CyborgDB Docs

The vector database that keeps
your data encrypted at rest, in transit, and in use.

Similarity search directly on encrypted embeddings — self-hosted in your VPC or on-prem. AES-256-GCM with your KMS key, per-operation key derivation, keys zeroized after use.

CyborgDB is an encrypted vector database — similarity search directly on encrypted embeddings, with per-operation key derivation via your KMS and keys zeroized after use. Only the final candidates are decrypted. It runs as a proxy layer over Postgres or Redis, exposes a familiar API that slots into existing AI pipelines, and can be GPU-accelerated with NVIDIA cuVS.

<5ms

p95 encrypted query latency

100M+

vectors per index

Zero

data leaves your boundary

Choose Your Path

Learn the Fundamentals

Understand the architecture and principles behind encrypted vector search

Start Building

Get hands-on with CyborgDB in minutes using our quickstart guide

Explore Deployment Options

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

How to Use These Docs

Navigation tips and conventions used throughout these docs