Elastic 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.
“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 maximise 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.”
