Elastic unveils its AI ecosystem to help enterprise developers accelerate building and deploying their Retrieval Augmented Generation (RAG) applications. The ecosystem claims to provide a curated, comprehensive set of AI technologies and tools integrated with the Elasticsearch vector database.
Steve Kearns, general manager of Search at Elastic, said: “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.”
Elastic AI Ecosystem
The Elastic AI Ecosystem is a comprehensive solution that includes pre-built Elasticsearch vector database integrations. These integrations provide seamless access to the critical components of GenAI applications, covering AI models, cloud infrastructure, MLOps frameworks, data prep and ingestion platforms, and AI security and operations.
These integrations play a crucial role in the development process, helping developers deliver more relevant experiences through RAG, prepare and ingest data from multiple sources, experiment with and evaluate AI models, leverage GenAI development frameworks, and observe and securely deploy AI applications.
The 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.
