- Introduces a novel architecture to keep confidential inference data secure through zero-trust design.
- Keeps vector embeddings end-to-end encrypted throughout their lifecycle (including at search time).
- Works with DBs such as Postgres and Redis, transforming traditional DBs into Confidential Vector DBs.
- Exposes a familiar API, making it easy to integrate with existing AI workflows.
- Provides high-performance indexing and retrieval which can be GPU-accelerated with CUDA.
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Understand the architecture and principles behind encrypted vector search
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Discover embedded libraries, managed service, and custom deployment options
Documentation Structure
Introduction
Core concepts, deployment models, and getting started guides
CyborgDB Service
CyborgDB self-hosted with REST API and client SDKs
CyborgDB Embedded
Self-hosted, embedded deployments with Python and C++ bindings
Integrations
LangChain and other framework integrations for seamless adoption
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