Deployment

Ahnlich consists of two services that work together:

• ahnlich-db: In-memory vector store with exact similarity search
• ahnlich-ai: AI proxy that transforms raw inputs (text, image, or audio) into embeddings

The recommended production setup runs both services using Docker.

Official Docker Images

Ahnlich provides prebuilt images on GitHub Container Registry:

• DB: ghcr.io/deven96/ahnlich-db:latest
• AI: ghcr.io/deven96/ahnlich-ai:latest

Docker Compose Setup

The easiest deployment for local or cloud use:

YAML

version: "3.8"

services:
  ahnlich_db:
    image: ghcr.io/deven96/ahnlich-db:latest
    command: >
      ahnlich-db run --host 0.0.0.0
      --enable-persistence
      --persist-location /root/.ahnlich/data/db.dat
      --persistence-interval 300
    ports:
      - "1369:1369"
    volumes:
      - ./data:/root/.ahnlich/data

  ahnlich_ai:
    image: ghcr.io/deven96/ahnlich-ai:latest
    command: >
      ahnlich-ai run --host 0.0.0.0
      --db-host ahnlich_db
      --enable-persistence
      --persist-location /root/.ahnlich/data/ai.dat
      --persistence-interval 300
    ports:
      - "1370:1370"
    volumes:
      - ./data:/root/.ahnlich/data
      - ./ahnlich_ai_model_cache:/root/.ahnlich/models

This configuration:

• Enables disk persistence (data survives restarts)
• Maps ports 1369 (DB) and 1370 (AI)
• Caches AI models across restarts

Persistence

Without persistence, all data is in-memory and lost on restart. To enable:

$--enable-persistence$--persist-location /root/.ahnlich/data/db.dat$--persistence-interval 300  # seconds

Mount the persist location to a host volume:

YAML

volumes:
  - ./data:/root/.ahnlich/data

Cloud Deployments

AWS EC2

1. Launch EC2 instance
2. Install Docker
3. Run DB:

   $docker run -d \  --name ahnlich_db \  -p 1369:1369 \  -v /data/ahnlich:/root/.ahnlich/data \  ghcr.io/deven96/ahnlich-db:latest \  ahnlich-db run --host 0.0.0.0 \    --enable-persistence \    --persist-location /root/.ahnlich/data/db.dat

4. Run AI:

   $docker run -d \  --name ahnlich_ai \  -p 1370:1370 \  --link ahnlich_db \  -v /data/ahnlich:/root/.ahnlich/data \  -v /data/models:/root/.ahnlich/models \  ghcr.io/deven96/ahnlich-ai:latest \  ahnlich-ai run --host 0.0.0.0 \    --db-host ahnlich_db \    --enable-persistence \    --persist-location /root/.ahnlich/data/ai.dat

Open ports 1369 and 1370 in your security group.

GCP Compute Engine

1. Create VM instance
2. Install Docker
3. Follow same Docker commands as AWS EC2
4. Create firewall rules for TCP ports 1369 and 1370
5. Mount a persistent disk to /data for persistence

Coolify

Coolify is a self-hosted PaaS supporting Docker images.

Steps:

1. Create new app → Docker Image
2. Set images:
   • DB: ghcr.io/deven96/ahnlich-db:latest
   • AI: ghcr.io/deven96/ahnlich-ai:latest
3. Configure run commands:
   • DB: ahnlich-db run --host 0.0.0.0 --enable-persistence --persist-location /root/.ahnlich/data/db.dat
   • AI: ahnlich-ai run --host 0.0.0.0 --db-host ahnlich_db --enable-persistence --persist-location /root/.ahnlich/data/ai.dat
4. Mount volumes:
   • /root/.ahnlich/data (persistence)
   • /root/.ahnlich/models (AI model cache)
5. Expose ports 1369 and 1370

Google Cloud Run

Cloud Run supports gRPC containers with these requirements:

• Containers listen on $PORT (use --port $PORT)
• Expose endpoints over HTTPS (port 443)
• Configure ahnlich-ai with --db-host <Cloud Run URL>

See Cloud Run gRPC Guide

Production Checklist

Item	Recommendation
Ports	Expose 1369 (DB) and 1370 (AI)
DB Connection	ahnlich-ai must use --db-host with reachable address
Persistence	Enable with --enable-persistence and bind volumes
Model Caching	Mount /root/.ahnlich/models for AI
Tracing	Optional: --enable-tracing --otel-endpoint <collector>
Security	Use TLS via proxy/load balancer for external exposure

References

• Ahnlich GitHub
• Docker Images
• Docker Compose Example
• Coolify Docs
• AWS EC2 Docker
• Cloud Run gRPC