---
title: "Deploy CrewAI Studio"
description: "Collaborative AI workflows, data analysis, and management."
category: "Other"
url: https://railway.com/deploy/crewai-studio
---

# Deploy CrewAI Studio

Collaborative AI workflows, data analysis, and management.

**[Deploy CrewAI Studio on Railway](https://railway.com/template/crewai-studio)**

- **Category:** Other

## Template content

### Postgres https://devicons.railway.app/i/postgresql.svg

- **Image:** ghcr.io/railwayapp-templates/postgres-ssl:18

### crewai-studio

- **Image:** tham0nk/crewai-studio:latest
- **Public domain:** Yes

## Documentation

# Deploy and Host CrewAI Studio on Railway

CrewAI Studio is a Streamlit-based interface for building and managing collaborative AI workflows with CrewAI. It provides tools for AI task collaboration, data analysis, visualization, and data management, backed by PostgreSQL for persistent application data.

## About Hosting CrewAI Studio

Hosting CrewAI Studio on Railway involves deploying the Streamlit application alongside a PostgreSQL database. The application is provided as a Docker image and runs on HTTP port 8501, while PostgreSQL provides persistent relational storage. On Railway, the application can connect directly to the PostgreSQL service using Railway reference variables rather than hardcoded database hostnames or credentials. Once deployed, you can expose CrewAI Studio through a Railway-generated domain and configure the required LLM provider API keys for your AI workflows.

## Common Use Cases

* Building and managing collaborative CrewAI workflows and AI agents
* Performing data analysis and visualization through a Streamlit interface
* Managing application data and AI workflow information with PostgreSQL

## Dependencies for CrewAI Studio Hosting

* **CrewAI Studio** — deployed from the `tham0nk/crewai-studio:latest` Docker image and served on port `8501`
* **PostgreSQL** — required for application data storage and persistence

### Deployment Dependencies

* CrewAI Studio documentation: https://docs.crewai.com/
* CrewAI Studio source repository: https://github.com/strnad/CrewAI-Studio
* LLM configuration documentation: https://docs.crewai.com/concepts/llms

### Implementation Details

CrewAI Studio connects to PostgreSQL through the `DB_URL` environment variable. On Railway, use a service reference instead of the original Zeabur Docker hostname:

```env
DB_URL=${{PostgreSQL.DATABASE_URL}}
```

The application can also be configured with LLM provider credentials, depending on the models you intend to use:

```env
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GOOGLE_API_KEY=
MODEL=
```

The CrewAI Studio service should expose port `8501`. PostgreSQL should remain private within the Railway project, while CrewAI Studio can be given a public Railway domain for browser access.

## Why Deploy CrewAI Studio on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying CrewAI Studio on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.


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