[Thank Goodness its Search] Accelerating Vector Search: GPU Indexing and Auto-Optimization in Amazon OpenSearch Service

Welcome to Thank Goodness It’s Search series—your Friday fix of OpenSearch learnings, feature drops, and real-world solutions. I will keep it short, sharp, and search-focused—so you can end your week a little more knowledge on Search than you started.

I'm excited to be back with you all after a long sabbatical, even though it's only Tuesday! I've spent the past few months deeply exploring advances in vector search technology, and I'm delighted to share some groundbreaking updates from Amazon OpenSearch Service, announced live at re:Invent 2025. These exciting new features were unveiled by AWS CEO Matt Garman during his keynote address, and I look forward to providing you with an in-depth overview of these innovations.

Introduction

Over the last few years, OpenSearch has evolved from a traditional search engine into a powerful vector-native platform, purpose-built for modern AI and semantic search workloads. What started as basic k-NN functionality has matured into a high-performance vector engine that enterprises rely on for large-scale retrieval, RAG (Retrieval Augmented Generation), and multimodal search applications.

Today, OpenSearch enables vector search at unprecedented scale - from terabytes to petabytes - supporting multi-billion vector workloads with ease.

This exponential growth brings four key challenges:

1. Indexing performance must scale to handle massive data ingestion pipelines
2. Search latency must remain consistently low even as data volumes grow exponentially
3. Cluster costs need to be optimized for efficiency and ROI
4. Search quality and relevance must meet high accuracy standards

In this re:Invent2025, Amazon OpenSearch Service Vector Engine made a significant leap forward and launched two transformative features: Auto-optimization and GPU-Accelerated Indexing. These innovations directly address critical challenges faced in vector search deployments. Whether you're starting with a proof-of-concept or scaling to production with billions of vectors, OpenSearch now offers advanced tools to streamline, simplify and accelerate your vector search deployment. Available for both serverless and provisioned deployments, these innovations mark a significant advancement in making vector search more accessible, efficient, and cost-effective.

GPU-Accelerated Indexing OpenSearch 3.3 introduces GPU acceleration powered by NVIDIA cuVS technology, enabling dramatic improvements in vector operations. By leveraging GPUs' massive parallel processing capabilities, index building is now up to 14x faster, reducing build times from days to hours while lowering costs. The feature offloads compute-intensive vector operations to GPUs, freeing CPU resources for other search tasks and delivering superior performance for large-scale workloads. This allows building billion-scale vector stores in under an hour at just a quarter of the indexing cost. AWS manages a warm pool of GPU instances with pay-per-use pricing - when enabled, the system automatically detects write volume thresholds and assigns single-tenant GPU resources as needed for index creation and updates, ensuring optimal performance without long-term commitments.

Key benefits include

• Up to 10x faster index build times for large-scale vector datasets at 1/4th the cost
• Significant cost savings with pay-per-use GPU resources
• Enhanced overall search performance by offloading vector graph creates/updates to GPUs
• Seamless integration with existing OpenSearch deployments without code changes
• Scalability to handle billions of vectors efficiently

Auto-Optimize Vector Index The Auto-Optimize feature revolutionizes how vector indices are configured and tuned. This automated service analyzes workloads from S3 (in parquet format) and provides optimal configuration recommendations based on actual usage patterns. It examines key factors like vector dimensionality, data distribution, and query patterns to determine ideal settings for parameters such as ef_construction, graph connections, and quantization methods. The optimization process involves thorough analysis of vector data and search patterns, including intelligent sampling for large datasets and comprehensive evaluation of performance metrics. The service generates detailed recommendations with estimated benefits and infrastructure sizing guidance.

Key benefits include

• Automated parameter tuning that eliminates manual experimentation
• Data-driven optimization based on workload characteristics
• Improved search performance with reduced query latency
• Cost optimization through efficient resource utilization
• Risk mitigation via pre-implementation impact analysis

Conclusion

With these 2 features, the workflow is now streamlined - use auto-optimize to determine optimal configurations aligned with your SLAs, validate the recommendations through offline experiments, then rapidly build production indices using GPU acceleration on your OpenSearch cluster. Whether you're scaling from thousands to billions of vectors or seeking to optimize existing deployments, these tools help accelerate your vector search journey from POC to production. Checkout our latest documentation and b

Next Steps To get started with GPU-Accelerated Indexing and Auto-Optimize Vector Index in Amazon OpenSearch Service, refer to the official documentation and tutorials available on the AWS website. Explore how these features can transform your vector search applications and drive innovation in your organization.

Here are some useful links to get you started:

• Amazon OpenSearch Service Documentation
• Getting Started with GPU-Accelerated Indexing
• Auto-optimize
• Amazon OpenSearch Service improves vector database performance and cost with GPU acceleration and auto-optimization
• GPU-accelerated vector search in OpenSearch: A new frontier

See you again this Friday with more search feature launched during re:Invent 2025. Until then, happy searching! 🔍