Endee.io has open sourced its Endee vector database to deliver faster, high-recall AI and semantic search on modest hardware, aiming to cut infrastructure costs and complexity for developers and enterprises.
Endee.io has open sourced Endee, a high-performance vector database engineered for modern AI workloads, positioning it as a lightweight, production-ready alternative to memory-heavy proprietary search infrastructure.
Built for semantic search, retrieval augmented generation (RAG), recommendation systems and large-scale vector search, Endee focuses on scale, speed, accuracy and cost efficiency. The company said the database delivers high recall and low latency while consuming significantly less memory, enabling deployment on modest hardware and lowering infrastructure and compute costs.
The project is fully open source, allowing developers, researchers and enterprises to inspect the codebase, deploy locally and contribute. Docker-based setup and a public GitHub repository aim to simplify evaluation and production adoption.
Endee targets common pain points seen with established vector platforms such as Pinecone, Qdrant, Milvus and Weaviate, including rising infrastructure costs, high memory consumption and scaling complexity.
Alongside the community edition, parent company Endee Labs Pvt Ltd offers a commercial enterprise version with role-based access controls, queryable encryption, compliance features, high availability, monitoring, and both serverless and on-premises deployments.
“We are extremely proud of how Endee has taken shape and the impact it is already having for teams building AI systems,” said Vineet Dwivedi, Founder of Endee.io. “Our goal with Endee was to rethink vector databases from the ground up so organizations can achieve high recall and low latency without paying the price of massive infrastructure. Open sourcing Endee allows the community to build alongside us and helps teams deploy AI systems that are faster, more secure, and far more cost efficient.”
Typical uses include enterprise search, RAG pipelines, recommendation engines, knowledge bases and AI agents.
