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Documentation Index

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CyborgDB
The vector database
that never sees your data.
Similarity search directly on encrypted embeddings — self-hosted in your VPC or on-prem, with no decryption, no TEE exit, and no plaintext in memory.
CyborgDB is an encrypted vector database — similarity search directly on encrypted embeddings, with no decryption, no TEE exit, and no plaintext in memory. 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

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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

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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