OpenSearch Project Blog

OpenSearch 3.3 introduces batch processing for remote semantic highlighting models, reducing ML inference calls from N to 1 per search query. Our benchmarks demonstrate 2-14× performance improvements depending on document length and result set size.

Neural sparse search combines the semantic understanding of neural models with the efficiency of sparse vector representations. This technique has proven effective for semantic retrieval while maintaining the advantages of...

Storage is a key factor driving the infrastructure cost of your OpenSearch cluster. As your data grows, storage requirements can increase multifold, depending on whether OpenSearch stores documents in multiple...

As we mark the first anniversary of the OpenSearch Software Foundation’s Technical Steering Committee (TSC), it’s important to celebrate the milestones we’ve achieved and acknowledge the dedicated individuals who helped...

What does it feel like to attend OpenSearchCon for the very first time?  When I walked in, I was nervous. I’ve been working in infrastructure and observability, but this was...

In an era of explosive data growth, organizations are facing a critical challenge: how to manage, analyze, and derive value from their logs without breaking the bank. Traditional proprietary solutions like Splunk have become increasingly expensive, leaving teams searching for a more sustainable approach to observability.

Search functionality is the cornerstone of any successful e-commerce platform. It’s the point of connection between customers and your product catalog. Your store’s search features directly influence the user experience,...

We’re thrilled to announce the launch of OpenSearch AI demos in Hugging Face, a set of cutting-edge demo applications that put the power of advanced AI search capabilities right at your fingertips. Whether...

Enhancing OpenSearch neural sparse models with GTE and LLM teachers.

Learn how the OpenSearch Writing Assistant helps contributors draft clear, accurate blog posts faster, with practical tips and real before-and-after examples.

How to start contributing to OpenSearch: A beginner’s guide based on my journey OpenSearch is a powerful open-source search and analytics engine that empowers developers and organizations to build scalable,...

From observing change to measuring search quality In the first post of this three-part series, we focused on how to measure change when switching from one search configuration to another....

Shaping the future of open source observability with the OpenSearch Observability TAG OpenSearch is helping developers gain the visibility they need into their applications and infrastructure, making it easier to...

Learn how a hybrid approach that takes the best from Lucene and Faiss achieves better performance.

The OpenSearch Project wouldn’t be where it is today without the developers, maintainers, and advocates who contribute their time, ideas, and energy to help it grow. That’s why we’re excited...

OpenSearch is an open source search and analytics platform that powers website search, log analytics, and application monitoring. It brings together the OpenSearch core engine, OpenSearch Dashboards for visualization, and...

Discover how Amazon EC2 team leveraged OpenSearch MCP server and Amazon Q to revolutionize droplet quality investigations, enabling efficient multi-cluster data analysis and reducing troubleshooting time from hours to minutes

How DataStax and the OpenSearch community achieved near-linear scalability for vector index construction through lock-free concurrent graph building Vector search has become the backbone of modern AI applications, from semantic...

Learn to deploy OpenSearch MCP server on Bedrock AgentCore Runtime to securely connect AI agents with OpenSearch clusters using CloudFormation or CLI methods.