On OpenSearch 3.0 GA. Q&A with Carl Meadows
Q1. What is the OpenSearch Project about?
OpenSearch is the trusted open source platform for AI-powered search, observability and analytics with built-in security, high performance, and a flexible architecture for modern data-driven applications. The platform offers developers and data practitioners a scalable, high-performance solution for search across massive datasets, log analytics, real-time monitoring, and more – all without vendor lock-in.
Since becoming an independent project under the governance of the OpenSearch Software Foundation, OpenSearch has continued to expand its contributor base to enable community-driven innovation.
Q2. How is enterprise search related to the development of AI applications?
Search is the backbone of many AI systems. OpenSearch brings together traditional search, analytics, and vector search in one complete package to accelerate AI application development by reducing the effort for builders to operationalize, manage, and integrate AI-generated assets.
Whether you’re retrieving documents for a generative AI pipeline, grounding responses in trusted data or identifying patterns across billions of records, search is what connects input to insight. Pairing traditional keyword search with semantic and vector search delivers fast, relevant and explainable results at scale. That’s why we’re seeing enterprise search evolve rapidly alongside AI. It’s no longer just about retrieving documents – it’s about powering intelligent applications.
Q3. How does OpenSearch relate to Amazon OpenSearch Service?
OpenSearch is an open source project. With the launch of the OpenSearch Software Foundation in 2024, AWS transitioned OpenSearch under the Linux Foundation, enabling neutral governance and long-term sustainability. Being part of the Linux Foundation ensures that OpenSearch stays open, transparent and community-led.
Amazon OpenSearch Service is an AWS-managed service that runs on top of the project software and removes the need to manage, monitor or maintain infrastructure. It is one of many OpenSearch adopters and contributors. AWS is a major supporter of the OpenSearch Software Foundation, alongside other leading companies like SAP, Uber and NetApp.
Q4. You have recently announced the general availability of OpenSearch 3.0. What are the key features of this new release?
OpenSearch 3.0 is our biggest release yet, both in terms of technical advancement and community momentum. It delivers upgrades in performance, data management, vector database functionality, and more to help users build and deploy powerful, flexible solutions for search, analytics, observability and other use cases. OpenSearch 3.0 introduces experimental GPU acceleration for vector workloads, which boosts performance for AI search use cases. It also adds pull-based ingestion to enhance ingestion efficiency, experimental support for gRPC for faster data transport and data processing, and more.
Q5. Forrester emphasizes that “traditional databases are no longer able to meet the growing demands of generative AI due to limitations in supporting modern vector multidimensional data and performing similarity searches.” How does OpenSearch 3.0 address this challenge?
That’s exactly what OpenSearch 3.0 was built to solve. Traditional search engines weren’t designed for the scale or complexity of vector workloads, but OpenSearch 3.0 is. We support high-dimensional vectors, hybrid search, and similarity scoring, and with GPU support, we’ve dramatically reduced latency and increased throughput. Whether you’re running retrieval-augmented generation (RAG) pipelines or embedding-based search, 3.0 gives you a purpose-built, open platform to support those needs at scale.
Q6. Do you have some benchmark to illustrate how OpenSearch 3.0 increases efficiency and performance? What kind of performance do you achieve for large-scale vector workloads?
Yes, this major release delivers a 9.5x performance improvement over OpenSearch 1.3. The performance increase of 9.5x reflects benchmarking on our Big5 workload, which tests full-text search performance, aggregation performance, complex Boolean queries, sorting and pagination, and indexing performance for various data types.
Beyond these improvements to core performance, we have other major changes including GPU Acceleration for OpenSearch Vector Engine. By enabling GPU deployment, leveraging NVIDIA cuVS, OpenSearch 3.0 reduces index-building time for data-intensive workloads. Additionally, reader and writer separation increases efficiency and performance for large workloads by allowing readers and writers to scale independently.
Q7. OpenSearch introduced GPU-based acceleration. What is it? and what are the benefits?
GPU-based acceleration in OpenSearch leverages NVIDIA’s cuVS technology to offload compute-intensive vector indexing tasks to GPUs. This approach dramatically reduces indexing times and operational costs, enabling the efficient handling of large-scale vector datasets essential for AI applications. The decoupled architecture also allows for greater flexibility and scalability in deployment.
Q8. In your announcement it is mentioned that OpenSearch 3.0 provides major advancements in how the platform ingests, transports and manages data. Can you clarify this?
We overhauled our data pipeline to support faster, more reliable ingest and replication.
This includes pull-based ingestion for streaming data, with support for Apache Kafka and Amazon Kinesis sources. With pull-based ingestion, users can fetch data and index it rather than pushing data into OpenSearch from REST APIs, which can drive up to 40% improved throughput and enable more efficient use of compute.
It also includes gRPC and Protobuf support to deliver higher-performance data transport, derived source for vectors that unlocks a 3x reduction in storage costs, and enhanced scaling and resource isolation with separate reads and writes for remote store.
For users, this means faster index build times, more resilient data movement, and better performance in high-throughput scenarios, whether you’re indexing logs, telemetry or vector data.
Q9. Is OpenSearch 3.0 now available?
Yes, OpenSearch 3.0 is generally available and production-ready. You can download it from opensearch.org or access it through cloud providers and community distributions. And as always, it’s open source under the Apache 2.0 license.
Q10. What type of AI applications will typically benefit from the use of OpenSearch 3.0?
Any application that relies on search, recommendations, or retrieval will benefit from OpenSearch 3.0, especially those using embeddings or LLMs. This includes RAG pipelines, document classification, similarity search, customer support automation, anomaly detection and more. If your application depends on fast, accurate retrieval across massive datasets, OpenSearch 3.0 is built for that.
Q11. Anything else you wish to add?
We’re building OpenSearch in the open, for the long term. What excites me most isn’t just the performance improvements – it’s the momentum from the community. Whether you’re an AI startup, a Fortune 100 company, or an independent contributor, we welcome you to be part of shaping what’s next. OpenSearch 3.0 is a big step, but it’s only the beginning.
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Carl Meadows, Governing Board Chair at the OpenSearch Software Foundation and Director of Product Management at Amazon Web Services (AWS)
Sponsored by AWS