I work on the Gradio team at Hugging Face and spend a lot of time with developers building AI apps.
People still ask me, "should I use Gradio, Streamlit, or something else?" all the time. The answer has changed a lot since 2024, but most comparison articles haven't kept up and benchmark against Gradio 4.x, which is over a year old. This post is what I'd actually tell a colleague picking a framework today.
The Short Version
If you're building AI apps today, Gradio is by far the best choice. It has the deepest AI infrastructure (ZeroGPU, HuggingFace Spaces), the most flexible UI options (gr.HTML, gr.Server), and native MCP, API, and concurrency support that no other framework offers.
Streamlit is still great for data dashboards, analytics tools, and business apps. If your app is mostly charts and tables with some interactivity, Streamlit works well.
The Outdated Narrative
The most common advice you'll still find online is "Gradio for quick ML demos, Streamlit for dashboard apps." This framing made sense in 2022. It doesn't hold up anymore. This advice dates to when Gradio had a single UI type, called Interface. It has since then released several lower-level primitives and customization features.
gr.Blocks (not gr.Interface) accounts for 80% of Gradio usage. With gr.Server, you can use any frontend framework while keeping Gradio's backend. With gr.HTML, you can build rich interactive components inline. Gradio now elegantly handles load, supports fast load times, OAuth, and prioritizes security with independent auditing (Trail of Bits — Gradio 5).
If you're reading a comparison article that only shows gr.Interface examples, or benchmarks against Gradio 4.x, or doesn't mention gr.Server, MCP, or ZeroGPU, the article is absolutely outdated.
What Changed in Gradio 5 and 6
Gradio 6.0 launched in November 2025. Since then, there have been 11 minor releases through April 2026, roughly two per month. Here's what shipped that matters for this comparison:
gr.Server (docs) lets you build any frontend you want (React, Svelte, plain HTML) while using Gradio's backend for queuing, streaming, concurrency management, and ZeroGPU. This is a FastAPI server with Gradio's API engine on top. If you've ever heard "Gradio limits your UI," this is the answer: bring whatever frontend you want, keep the backend infrastructure. I wrote a detailed post about this: Any Custom Frontend with Gradio's Backend.
gr.HTML(docs) got major upgrades: custom templates with ${value} substitution, scoped CSS, JavaScript interactivity, and the ability to create reusable custom components. You can build a full interactive web app inside a single gr.HTML component. More on this in One-Shot Any Web App with gr.HTML.
MCP server support is a one-liner. You can add mcp_server=True to .launch() and every API endpoint in your Gradio app becomes an MCP tool, with names, descriptions (from your Python docstrings), and input schemas generated automatically. Your app is now callable by Claude, ChatGPT, Cursor, VS Code, or any MCP client. The endpoint lives at /gradio_api/mcp/sse.
Beyond the basics, Gradio supports all MCP primitives plus MCP-only functions via gr.api(), which appear in the MCP interface but not the UI.
A framework that is built for the agentic age. Any Gradio app is MCP compliant, and has a built-in API so it can be used by your agent in custom workflows via MCP or direct API calls. With v6.10.0, Gradio has also released support for calling app endpoints from the terminal via hf-gradio Predictions CLI.
Best practices baked in. SSR and progressive loading for fast page loads. Built-in media streaming. Security audit. Multi-page apps. These aren't easy to "vibe code," and most other frameworks don't offer them out of the box. Gradio is designed for the needs of today's AI engineers.
Gradio vs Streamlit: Where Each Wins
Here's a concrete comparison of what each framework can and can't do as of April 2026:
| Feature | Gradio | Streamlit |
|---|---|---|
| ML Components | Audio, Image, Video, Model3D, AnnotatedImage, Chatbot with multimodal support | Basic media display. No specialized ML components |
| MCP Server | Native. One line to enable | None. No built-in way to expose an app as MCP tools |
| Free GPU | ZeroGPU on Spaces (H200, 70GB vRAM). @spaces.GPU decorator |
Not available. Streamlit apps on Spaces can't use ZeroGPU |
| Custom Frontends |
gr.Server for any framework. gr.HTML for inline custom components |
Limited to Streamlit's component API and st.components.v1.html
|
| Execution | Event-driven. Only the triggered function runs | Full script re-execution on every interaction |
| API Access | Every app has a built-in API. gradio_client for Python, @gradio/client for JS |
No built-in API layer |
| Free Hosting | HuggingFace Spaces (default SDK) | Streamlit Community Cloud, also on HF Spaces |
| Dashboards | Possible but not the sweet spot | Excellent. st.dataframe, st.metric, st.plotly_chart are polished |
| Production Systems Examples | Automatic1111 WebUI, LLaMA-Factory, LMSYS Chatbot Arena, and Text Generation WebUI | Streamlit's production use is mostly internal dashboards and enterprise tools (Uber, Dropbox, Snowflake ecosystem, etc.), not widely-known open-source ML projects that people would recognize by name. |
ZeroGPU: Free Serverless GPUs for Gradio Apps
ZeroGPU is a serverless GPU system on HuggingFace Spaces that dynamically allocates state-of-the-art GPUs (NVIDIA H200 GPUs with 70GB vRAM at the time of writing this article) to Gradio apps on demand. You add a @spaces.GPU decorator to any function that needs GPU access, and the system handles allocation and release automatically.
PRO users ($9/month) get 25 minutes of daily H200 compute with highest priority. Free-tier users get access too, with lower priority. Personal accounts can run up to 10 ZeroGPU Spaces.
This only works with the Gradio SDK. Streamlit apps on HuggingFace Spaces cannot access ZeroGPU. For ML demos that need GPU inference, this is a significant practical advantage.
What Happened to the Other Frameworks
Chainlit (~11.9K stars): The founding team (LiteralAI/Chainlit SAS) stepped back from active development in May 2025 and pivoted to a new Y Combinator startup called Summon. Chainlit is now community-maintained. Two high-severity security vulnerabilities were found in late 2025. If you're building a pure chat interface, Chainlit still works, but the long-term outlook is uncertain.
Mesop (~6.5K stars): Moved from google/mesop to mesop-dev/mesop, which suggests reduced Google sponsorship. Recent releases are mostly security fixes. The AI-related package was removed.
NiceGUI (~15.5K stars): Actively developed, but targets general-purpose Python web UIs (robotics, IoT, dashboards), not ML demos specifically.
Reflex (~28.3K stars): A fast-growing Python web framework. Y Combinator-backed. But it compiles to Next.js and targets enterprise apps, not ML apps.
Panel (~5.7K stars): Still solid for scientific visualization and notebook-to-dashboard workflows.
No other significant new ML demo frameworks were launched in the recent period.
Which Should You Pick
Use Gradio if you're building AI apps, simple model inference apps, model playgrounds, or anything that needs GPU access, MCP tool exposure, or specialized ML components. Also, use Gradio if you want the flexibility to bring a custom frontend later via gr.Server.
Use Streamlit if you're building data dashboards, analytics tools, or business reporting apps where the core interaction is filtering and visualizing tabular data. Streamlit's st.dataframe, charting integrations, and Community Cloud make this workflow smooth.
Use Others if you're building a general web app that happens to use Python. Different problem, different tool.
For ML work in 2026, Gradio is the framework to start with.
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