topoteretes--cognee/distributed/deploy

Cognee Deployment

1-click deployment configurations for hosting Cognee as a service.

Quick Start

Platform	Best For	Command
Modal	Serverless, auto-scaling, GPU workloads	bash distributed/deploy/modal-deploy.sh
Railway	Simplest PaaS, native Postgres	railway init && railway up
Fly.io	Edge deployment, persistent volumes	bash distributed/deploy/fly-deploy.sh
Render	Simple PaaS with managed Postgres	Deploy to Render button
Daytona	Cloud sandboxes (SDK or CLI)	python distributed/deploy/daytona_sandbox.py

All platforms require setting LLM_API_KEY as a minimum.

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Modal (Serverless)

Best for bursty workloads — scales to zero when idle, auto-scales under load. No infrastructure to manage.

# Install Modal CLI
pip install modal && modal setup

# Deploy (set your API key first)
export LLM_API_KEY=sk-xxx
bash distributed/deploy/modal-deploy.sh

The script creates a Modal secret group and deploys the FastAPI server. Your endpoint URL will be shown in the Modal dashboard.

Configuration: Edit distributed/deploy/modal_app.py to adjust:

• timeout — max request duration (default: 3600s for long cognify jobs)
• container_idle_timeout — time before scaling to zero (default: 300s)
• allow_concurrent_inputs — requests per container (default: 10)

Persistent data: Uses a Modal Volume mounted at /data for file-based databases. For production, configure Postgres + PgVector instead.

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Railway

Simplest path to a hosted Cognee API. Native Postgres add-on with pgvector support.

Option A: Railway CLI

# Install Railway CLI
npm install -g @railway/cli && railway login

# From the cognee repo root:
cp distributed/deploy/railway.toml .
railway init
railway up

Option B: 1-Click Template

Use the Railway template in distributed/deploy/railway-template.json to create a "Deploy on Railway" button. The template provisions:

• Cognee API service (from Dockerfile)
• PostgreSQL with pgvector
• Auto-wired environment variables

Cost: ~$5/mo hobby tier.

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Fly.io

Edge deployment with persistent volumes. Good latency for global users.

# Install flyctl
curl -L https://fly.io/install.sh | sh && fly auth login

# Deploy
export LLM_API_KEY=sk-xxx
bash distributed/deploy/fly-deploy.sh

The script handles app creation, secrets, volume provisioning, and deployment. Your API will be at https://cognee.fly.dev.

Customization: Edit distributed/deploy/fly.toml to adjust:

• primary_region — deployment region
• vm.memory / vm.cpus — instance sizing
• auto_stop_machines — set to "off" to keep always-on

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Render

Simple PaaS with managed Postgres and persistent disks.

Deploy with Blueprint

The distributed/deploy/render.yaml blueprint provisions:

• Cognee API web service
• PostgreSQL 17 database
• 10GB persistent disk for file-based data

# Copy render.yaml to repo root and push
cp distributed/deploy/render.yaml render.yaml
git add render.yaml && git commit -m "Add Render blueprint"
git push

Then connect the repo in the Render dashboard and deploy.

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Daytona (Cloud Sandbox)

Daytona provides secure, isolated cloud sandboxes. Cognee runs inside a sandbox with persistent storage.

Option A: Python SDK

pip install daytona

export DAYTONA_API_KEY=your-key   # from https://app.daytona.io
export LLM_API_KEY=sk-xxx
python distributed/deploy/daytona_sandbox.py

Option B: CLI

brew install daytonaio/cli/daytona
daytona create
# Inside the sandbox:
pip install 'cognee[api]'
python -m uvicorn cognee.api.client:app --host 0.0.0.0 --port 8000

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Devcontainers (Codespaces / VS Code)

For contributors who want a pre-configured development environment. Uses .devcontainer/devcontainer.json at the repo root.

GitHub Codespaces

gh codespace create --repo topoteretes/cognee

VS Code Dev Containers

Open the repo in VS Code and select "Reopen in Container".

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Docker Compose (Self-Hosted)

For running on your own infrastructure, use the existing docker-compose setup:

# Minimal (SQLite + LanceDB + Ladybug - no external deps)
docker-compose up cognee

# With Postgres + pgvector
docker-compose --profile postgres up

# With Neo4j graph database
docker-compose --profile neo4j up

# Full stack with UI
docker-compose --profile ui up

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Production Recommendations

1. Use Postgres + PgVector instead of file-based databases. SQLite/LanceDB/Ladybug don't handle concurrent writes well in containerized environments.

2. Set CORS_ALLOWED_ORIGINS to your actual frontend domain instead of *.

3. Enable authentication for multi-tenant deployments: Set ENABLE_BACKEND_ACCESS_CONTROL=true (default) and configure user management. For a single-user internal deployment with auth off, set ENABLE_BACKEND_ACCESS_CONTROL=false; REQUIRE_AUTHENTICATION=false alone is not sufficient when multi-tenant mode is on.

4. Configure rate limiting: Set LLM_RATE_LIMIT_ENABLED=true to avoid hitting provider limits.

5. Trace: Enable OpenTelemetry tracing with COGNEE_TRACING_ENABLED=true and an OTLP endpoint. Install with pip install cognee[tracing].

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Environment Variables Reference

Variable	Required	Default	Description
LLM_API_KEY	Yes	—	API key for your LLM provider
LLM_MODEL	No	openai/gpt-5-mini	Model identifier
LLM_PROVIDER	No	openai	LLM provider name
DB_PROVIDER	No	sqlite	sqlite or postgres
DB_HOST	If postgres	—	Database host
DB_PORT	If postgres	5432	Database port
DB_USERNAME	If postgres	—	Database user
DB_PASSWORD	If postgres	—	Database password
DB_NAME	If postgres	—	Database name
VECTOR_DB_PROVIDER	No	lancedb	lancedb, pgvector, chromadb
GRAPH_DATABASE_PROVIDER	No	ladybug	ladybug, neo4j
CORS_ALLOWED_ORIGINS	No	*	Allowed CORS origins
ENABLE_BACKEND_ACCESS_CONTROL	No	true	Multi-tenant isolation; when true, auth is required
REQUIRE_AUTHENTICATION	No	inherits from ENABLE_BACKEND_ACCESS_CONTROL	Explicit auth override (false ignored when multi-tenant is on)