OpenSearch Vector Engine - OpenSearch

OpenSearch Vector Engine - OpenSearch
Close Search
OpenSearch Vector Engine
Overview
Key features
Use cases
Get started
Integrations
An open-source, all-in-one vector database for building scalable and future-proof AI apps
To power artificial intelligence (AI) apps at scale, you need a database specially designed for the way AI understands data. OpenSearch Vector Engine is designed for accuracy, speed, and scalability, enabling you to build stable AI applications on a proven platform that scales to tens of billions of vectors, with low latency and high availability built-in. Unlike traditional databases that struggle with unstructured information, vector databases are designed for high-dimensional data—delivering lightning-fast results for chatbots, recommendations, image search, and other AI use cases.
Play Video
Play Video
YouTube: Tens of billions of vectors: scale your AI applications with OpenSearch Vector Engine
Read the getting started guide
Vector Engine documentation
OpenSearch Vector Engine for vector data
OpenSearch Vector Engine brings together the power of traditional search, analytics, and vector search in one complete package. With OpenSearch vector databases, organizations can accelerate AI development by reducing the effort for builders to operationalize, manage, and integrate AI-generated assets.
Bring your models, vectors, and metadata into OpenSearch to power vector, lexical, and hybrid search and analytics—with performance and scalability built in.
What is a vector database?
Information exists in various forms, from unstructured data like text documents, rich media, and audio to structured data like geospatial coordinates, tables, and graphs. AI advances have made it possible to encode all these data types into vectors using models or embeddings. These vectors represent data points in a high-dimensional space, capturing the meaning and context of each asset. By mapping relationships between data points, search tools can efficiently identify similar assets based on their proximity in this space.
Vector Embeddings
Reduce search costs, improve results
Vector databases enable fast, low-latency similarity searches by storing and indexing vectors alongside metadata. With OpenSearch’s k-nearest neighbors (k-NN) functionality—powered by advanced indexing algorithms like Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) System—you can efficiently run high-performance queries on vectorized data.
Beyond search, vector databases enhance k-NN with robust data management, fault tolerance, resource access controls, and powerful query engines, providing a strong foundation for AI-driven applications.
Vector Search vs. Traditional Search
Multimodal Vector Embeddings for Search
Trusted in production
Power AI applications on a mature search and analytics engine trusted in production by tens of thousands of users.
Proven at scale
Build stable applications with a data platform proven to scale to tens of billions of vectors, with low latency and high availability.
Open and flexible
Choose open-source tools and leverage integrations with popular open frameworks, with the option of using managed services from major cloud providers.
Build for the future
Future-proof your AI applications with vector, lexical, and hybrid search,
analytics, and observability—all in one suite.
OpenSearch vector functionality from query to result.
Key features
k-NN search
Use low-latency queries to discover assets by degree of similarity through k-nearest neighbors (k-NN) functionality.
Vector quantization support
Improve performance and cost by reducing your index size and query latency with minimal impact on recall.
Intelligent filtering
Apply intelligent strategies to optimize recall and latency for vector search.
Open and extensible
Build reliable, scalable solutions that operationalize embeddings and incorporate vector search functionality with an integrated Apache 2.0-licensed vector database.
Semantic search
Improve accuracy and relevance for human language queries through searches that consider context and relationships.
Hybrid search
Combine keyword-based search with semantic, dense vector search to improve search relevance, tune search relevance by combining and normalizing query relevance scores.
Built-in anomaly detection
Automatically detect unusual behavior in your data in near real time using the Random Cut Forest (RCF) algorithm.
Memory-optimized search
Optimize Faiss performance by memory-mapping indexes and using the OS cache, eliminating full memory loads and reducing I/O.
Production-ready GPU acceleration
Accelerate vector index builds in OpenSearch with GPU support, dramatically slashing build times and costs for high-scale search workloads.
Use cases
OpenSearch Vector Engine’s vector database supports a range of applications. The following are just a few examples of solutions you can build.
Visual search
Create applications that allow users to take a photograph and search for similar images without having to manually tag images.
Semantic search
Enhance search relevancy by powering vector search with text embedding models that capture semantic meaning and use hybrid scoring to blend term frequency models (Okapi BM25) for improved results. To learn more, see
Semantic search
Multimodal search
Use state-of-the-art models that can fuse and encode text, image, and audio inputs to generate more accurate digital fingerprints of rich media and enable more relevant search and insights. To learn more, see
Multimodal search
Generative AI agents
Build intelligent agents powered by generative AI while minimizing hallucinations. Use OpenSearch to enhance retrieval-augmented generation (RAG) workflows with large language models (LLMs) for more accurate and context-aware responses. To learn more, see
Generative AI agents
Personalization
Recommendation engine
Generate product and user embeddings using collaborative filtering techniques and use OpenSearch to power your recommendation engine. Enhance customer experiences by providing relevant repeat purchase, try new, and cold start recommendations.
User-level content targeting
Personalize web pages by using OpenSearch to retrieve content ranked by user propensities using embeddings trained on user interactions.
Related products
Deliver personalized shopping experiences by suggesting similar products based on a specific item, and guide customers to relevant products based on their browsing history or past purchases, powering “You might like this because you bought/viewed/clicked on this” recommendations. OpenSearch Vector Engine generates rich item embeddings optimized for predicting which products are likely to be purchased together, enhancing engagement and conversion rates.
Personalized product search
Improve product ranking by combining traditional text-based search algorithms like Okapi BM25 with vector search capabilities. OpenSearch Vector Engine draws on historical business data to optimize search rankings, going beyond simple language similarities to deliver highly personalized results, boosting relevance and increasing the likelihood of conversion.
Sales, marketing and finance
Churn prediction
Optimize growth by predicting customer churn, identifying users at risk of unsubscribing or stopping purchases. Take proactive steps, such as personalized notifications, discounts, or special offers, to re-engage and retain these users. With OpenSearch Vector Engine, you can build powerful models to effectively predict and prevent churn, driving long-term customer loyalty.
Customer LTV prediction
Predict customer lifetime value (LTV) to optimize marketing, strengthen customer relationships, and maximize profitability. Forecast future revenue, refine strategies and allocate resources more efficiently, focusing on high-value customers. With OpenSearch Vector Engine, you can build accurate models to predict LTV and drive smarter decision-making.
Email personalization
Increase email engagement with personalized content that drives active user counts and order rates. OpenSearch Vector Engine enables fast deployment of advanced recommendation models, enhancing email campaigns with tailored suggestions that deliver measurable business results.
Industrial and manufacturing
Predictive maintenance
Analyze time-series and sensor data to predict when equipment needs repair or servicing, preventing costly downtime and repairs. By storing sensor data as vector data and using ML algorithms, OpenSearch Vector Engine helps analyze equipment data to predict potential failures before they happen, enabling timely
maintenance and avoiding expensive breakdowns.
Quality control
Surface defects in real-time during the manufacturing process to ensure product quality and reduce waste. OpenSearch Vector Databases’ built-in anomaly detection capabilities help identify issues as they occur, enabling immediate action to maintain high standards and improve efficiency.
Fraud detection
Tackle a wide range of fraud and abuse challenges, from money laundering and credit card fraud to insurance and return fraud. OpenSearch Vector Engine’s built-in anomaly detection capabilities help organizations identify suspicious activity with precision and efficiency, enhancing security and reducing risk.
Getting started
You can get started with OpenSearch Vector Engine by viewing our getting started guide to the right, or by exploring our
vector search documentation
. To learn more or to start a discussion, join our
public Slack channel
, check out our
user forum
, and follow our blog for the latest on OpenSearch tools and features.
Getting started guide
Integrations
OpenSearch Vector Engine integrates with leading LLMs, ML frameworks, and vector search engines, enabling the development of cutting-edge search and AI applications.
Most recent OpenSearch blog posts
April 21, 2026
in
Blog
Benchmarking multimodal document search in OpenSearch: Three approaches compared
Documents with tables, charts, and diagrams need more than text-based search. We benchmarked three multimodal document search approaches in OpenSearch on 1,000 report pages. ColPali late interaction reranking achieved the…
April 17, 2026
in
Blog
OpenSearchCon Europe closes: What the Foundation is building next
Day two of OpenSearchCon Europe in Prague ended the way the best conferences do: with something the community has been waiting for.
April 16, 2026
in
Blog
OpenSearchCon Europe is underway: Here is what you missed this morning
What a start to OpenSearchCon Europe in Prague. This morning's keynotes set the tone for what promises to be a great two days, and a few themes emerged that speak…
April 16, 2026
in
Case Studies
From Midnight Alerts to Autonomous Recovery: How Noventiq Helped Build Self-Healing Infrastructure
A Noventiq-led initiative demonstrates how combining agentic AI with vector search transforms infrastructure from reactive monitoring to autonomous self-healing systems, reducing MTTR and operational burden while maintaining human control.
April 15, 2026
in
Announcements
Blog
CERN joins the OpenSearch Software Foundation as an associate member
The OpenSearch Software Foundation is pleased to announce that CERN has joined as an associate member. The intergovernmental organization operates the world's largest particle accelerator and is responsible for landmark…
April 15, 2026
in
Announcements
OpenSearch Software Foundation strengthens enterprise readiness with new long-term support versions
New program provides defined 18-month support lifecycles, SBOM-backed security compliance, and accredited vendor support Summary At OpenSearchCon Europe, the OpenSearch Software Foundation announced a long-term support (LTS) program to deliver…
Subscribe for updates, event info, and the latest community news
OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
github
slack
mastodon
bluesky
x-twitter
Participate
Code of Conduct
Forum
Project Repo
Community Repo
Meetup
Connect
Providers
Become a Provider
Find a Provider
Resources
About
FAQ
Release Schedule
Maintenance Policy
Documentation
Getting Started
Testimonials
Trademark & Brand
© Copyright The Linux Foundation. The OpenSearch Project is a project of The Linux Foundation. For web site terms of use, trademark policy and other policies applicable to The OpenSearch Project please see
Linux Foundation Policies
. The OpenSearch Project supports the OpenSearch open source project, which has been established as OpenSearch Project a Series of LF Projects, LLC. For policies applicable to the OpenSearch Project a Series of LF Projects, LLC, please see
LF Projects, LLC Policies
and
the OpenSearch Trademark Policy
Close Menu
About
Foundation
Governing Board
Charter
Meeting Calendar
Long-Term Support
Platform
Platform
OpenSearch Core
OpenSearch is a powerful search and analytics engine built on Apache Lucene.
OpenSearch Dashboards
Our data visualization toolset is a flexible, fully integrated solution for visually exploring and querying your data.
OpenSearch Data Prepper
A server-side data collector designed to enrich, transform, and aggregate data for downstream analytics with Opensearch.
OpenSearch Vector Engine
Power machine learning and generative AI applications with a high-performance database designed for the way AI understands data.
Capabilities
Machine learning and AI
Vector Search
Anomaly Detection
E-Commerce
Enterprise Search
Document Search
Search Relevance
Observability
Observability Stack
Performance Monitoring
Log Analytics
Security Analytics
Threat Intelligence
Event Correlation
Performance Benchmarks
View key performance metrics across different workloads
View Data
Roadmap
Image
OpenSearch Project Roadmap
Explore our official project roadmap on GitHub
Community
OpenSearch Community
Forum
Find answers to your questions, help others in the community, and join the conversation.
Events
Community Meetings, Development Backlog & Triage, in-person, and virtual events.
Ambassadors
Ambassadors Community leaders who share knowledge and grow OpenSearch.
Solutions Providers
Find open-source providers offering solutions and services.
Projects
Highlights of projects built by the community.
User Groups
Join the OpenSearch Project Meetup Network
Community Resources
Slack
Speak with other developers.
GitHub Project Organization
Join us for in-person and virtual events to learn the latest about the project.
OpenSearchCon
Image
OpenSearchCon Europe
16-17 April 2026 | Prague, Czechia
OpenSearchCon India
15-16 June 2026 | Mumbai, India
OpenSearchCon North America
September 22-24, 2026 | San Jose, CA
Documentation
Documentation Library
OpenSearch and Dashboards
Build your OpenSearch solution using core tooling and visualizations.
OpenSearch Data Prepper
Filter, mutate, and sample your data for ingestion into OpenSearch.
Migration Assistant
Begin your transition to OpenSearch with our migration toolkit
Documentation Library II
OpenSearch Benchmark
Measure performance metrics for your OpenSearch cluster.
Clients
Interact with OpenSearch from your application using language APIs.
Blog & News
Analyst Reports
Announcements
Authors
Blog
Case Studies
News
The Search Party
Downloads