts to spend weeks of manual tuning. The time, cost and effort weighed down innovation velocity, and customers forwent cost and performance optimizations. You can now run serverless, auto-optimize jobs to generate optimization recommendations. You simply specify search latency and recall requirements, and these jobs will evaluate index configurations (k-NN algorithms, quantization, and engine settings) automatically. Then, you can use vector GPU-acceleration to build an optimized index up to 10X faster at a quarter of the indexing cost. Serverless GPUs dynamically activate and accelerate your domain or collection, so you’re only billed when you benefit from speed boosts—all done without you managing GPU instances.
These capabilities help you scale AI applications including semantic search, recommendation engines, and agentic systems more efficiently. By simplifying and accelerating the time to build large-scale, optimized vector databases, your team will be empowered to innovate faster.
Asia Pacific (Sydney),Vector GPU-acceleration is available for vector collections and OpenSearch 3.1+ domains in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland), and Asia Pacific (Tokyo) Regions. Vector auto-optimize is available for vector collections and OpenSearch 2.17+ domains in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt) and Europe (Ireland) Regions. Learn more.
Categories: marketing:marchitecture/analytics,general:products/amazon-opensearch-service,marketing:marchitecture/artificial-intelligence,marketing:marchitecture/serverless
Source: Amazon Web Services
