CyborgDB Embedded provides native libraries for Python and C++, enabling you to integrate confidential vector search directly into your applications. This deployment model gives you maximum control, performance, and security by running entirely within your infrastructure.

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Why Choose CyborgDB Embedded?

Maximum Security
Your data never leaves your environment. Vector embeddings remain encrypted on your hardware with keys under your complete control. Ultimate Performance
Direct integration eliminates network overhead and serialization costs. Take advantage of local CPU/GPU resources and custom optimizations. Full Control
Configure every aspect of the system - from index parameters to memory management. Perfect for custom requirements and specialized deployments. Any Environment
Deploy in air-gapped networks, edge devices, or custom infrastructure where external APIs aren’t suitable.

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Available Libraries

cyborgdb-core (Enterprise)

Full-featured production library
  • Python + C++ bindings
  • All index types (IVFFlat, IVFPQ, IVF)
  • GPU acceleration with CUDA
  • Unlimited vectors and CPU threads
  • All backing stores (PostgreSQL, Redis, Memory)
  • Enterprise support

cyborgdb-lite (Evaluation)

Lightweight evaluation version
  • Python bindings only
  • IVFFlat index type
  • Up to 1M vectors, 4 CPU threads
  • PostgreSQL and Memory backends
  • Perfect for testing and prototyping
  • Community support
Free API Key Users: If you’re using a free API key, CyborgDB Service will automatically use cyborgdb-lite under the hood, which includes:
  • Up to 1M vectors maximum
  • 4 CPU threads limit
  • PostgreSQL and Memory backing stores only
  • No GPU acceleration
Upgrade to a paid plan to unlock cyborgdb-core with unlimited vectors, all backing stores, GPU acceleration, and full performance capabilities.

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Quick Start Path

Embedded Quickstart

Get running in 5 minutes with Python or C++Step-by-step guide covering both Python and C++ embedded library setup and usage.

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Architecture Overview

CyborgDB Embedded integrates directly into your application process: Key Benefits:
  • No external API dependencies
  • Sub-millisecond query latency potential
  • Complete data sovereignty
  • Custom integration possibilities

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When to Use Embedded vs Service

  • Data must stay on-premises - Regulatory or security requirements
  • Ultra-low latency needed - Sub-millisecond response requirements
  • Custom integration required - Unique workflow or system requirements
  • Cost optimization critical - High-volume usage with cost sensitivity
  • Air-gapped deployment - No external network access available
  • Single application focus - One primary application using vector search

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Next Steps

Start Building

Get hands-on with the quickstart guideFollow our comprehensive guide covering both Python and C++ setup

Browse Capabilities

Explore detailed integration examplesLearn about encrypted index operations and data management

Framework Integration

LangChain and other integrationsDrop-in replacement for existing vector stores
Ready to build confidential AI applications with complete control? Start with our Embedded Quickstart guide!