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Cognee is designed for flexible deployment across development and production environments, with configurable data storage backends that scale with your needs.

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Data Storage Architecture

Cognee operates on a three-tier data storage model, each optimized for specific data types and query patterns:

Graph Database

Relationships & EntitiesStores knowledge graph structure, entity relationships, and semantic connections.

Vector Database

Embeddings & SearchHandles semantic embeddings for similarity search and content retrieval.

Relational Database

Metadata & StateManages datasets, user permissions, pipeline state, and operational data.
Each storage layer can be deployed as managed services, self-hosted servers, or file-based systems (like S3 buckets), giving you complete flexibility over your infrastructure.

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Deployment Options

Choose the deployment strategy that matches your requirements:
Local & Testing
  • Docker: Containerized local deployment with embedded databases
  • MCP: Direct integration with code editors and IDEs
  • File-based: SQLite, local files, and embedded vector stores

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Storage Configuration Examples

Local Development

Embedded & File-based
Multi-Agent Limitation: Default Kuzu graph store uses file-based locking and is not suitable for concurrent access from multiple agents. Use Neo4j or FalkorDB for multi-agent deployments.
Managed Services
S3 + Managed Databases
Cognee stores all persistent data under SYSTEM_ROOT_DIRECTORY (default: .cognee_system). There is no dedicated export API; migration works by either copying the database files or switching to shared external databases.
Stop Cognee on the source instance, copy the databases/ folder to the destination, then set SYSTEM_ROOT_DIRECTORY to the new path:
File paths inside databases/:
  • cognee_graph_kuzu — Kuzu graph database
  • cognee.lancedb — LanceDB vector store
  • cognee_db — SQLite relational database
On the new instance, configure:
See Graph Stores and Vector Stores for all supported external providers.

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Quick Start Guide

1

Choose Deployment

Select your deployment method based on scale and requirements
2

Configure Storage

Set up your preferred combination of graph, vector, and relational databases
3

Deploy & Test

Launch Cognee and verify connectivity to all storage backends
4

Scale

Adjust storage and compute resources based on usage patterns

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Deployment Methods

Docker Deployment

Local & ServerStart Cognee with optional databases using compose profiles.

Modal Deployment

Serverless & Auto-scalingPerfect for variable workloads with automatic resource management.

Islo Sandbox

Ephemeral & ShareableLaunch a temporary public Cognee API for demos and short-lived evaluation.

Kubernetes (Helm)

Enterprise & ProductionContainer orchestration with full control and high availability.

EC2 Deployment

Traditional CloudStandard server deployment with custom configurations.

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Self-hosted vs Cognee Cloud

Cognee can run fully self-hosted without Cognee Cloud. The open-source package works as an embedded Python SDK, a Docker/Compose service, or a server deployment on Modal, Kubernetes, or a VM. The same core memory operations are available in both paths. cognee.serve() can point the local SDK at Cognee Cloud or at your own self-hosted API backend; it does not copy local datasets by itself. To move an already-built local graph, use cognee.push(). Self-hosted data is organized by datasets, not Cloud projects. Without Cognee Cloud, datasets live in the storage backends you configure, such as local Kuzu/LanceDB/SQLite files, a Modal persistent volume, or external Postgres, Neo4j, Qdrant, and related services.

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Architecture Benefits

Flexible Data Tiers: Each storage layer can be independently scaled, managed, or migrated without affecting others.
Cost Optimization: Use file-based storage (S3) for archival data and managed services for active workloads.
Security: Ensure proper network security and access controls across all storage tiers in production deployments.

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