Databricks Apps vs Streamlit

Summary

• Databricks Apps is a platform for building web applications on Databricks, combining FastAPI (backend) and React (frontend).
• Streamlit is an open-source Python framework for building data applications as single-file apps that run in any environment.
• Databricks Apps are purpose-built for Databricks data and compute; Streamlit is framework-agnostic and requires you to manage infrastructure.
• Databricks Apps provide a unified authentication and data-access model through Databricks; Streamlit requires manual credential management.
• Use Databricks Apps if you're a Databricks customer wanting tight platform integration; use Streamlit if you want flexibility and multi-cloud deployment.

Databricks Apps vs Streamlit

Databricks Apps and Streamlit serve overlapping use cases (data applications), but differ in scope. Databricks Apps is a complete platform: FastAPI backend, React frontend, and tight integration with Databricks compute, data, and identity. Streamlit is an open-source framework for building single-file Python applications that can run anywhere. Databricks Apps prioritize platform integration and multi-user scalability; Streamlit prioritizes simplicity and portability.

What is Databricks Apps?

Databricks Apps is a platform for building web applications within the Databricks workspace. Apps consist of a Python backend (FastAPI) and React frontend. The backend runs as a service, accessing Databricks compute (clusters, warehouses), data in Unity Catalog, and workspace identity management. The platform handles authentication, secret management, deployment, and scaling automatically.

What is Streamlit?

Streamlit is an open-source Python library that turns data scripts into interactive web applications. An app is a single Python file where UI elements (buttons, charts, inputs) are declared in code. When a user interacts, the script reruns from top to bottom. Streamlit handles rendering and interactivity; you manage data connectivity, deployment, and infrastructure independently.

Key capabilities to evaluate

Capability	Databricks Apps	Streamlit (Open Source)
Architecture	FastAPI backend + React frontend (separated)	Single-file Python app
Infrastructure	Runs in Databricks workspace	Runs anywhere (cloud, on-prem, local)
State Model	HTTP-stateless backend	Session-state in Python memory
Data Integration	Native: clusters, warehouses, Unity Catalog	Manual: Python libraries (SQLAlchemy, pandas, etc.)
Authentication	Workspace identity, role-based access	Your own auth layer or None
Deployment	Managed by Databricks	DIY (containers, servers, Streamlit Cloud)
Scalability	Designed for multi-user production	Per-user execution model
Development Speed	Moderate (backend + frontend code)	Rapid (single file)
Framework Lock-In	Databricks-specific	Platform-agnostic
Cost Model	Included in Databricks workspace	Free (OSS), optional Streamlit Cloud

How Databricks approaches applications

Databricks Apps tightly integrate with the Databricks platform. The FastAPI backend runs as a managed service, eliminating deployment complexity. Data connectivity is built-in: your app inherits Databricks workspace permissions through Unity Catalog. Authentication is automatic: users are identified via workspace identity. The React frontend enables rich, production-quality user experiences.
This integration comes at a cost: Databricks Apps are tied to the Databricks platform. Portability and multi-cloud deployment are limited.

How Streamlit approaches applications

Streamlit prioritizes developer experience and portability. Write a Python script, declare the UI in code, and Streamlit handles rendering. The app runs in any environment: your laptop, a cloud VM, Kubernetes, or Streamlit Cloud (optional commercial hosting). Data connectivity is flexible: use any Python library (Pandas, SQLAlchemy, boto3, etc.). Authentication is your responsibility.
Streamlit's simplicity comes with tradeoffs: the full-script rerun model scales poorly with many users, state management is in-process (not shared across instances), and you manage infrastructure and credentials yourself.

Common use cases

Databricks Apps: Production dashboards, internal reporting tools for teams already in Databricks, applications combining Databricks compute with web UI.
Streamlit: Exploratory analysis, internal tools for data teams, ML model demos, data visualizations, rapid prototyping, and multi-cloud deployments.

FAQs

Can I run a Streamlit app on Databricks?

Yes. Streamlit can run on Databricks compute (clusters or serverless SQL warehouses). However, you lose the native integration that Databricks Apps provide (authentication, data access, secret management).

Is Streamlit suitable for production applications with many users?

Streamlit can be deployed to production, but its single-file, full-rerun architecture scales to small/medium teams. For large teams or high concurrency, Databricks Apps is a better fit.

Can I use Databricks Apps outside of Databricks?

No. Databricks Apps require a Databricks workspace and integrate with Databricks compute and data.

Can I convert a Streamlit app to Databricks Apps?

Not automatically. You'd need to refactor: separate business logic (backend) from UI (frontend) and rewrite the frontend in React.