Flowise with Postgres

Why deploy flowise with postgres on Render?

A Render deployment template for running Flowise with managed PostgreSQL as the database backend. It solves the problem of self-hosting a visual LLM workflow builder with production-ready infrastructure—persistent state in Postgres, auto-generated secrets, and a path to horizontal scaling without managing database connections manually.

This template wires Flowise directly to managed Postgres with all five database environment variables pre-configured through Render's service references—no connection strings to copy or credential juggling. Cryptographic secrets for JWT, sessions, and encryption are auto-generated and persisted by Render, so your security-critical config survives redeploys without manual secret management. You get daily Postgres backups automatically, and once you move uploads to S3, you can drop the disk and scale to multiple instances with a single render.yaml change.

Architecture

What you can build

You'll have a running Flowise instance where you can visually build and deploy LLM agents and workflows, with your data stored in managed Postgres that backs up daily. The setup handles all the secrets and database wiring automatically, so you can start creating chatflows immediately and scale to multiple instances later without re-architecting.

Key features

• Auto-wired Postgres connection: Database credentials flow automatically via fromDatabase references in render.yaml, eliminating manual connection string configuration.
• Auto-generated cryptographic secrets: All secrets including JWT tokens, session secret, and encryption keys are generated by Render at deploy time and persisted as service env vars.
• Horizontal scaling ready: Architecture supports scaling to multiple instances once file uploads are moved to S3 and the persistent disk is removed.
• Managed Postgres backups: Daily automatic database snapshots with point-in-time recovery on paid plans without running pg_dump.
• Visual LLM workflow builder: Runs the official Flowise Docker image providing a drag-and-drop interface for building LLM agents and RAG pipelines.

Use cases

• Platform team deploys shared Flowise instance for 25 engineers building RAG apps
• Startup ships customer-support chatbot needing daily Postgres backups for compliance
• Agency runs multi-client LLM workflows with concurrent editors on one instance
• DevOps pairs production Postgres Flowise with SQLite preview for safe staging

What's included

Next steps

1. Open the Flowise URL and create your first admin account — You should see the account creation form on first visit, and after submitting, you'll land on the empty chatflows dashboard
2. Test the Postgres connection by creating a new chatflow with a simple LLM node — Save the chatflow, refresh the page, and verify it persists in the chatflows list (confirming Postgres is storing data correctly)
3. Configure an LLM credential by clicking Credentials in the sidebar and adding your OpenAI or Anthropic API key — You should see the credential saved with a green checkmark, and it will be available when building your first agent

Resources

• Flowise official site