Mage AI

Sign in to the AWS Management Console, open the EC2 service and select Launch instance. Under Application and OS Images, choose AWS Marketplace AMIs and search for Mage AI. Select the SecureAnyCloud listing, choose Select, then choose Continue on the subscription summary.

Pick an instance type of t3.medium or larger. Choose your EC2 key pair under Key pair (login). Under Network settings, select your VPC and subnet. Create or select a security group that opens port 22 from your management network and port 80 for the pipeline editor and the REST API. Leave the root volume at the default size or larger.

Select Launch instance. First-boot initialisation generates the owner password shortly after the instance state becomes Running and the status checks pass. The Python application takes a minute or two to finish its first start, so allow a little time before the editor answers.

This command launches an instance from the SecureAnyCloud Mage AI Marketplace AMI into an existing subnet and security group. Replace <ami-id> with the AMI ID shown on the Marketplace listing and <key-name> with your EC2 key pair name. Replace <subnet-id> with your subnet ID and <security-group-id> with a security group that opens ports 22 and 80.

aws ec2 run-instances \
  --image-id <ami-id> \
  --instance-type t3.medium \
  --key-name <key-name> \
  --subnet-id <subnet-id> \
  --security-group-ids <security-group-id> \
  --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=mage-ai}]'

When the instance reaches the Running state and its status checks pass, note its public IP address or DNS name from the EC2 console or with aws ec2 describe-instances.

Connect over SSH with your key pair and the login user for your operating system variant.

OS variant SSH login user

Ubuntu 24.04 ubuntu

ssh -i <key-name>.pem ubuntu@<public-ip>

Your instance generated a unique Mage owner password on first boot. Read it as root:

sudo cat /root/mage-ai-aws-credentials.txt

The file lists the editor URL, the owner email (admin@sac.local), the owner username (admin), the generated password and a REST API example. Keep this password somewhere safe.

nginx serves the Mage pipeline editor on port 80. In a browser, go to:

http://<instance-public-ip>/

On the sign-in page, enter the email admin@sac.local and the password from the credentials file. You land on the pipelines dashboard, which lists the pipelines in the project with their type and status.

Open a pipeline to enter the notebook-style editor. The block tree is on the left, and each block's code and configuration is in the centre. Blocks are small pieces of Python, SQL or R wired together into a directed graph. You can run a block, preview its output, and add data loader, transformer and data exporter blocks from the palette.

The pipeline runs view lists every executed run with its trigger, status and timing, drawn from the built-in scheduler and run history. Open a run to inspect its block-level logs.

Over SSH, confirm that the application and the nginx proxy are active and that the ports are listening:

sudo systemctl is-active mage nginx

sudo ss -tlnp | grep -E ':(80|6789) '

You should see both services reported as active, the application listening on 127.0.0.1:6789 (loopback only), and nginx listening on port 80. You can also check the application's health endpoint over loopback:

curl -s -o /dev/null -w '%{http_code}\n' http://127.0.0.1:6789/api/status

This returns 200 when Mage is ready.

The Mage REST API is served on the same port 80 behind the same login. First create a session to obtain a token. From a remote client, POST your owner email and password to /api/sessions. The public API key shown is the standard Mage client key and is not a secret. The request looks like this:

curl -s -X POST http://<instance-public-ip>/api/sessions \
  -H 'Content-Type: application/json' \
  -H 'X-API-KEY: zkWlN0PkIKSN0C11CfUHUj84OT5XOJ6tDZ6bDRO2' \
  --data '{"session":{"email":"admin@sac.local","password":"<password>"}}'

The response contains a session.token, which is a JSON Web Token. Mage's bearer authentication expects the OAuth access token carried inside that JWT, not the JWT string itself. Decode the token claim from the JWT payload and use it as the bearer.

This one-liner signs in, extracts the access token and lists the pipelines, all over loopback on the instance:

ACCESS_TOKEN=$(curl -s -X POST http://127.0.0.1/api/sessions \
  -H 'Content-Type: application/json' \
  -H 'X-API-KEY: zkWlN0PkIKSN0C11CfUHUj84OT5XOJ6tDZ6bDRO2' \
  --data "{\"session\":{\"email\":\"admin@sac.local\",\"password\":\"$(sudo grep '^mage.owner.pass=' /root/mage-ai-aws-credentials.txt | cut -d= -f2-)\"}}" \
  | python3 -c 'import sys,json,base64; jwt=json.load(sys.stdin)["session"]["token"]; p=jwt.split(".")[1]; p+="="*(-len(p)%4); print(json.loads(base64.urlsafe_b64decode(p))["token"])')
curl -s http://127.0.0.1/api/pipelines \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H 'X-API-KEY: zkWlN0PkIKSN0C11CfUHUj84OT5XOJ6tDZ6bDRO2'

From a remote client, substitute http://<instance-public-ip> for http://127.0.0.1 and pass the owner password in place of the sudo grep lookup.

In the editor, create a new pipeline and add blocks from the palette. A typical batch pipeline has a data loader block that reads from a database, warehouse, file or API. It then has one or more transformer blocks written in Python or SQL, and a data exporter block that writes the result to your destination. Run each block to preview its output as you build.

When the pipeline is ready, create a trigger to schedule it. Mage supports schedule triggers on a cron interval, event triggers and API triggers. The scheduler runs active triggers automatically and records every run in the run history with its logs and status.

Database and warehouse connections are configured in the project's io_config.yaml, which lives in the project directory on the data volume. Secrets such as passwords are encrypted with a per-instance key held in the .mage_data directory. The key is generated on first boot and never shipped in the image.

The Mage project directory and the SQLite metadata and user database live on a dedicated EBS volume mounted at /var/lib/mage. This keeps your pipelines and owner account off the operating system disk and lets you resize or snapshot them independently. Confirm the mount with:

df -h /var/lib/mage

To grow the project store, expand the EBS volume in the AWS console. Then grow the filesystem on the instance with sudo resize2fs on the underlying device. The Mage project is at /var/lib/mage/sac and the metadata database is the SQLite file /var/lib/mage/mage-metadata.db.

nginx serves the pipeline editor and REST API over plain HTTP on port 80. For production use, place them behind TLS.

Obtain a certificate for your domain, for example a managed certificate on an Application Load Balancer in front of the instance, or Certbot installed on the instance. Then configure nginx to listen on 443 with your certificate and proxy to 127.0.0.1:6789, exactly as the bundled site does for port 80. Keep the WebSocket upgrade headers in place so the live editor and terminal work. Restrict the security group so ports 80 and 443 are reachable only from the networks that build and run pipelines.

Back up your work by snapshotting the /var/lib/mage EBS volume. The snapshot captures the project directory, the pipelines and the metadata database in one consistent image.

Apply operating system security updates with sudo apt-get update && sudo apt-get upgrade, and reboot when a new kernel is installed. Mage and nginx start automatically on boot.

To manage users, sign in as the owner and open the workspace users settings. There you can add users and assign roles.