SecureAnyCloud
Deployment guide

Langflow

Overview

Langflow is an open-source, low-code visual builder for AI agents and retrieval-augmented generation (RAG) applications. You design flows by dragging and connecting components on a canvas, test them interactively, and serve them as APIs.

This SecureAnyCloud image installs Langflow 1.9 in a Python virtual environment at /opt/langflow and fronts it with an nginx reverse proxy on TCP 80. It generates a unique administrator password and credential-encryption key on the first boot of every VM.

What you get:

  • Langflow 1.9 in a Python virtual environment at /opt/langflow
  • An nginx reverse proxy on :80 in front of the Langflow service, which is bound to loopback
  • A dedicated Azure data disk at /var/lib/langflow that holds the SQLite database, saved flows, uploads and the credential-encryption key, separate from the OS disk and re-provisioned with every VM
  • A per-VM administrator password and Fernet LANGFLOW_SECRET_KEY, generated at first boot and written to /root/langflow-credentials.txt
  • langflow.service and nginx.service as enabled, active systemd units
  • 24/7 SecureAnyCloud support

Prerequisites

You need an active Azure subscription, an SSH key pair, and a VNet with a subnet in the target region.

Standard_B2ms (2 vCPU / 8 GiB RAM) is a good starting point. Scale up for heavier flows or more concurrent users.

Configure the network security group (NSG) to allow 22/tcp inbound from your management network and 80/tcp from the clients that need the Langflow UI. For public exposure, put TLS in front of port 80 as described in Enabling HTTPS.

Step 1 - Deploy from the Azure Marketplace

Sign in to the Azure Portal, choose Create a resource, search the Marketplace for Langflow by SecureAnyCloud, and select Create.

On the Basics tab, pick your subscription, resource group, region and size (Standard_B2ms or larger). Under Administrator account, choose SSH public key and paste your key. Under Inbound port rules, allow SSH (22) and HTTP (80).

Review the dedicated data disk on the Disks tab, then select Review + create and Create.

Step 2 - Deploy from the Azure CLI

# Accept the Marketplace terms once per subscription (required before the first CLI deploy)
az vm image terms accept --urn sac1647283583153:langflow-ubuntu-24-04:default:latest
az vm create \
  --resource-group <your-rg> \
  --name langflow \
  --image sac1647283583153:langflow-ubuntu-24-04:default:latest \
  --size Standard_B2ms \
  --admin-username azureuser \
  --ssh-key-values ~/.ssh/id_ed25519.pub \
  --vnet-name <your-vnet> --subnet <your-subnet> \
  --public-ip-sku Standard
az vm open-port --resource-group <your-rg> --name langflow --port 80 --priority 1010

Step 3 - Connect to your VM

ssh azureuser@<vm-public-ip>

Step 4 - Confirm the services are running

systemctl is-active langflow.service nginx.service ss -tln | grep -E ':80 |:7860 ' curl -s http://127.0.0.1/health

Both services report active. nginx listens on port 80, the Langflow service listens on loopback 7860, and the health endpoint returns OK.

Step 5 - Retrieve your per-VM credentials

The administrator password is generated uniquely on the first boot of your VM and written to a root-only file:

sudo cat /root/langflow-credentials.txt

The username is admin, and the password is the LANGFLOW_SUPERUSER_PASSWORD value. Keep this file safe, and change the password after your first sign-in.

Step 6 - Open Langflow and sign in

Browse to http://<vm-public-ip>/ and sign in with the username admin and the password from Step 5. The Langflow workspace opens, where you can create and manage flows.

Step 7 - Build your first flow

In the UI, choose New Flow and start from a template or a blank canvas. Drag components (inputs, prompts, models, retrievers, outputs) onto the canvas and connect them to form a pipeline. Use the Playground to test the flow interactively.

Step 8 - Connect a model provider and serve your flow

Add your model-provider API key to the relevant component in the UI, for example an OpenAI or Anthropic component. To confirm that the login API works end to end, run this from the shell:

PASS=$(sudo grep '^LANGFLOW_SUPERUSER_PASSWORD=' /root/langflow-credentials.txt | cut -d= -f2-)
curl -s -X POST http://127.0.0.1/api/v1/login \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  --data-urlencode 'username=admin' --data-urlencode "password=$PASS" \
  | python3 -c "import sys,json; print('token_type:', json.load(sys.stdin)['token_type'])"

When a flow is built, select API in the UI to get the endpoint and a ready-made curl or Python snippet to call it.

Step 9 - Confirm the runtime

/opt/langflow/venv/bin/langflow --version

Enabling HTTPS

Terminate TLS at nginx with a real domain that points at the public IP address of the VM. Install certbot and request a certificate, replacing the domain with yours:

sudo apt-get update && sudo apt-get install -y certbot python3-certbot-nginx
sudo certbot --nginx -d your-domain.example.com

certbot updates the nginx configuration and sets up automatic renewal.

Backup and maintenance

All Langflow state lives on the dedicated data disk at /var/lib/langflow. This includes the SQLite database, saved flows, uploads and the encryption key. Snapshot that disk in Azure to back up your flows.

Keep the OS patched with sudo apt update && sudo apt upgrade. The Langflow service restarts cleanly with sudo systemctl restart langflow.

Support

This image is backed by 24/7 SecureAnyCloud support. Contact us by email and chat for help with deployment, flow design, model-provider integration, scaling, TLS and backups.

All product and company names are trademarks or registered trademarks of their respective holders. Their use does not imply any affiliation with or endorsement by them.

Deploy on Azure

Launch Langflow on Ubuntu 24.04 LTS on Azure with 24/7 support from SecureAnyCloud.

View this image

Need Help?

Our support team is available 24/7.

sales@secureanycloud.com

Deploy on Azure