This guide walks you through deploying IronCloudLLM on AWS using the official AMI. The AMI is preconfigured with Docker, so the actual application runs inside containers under /opt/ironcloudllm. By following these steps, you’ll go from zero to a working, self-hosted instance of LibreChat (IronCloud’s core interface).

1. Launching the IronCloud AMI #

1. Launching the IronCloud AMI #

1. An AWS account with permissions to launch EC2 instances.
2. Familiarity with basic EC2 concepts (key pairs, security groups, IAM roles).
3. An existing VPC and subnet in the region you want to deploy.
4. An EC2 key pair (.pem file) so you can SSH into the instance.

2. Launching the IronCloud AMI #

1. Log into the AWS Management Console.
2. Navigate to EC2 → AMIs.
3. Search for the IronCloudLLM AMI (you will have been provided an AMI ID such as ami-xxxxxxxx).
4. Click Launch Instance from Image.
5. Choose an instance type. Recommended minimum:
   • t3.large (2 vCPUs, 8 GB RAM) for testing.
   • m5.xlarge or larger for production use.
6. Configure storage (default 50GB EBS is typically fine).
7. Assign a security group that allows:
   • TCP 22 (SSH) – your IP only
   • TCP 3080 – LibreChat web UI
   • TCP 5002 (optional) – TTS API if enabled
8. Attach your key pair and launch.

3. Accessing the Instance #

Once the instance is running:

ssh -i my-key.pem ec2-user@<EC2-PUBLIC-IP>

Check that Docker is installed:

docker --version

Expected output should show a recent Docker build.

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4. Directory Layout #

The AMI comes preloaded with IronCloud under /opt/ironcloudllm:

/opt/ironcloudllm
├── docker-compose.yml
├── client/
├── api/
├── rag_api/
└── .env

This setup runs multiple containers (LibreChat, RAG API, vector DB, MongoDB, etc.) all orchestrated by docker-compose.

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5. Starting IronCloudLLM #

Move into the directory and start the stack:

cd /opt/ironcloudllm
docker compose up -d

This will launch:

• LibreChat frontend on port 3080
• MongoDB for chat persistence
• VectorDB (pgvector/Meilisearch) for retrieval-augmented generation
• RAG API for indexing and embeddings

Verify containers are running:

docker ps

You should see multiple containers (names like ironcloudllm, chat-mongodb, vectordb, etc.).

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6. Accessing LibreChat #

Open a browser and navigate to:

http://<EC2-PUBLIC-IP>:3080

You’ll be greeted by the LibreChat UI. This is your self-hosted IronCloud environment.

Default username: admin@admin.com

Default password: password

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7. Configuration #

The .env file under /opt/ironcloudllm controls your deployment. Examples:

# Example: OpenAI API Key (if using external LLMs)
OPENAI_API_KEY=sk-xxxx

# Example: AWS Bedrock settings
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=yourkey
AWS_SECRET_ACCESS_KEY=yoursecret

After editing .env:

docker compose down
docker compose up -d

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8. Logs and Troubleshooting #

Check logs for any container:

docker logs ironcloudllm
docker logs rag_api

If the stack fails to start, make sure:

• No other service is using ports 3080, 5002, or 27017.
• Your .env file has valid API keys if you’re connecting to OpenAI or Bedrock.

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9. Stopping and Restarting #

To gracefully stop:

docker compose down

To restart:

docker compose up -d

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10. Next Steps #

• Secure Access: Put the instance behind an Application Load Balancer or configure HTTPS via reverse proxy (NGINX or AWS ACM).
• Persistent Storage: Ensure EBS volumes are sized appropriately and set for auto-backup.
• Scaling: For larger orgs, consider ECS/Fargate for container orchestration.

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Summary #

You’ve successfully deployed IronCloudLLM on AWS using the official AMI. The AMI boots with Docker preinstalled and a full LibreChat stack under /opt/ironcloudllm. Once launched, you can access LibreChat on port 3080, connect it to Bedrock or OpenAI, and begin using secure, self-hosted AI inside your AWS environment.