Elastic Announces AI Ecosystem to Accelerate GenAI Application Development

Elasticsearch vector database integrations with industry-leading AI technology give developers best-in-class resources to expedite the deployment of RAG applications

Elastic (NYSE: ESTC), the Search AI Company, announced its AI ecosystem to help enterprise developers accelerate building and deploying their Retrieval Augmented Generation (RAG) applications. The Elastic AI Ecosystem provides developers with a curated, comprehensive set of AI technologies and tools integrated with the Elasticsearch vector database, designed to speed time-to-market, ROI delivery, and innovation.

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“The enterprise AI market is evolving at an accelerating rate, with new products and services arriving daily. While this dizzying array of options expands the portfolio of capabilities available to enterprises and their developers, it can simultaneously slow them down by increasing the number of choices and integrations that need to be made,” said Stephen O’Grady, principal analyst with RedMonk. “One way to balance the need for new capabilities with a streamlined developer experience is by thoughtfully curating and integrating tools to maximize their collective capabilities. This is what Elastic designed its AI Ecosystem to do.”

The Elastic AI Ecosystem offers developers pre-built Elasticsearch vector database integrations from a trusted network of industry-leading AI companies to deliver seamless access to the critical components of GenAI applications across AI models, cloud infrastructure, MLOps frameworks, data prep and ingestion platforms, and AI security & operations.

These integrations help developers:

• Deliver more relevant experiences through RAG
• Prepare and ingest data from multiple sources
• Experiment with and evaluate AI models
• Leverage GenAI development frameworks
• Observe and securely deploy AI applications

The Elastic AI Ecosystem includes integrations with Alibaba Cloud, Amazon Web Services (AWS), Anthropic's Claude, Cohere, Confluent, Dataiku, DataRobot, Galileo, Google Cloud, Hugging Face, LangChain, LlamaIndex, Microsoft, Mistral AI, NVIDIA, OpenAI, Protect AI, RedHat, Vectorize, and Unstructured.

“Elasticsearch is the most widely downloaded vector database in the market, and customers and developers want to use it with the ecosystem's best models, platforms, and frameworks to build compelling RAG applications,” said Steve Kearns, general manager of Search at Elastic. “With our handpicked ecosystem of technology providers, we’re making it easier for developers to leverage Elastic’s vector database and choose the best combination of leading-edge technologies for their RAG applications. These integrations will help developers test, iterate, and deliver their RAG applications to production faster and improve the accuracy of their Gen AI applications.”

For more information on the Elastic AI Ecosystem, read here.

What the Elastic AI Ecosystem is saying:

• "We’re committed to making it easy for developers to build and deploy generative AI applications,” said Stephen Orban, vice president, Migrations, ISVs, & Marketplace, Google Cloud. “Through our partnership with Elastic, enterprises and developers gain access to powerful resources, streamlined frameworks, and robust governance tools – all powered by Google Cloud’s AI-optimized infrastructure to deliver next-gen AI capabilities.”
• “Combining Hugging Face’s Inference Endpoints with Elastic’s retrieval relevance tools helps users gain better insights and improve search functionality,” said Jeff Boudier, head of product at Hugging Face. “With this integration, developers get a complete solution to leverage the best open models, hosted on Hugging Face multi-cloud GPU infrastructure, to build semantic search experiences in Elasticsearch.”
• “Our work with Elastic helps developers build GenAI applications faster and more effectively,” said Harrison Chase, co-founder and CEO of LangChain. “Leveraging LangGraph alongside Elasticsearch’s vector database, developers can create high-impact agentic applications that streamline the path from development to production.”
• “Elastic's integrations with Microsoft Azure AI solutions enable their users to use cutting-edge technology to build production-ready AI applications for their customers. This dynamic collaboration is a powerhouse of continuous innovation, driving benefits for customers, Elastic, Microsoft, and the broader partner ecosystem,” said Liliana Gonzalez, senior director, Partner Development, at Microsoft.
• “Broadening our collaboration with Elastic strengthens users’ power of choice on a reliable, consistent AI platform,” said Steven Huels, vice president and general manager, AI Engineering at Red Hat. “We’re pleased to bring new support for RAG patterns, a critical first step for enterprises beginning their AI journeys and building trust within the AI marketplace.”

Additional Resources

• Elastic AI Ecosystem Information
• Elastic AI Ecosystem Blog
• Tech Provider Integrations
• Integration How-to Resources
• Vector DB Technical Podcast
• For the latest in Gen AI learnings and resources, bookmark Elastic Search AI Labs

About Elastic

Elastic (NYSE: ESTC), the Search AI Company, enables everyone to find the answers they need in real-time using all their data, at scale. Elastic’s search, observability and security solutions are built on the Elastic Search AI Platform, the development platform used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.

Elastic and associated marks are trademarks or registered trademarks of Elastic N.V. and its subsidiaries. All other company and product names may be trademarks of their respective owners.

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