Gradio vs Streamlit

Gradio

Build ML web demos in minutes with Python

Streamlit

Build data apps in Python with minimal code

Feature Gradio Streamlit
Category AI / ML AI / ML
Sub-category ML Demo ML Demo
Maturity stable stable
Complexity beginner beginner
Performance tier lightweight lightweight
License Apache-2.0 Apache-2.0
License type permissive permissive
Pricing fully free fully free
GitHub stars 37.0K 40.0K
Contributors 400 300
Commit frequency daily weekly
Plugin ecosystem none medium
Docs quality excellent excellent
Backing org Hugging Face Snowflake
Funding model vc_backed corporate
Min RAM 256 MB 512 MB
Min CPU cores 1 1
Scaling pattern single_node single_node
Self-hostable Yes Yes
K8s native No No
Offline capable No No
Vendor lock-in none none
Languages Python Python
API type REST, SDK SDK
Protocols HTTP HTTP
Deployment pip, docker pip, docker
SDK languages python python
Team size fit solo, small solo, small
First release 2019 2019
Latest version

When to use Gradio

  • Share ML model demos with stakeholders via link
  • Build internal data labeling tools
  • Create interactive AI applications for non-technical users
  • Prototype and test models before production deployment

When to use Streamlit

  • Build interactive data exploration dashboards
  • Create ML model demos with file upload and predictions
  • Internal analytics tools for non-technical teams
  • LLM chatbot prototypes with conversation UI

Gradio anti-patterns

  • Not for production-grade web applications
  • Limited customization compared to React/Vue
  • Not suitable for complex multi-page apps

Streamlit anti-patterns

  • Not for complex multi-page production apps
  • Reruns entire script on interaction - slow for heavy compute
  • Limited layout control compared to proper frontend frameworks
Full Gradio profile → Full Streamlit profile → All comparisons