Vector retrieval for AI applications
Stores and searches vectors for production AI workloads, with positioning around RAG, semantic search, agents, and recommendations.
Endee
Endee is an open source vector database for production AI systems, with managed cloud, enterprise, and edge deployment options. It helps teams build RAG, semantic search, agent retrieval, and recommendation workflows with APIs, SDKs, and memory-efficient search.
Overview
Endee is an open source vector database built for production AI systems. It focuses on fast vector search, lower memory usage, and retrieval workflows used in RAG, semantic search, AI agents, and recommendations.
The product is offered as Endee Managed Cloud with Starter, Pro, and Scale pricing tiers, plus an Enterprise edition and an edge-device deployment option. The site also describes a RESTful API, SDKs for Python, TypeScript, Go, and Java, and deployment targets that include on-prem infrastructure and devices such as Raspberry Pi, Android, and NVIDIA Jetson.
Core capabilities
Memory-efficient search architecture
Uses a Vector Graph Engine with layered memory design, processor acceleration, and intelligent quantization to reduce memory use while keeping search fast.
Hybrid and keyword search
Supports hybrid search on the managed cloud plans, combining vector and keyword retrieval and adding BM25 on Pro and Scale tiers.
Developer-facing API and SDKs
Exposes a RESTful API and SDKs for Python, TypeScript, Go, and Java, which gives developers several ways to connect applications.
Operational controls on paid tiers
Offers enterprise controls such as RBAC, audit logs, manual or automated backups, and support tiers that increase with the plan.
Edge-device deployment
Provides an edge deployment path for Raspberry Pi, Android, and NVIDIA Jetson, with native ARM binaries and offline on-device search.
Where Endee fits
Production RAG and AI agents
Use Endee to power retrieval for AI systems that need relevant context for generation, search, or multi-step reasoning.
Semantic search across content
Use hybrid search and semantic retrieval to find documents, products, or content by meaning instead of only matching keywords.
Recommendations and personalization
Use vector similarity and item embeddings to surface personalized results and related items in recommendation flows.
Enterprise-controlled deployments
Use the enterprise edition when you need on-prem deployment, encryption-oriented search, backups, RBAC, and audit logs.
Offline and edge-device search
Use the edge offering when search must run locally on Raspberry Pi, Android, or NVIDIA Jetson without cloud round-trips.
Pros and Cons
Pros
- Open source under an Apache 2.0 license, with GitHub access and a permissive contribution model.
- Offers multiple deployment paths: managed cloud, enterprise on-prem, and edge devices.
- Includes developer APIs and SDKs for several common languages.
- Managed plans include practical operational features such as hybrid search, backups, RBAC, and audit logs.
- Public benchmark pages show a focus on recall, latency, throughput, and memory efficiency.
Cons
- Public pages do not provide a full integrations catalog or exhaustive platform support matrix.
- Some advanced capabilities are split across Managed Cloud, Enterprise, and edge offerings, so buyers need to verify which edition matches their deployment needs.
FAQ
What is Endee used for?
Endee is a vector database for production AI systems. The site positions it for retrieval-heavy workloads such as RAG, semantic search, AI agents, and recommendations.
Does Endee offer different deployment options?
The pricing page shows a free Starter tier, a $99/month Pro tier, and a $999/month Scale tier in Endee Managed Cloud. Endee also offers an Enterprise edition and an edge-device deployment option.
How do teams integrate Endee into applications?
The site says Endee exposes a RESTful API and SDKs for Python, TypeScript, Go, and Java, and it also offers docs and GitHub access for open-source use.
What changes between the available plans?
The pricing page shows limits for queries, indexes, vectors, and support vary by plan. The enterprise page adds features such as on-prem deployment, Queryable Encryption, continuous backups, and RBAC.
Are all integrations and platform details documented publicly?
Endee’s public pages do not list every integration partner or ecosystem support option, so buyers should confirm compatibility with their stack before adopting it.
Quick Facts
- Category
- Vector database
- Source domain
- endee.io
- License
- Apache 2.0
- Deployment options
- Managed cloud, enterprise/on-prem, and edge devices
- APIs / SDKs
- RESTful API; Python, TypeScript, Go, Java
- Pricing
- Free Starter tier; Pro at $99/month; Scale at $999/month
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