The 7 best Streamlit alternatives for 2026

Streamlit made it much easier for Python users to build interactive apps without having to piece together a traditional web stack. It gave data teams and developers a fast, approachable way to turn scripts into interactive tools without having to think like front-end engineers. That’s a big part of why it became such a common choice for dashboards, internal tools, demos, and data products.

But people usually don’t look for a Streamlit alternative at random. They look because they want something a little different: a workflow that starts in notebooks, more control over application logic, a framework that fits the PyData ecosystem more naturally, or a faster way to build AI demos and chat interfaces. Sometimes a team wants more control over callbacks, layout, or longer-running tasks. Sometimes the project is really an AI demo, a chat interface, or a model playground rather than a general data app. And sometimes the notebook itself is already most of the product. Once you look at the landscape that way, the alternatives become easier to separate.

At a glance

Deepnote

Streamlit starts from the app and works outward. Deepnote starts from the notebook, where the data, code, execution history, and context already live and builds toward the app from there. That’s a meaningful difference in workflow, and it’s the reason Deepnote belongs first on this list.

The stronger distinction from Streamlit isn’t just collaboration or UI publishing. It’s that Deepnote treats the notebook as an executable artifact that humans and agents can both work in and run against. Deepnote Agent operates at the project level, editing Python, SQL, and text blocks across an entire notebook with full context – not completing one cell at a time. Notebooks can be scheduled, triggered via API, and connected to Slack. Teams can build autonomous agent workflows that run headlessly and produce auditable, reproducible outputs. Streamlit has no equivalent to this: it is an app builder, not a runtime for human-and-agent collaboration.

The app publishing side is also covered. Deepnote has native Streamlit support: teams can run existing Streamlit scripts directly inside Deepnote without changing anything. And for teams ready to move further, input blocks parameterize dashboards and notebooks into interactive apps, and published apps let multiple people use the same artifact simultaneously without interfering with each other.

Pros:

• Notebooks function as executable artifacts: schedulable, API-triggered, parameterized, and composable into larger workflows
• Agent operates across the full project context, not individual cells
• Path from collaborative analysis to shareable web-app, scheduled job, or autonomous workflow is built in

Cons:

• Not the right choice for teams that need a pure, standalone app framework decoupled from notebook workflows
• Deepnote isn't a BI tool; if pivot tables and dashboarding are the core use case, something purpose-built for that will serve you better.
• Some teams want deeper native support for building production AI systems outside the notebook context

Gradio

Gradio has grown into one of the clearest Streamlit alternatives for AI-native apps. Gradio is the primary SDK for Hugging Face Spaces: the platform used by over 1 million developers a month to build and share AI interfaces. Publishing a Gradio app to Spaces means instant hosting, API access via gradio_client, ZeroGPU support, and now automatic MCP server exposure. Any Spaces app running Gradio 5.28+ is MCP-compatible, which means the entire Spaces catalog has effectively become a distributed tool registry for LLMs. That is a unique position no other Python app framework holds.

Current Gradio supports queuing, streaming, multipage apps, client libraries, chatbot tooling, and direct API endpoints exposed from plain Python functions. gradio.Server lets teams bring a fully custom frontend: React, Svelte, plain HTML while still getting Gradio’s queuing engine, concurrency management, MCP support, and Spaces hosting on the backend.

Pros:

• The primary SDK for Hugging Face Spaces; free hosted MCP server publishing out of the box
• Spaces ecosystem functions as a discoverable tool registry for LLMs
• Strong for AI demos, chat interfaces, tool-using agent UIs, and model playgrounds

Cons:

• Less suitable for general-purpose data dashboards or analytics workflows that aren’t model-facing
• Apps are stateless by default; managing longer-running or more complex state requires additional work
• Not built around notebook-native workflows; teams doing analysis-to-app work will find Deepnote a more natural fit

Dash

Dash is the obvious alternative when Streamlit starts to feel too implicit and you want explicit application architecture. Plotly positions it as the original low-code framework for production-oriented data apps in Python, and the current product reflects that: background callbacks let long-running work run in a separate queue using Diskcache or Celery, and partial property updates let you patch only the changed part of a component rather than resending everything. That’s the kind of control teams start asking for once an app graduates from prototype.

Plotly positions it as the original low-code framework for production-oriented data apps in

Pros:

• Explicit callback architecture gives teams more control over data flow and component updates
• Background callbacks and Celery support make it viable for longer-running production tasks
• More engineering-oriented feel is an advantage for teams building complex, stateful apps

Cons:

• More verbose to write than Streamlit; the callback model has a steeper initial learning curve
• Less active community growth than Gradio or Shiny for teams building AI-facing interfaces
• No native notebook workflow or agentic execution path

Dash has also had a more visible UI refresh since the older compare pages were written. Dash 4 updated the styling of core components, added dcc.Button, improved uploads with folder selection, and added newer properties across inputs, dropdowns, tabs, and date pickers.

Panel

Panel is the PyData-native option. That alone makes it more relevant than many of the generic alternatives that often get listed. Panel is trying to be a broader Python app framework that feels comfortable with notebooks, data apps, custom components, and the wider PyData stack.

Pros:

• Broadest PyData ecosystem compatibility of any tool on this list
• WebAssembly/Pyodide support for browser-side execution
• Panel 2.0 roadmap points toward a more modern UI layer without breaking existing apps

Cons:

• More concepts to learn than Streamlit; the flexibility comes with surface area
• Smaller community than Streamlit or Dash, which means fewer examples and community plugins
• Panel 2.0 is not yet released; the Material UI layer is still in progress

The current Panel docs also make its direction clearer than before. Panel 2.0 points toward a new opt-in panel.ui namespace with Material UI–based components and modern defaults, without breaking existing apps. At the same time, today’s Panel already supports ReactiveESM custom components, WebAssembly and Pyodide deployment, chat interfaces, layout templates, and a long list of native integrations across Plotly, Vega, HoloViews, ipywidgets, Perspective, and more. That makes Panel especially interesting for users who like Streamlit’s Python-first feel, but want a wider ceiling.

Shiny for Python

Shiny for Python is the alternative for teams that want a more formal reactive model. Posit’s own comparison with Streamlit lays out the trade-off directly: Streamlit reruns the whole script when input changes, while Shiny’s reactive execution minimizes rerendering, scales to larger apps more naturally, and makes it easier to structure complexity without hand-managing state. That’s the kind of difference that may not matter on day 1, but often matters a lot later.

Pros:

• Reactive execution model minimizes rerendering and scales better than Streamlit’s full-rerun approach
• Strong fit for larger, more stateful apps where architecture matters
• querychat and LLM template support extend it into AI-adjacent workflows
• OpenTelemetry support makes production observability more straightforward

Cons:

• The reactive programming model requires a shift in how you think about app logic; not as approachable for quick scripts
• Smaller Python community than its R equivalent; the documentation and ecosystem are still catching up
• Less relevant for AI demo or agent interface work, where Gradio is the more natural fit

Recent updates include bookmarking, new generative AI documentation and toolbar components for compact UIs plus built-in OpenTelemetry support for production observability. The docs and blog also now lean into chat interfaces, LLM templates, querychat, and modular app patterns. So Shiny has become a stronger answer for teams that want a Python framework with serious reactive structure and a growing AI layer around it.

Taipy

Taipy is the business-app option on this list. It is a Python open-source library for data-driven web applications that covers both front-end and back-end, and explicitly says you can build interactive multi-page applications without prior web development experience. That makes it a better fit than Streamlit for teams that want a Python-only framework but need more workflow and state machinery than a typical dashboard script provides.

Pros:

• Better support for multi-page workflows and longer-running interactions than Streamlit
• Data decimation and performance improvements make it more viable for large dataset visualization
• Async-friendly callbacks for longer-running tasks

Cons:

• Smaller community and ecosystem than the more established tools on this list
• Not a natural fit for AI demos or notebook-first workflows
• Less relevant outside the use case of internal business-style Python applications

Taipy 4.1 added an EventProcessor, MockState for testing, async-friendly long-running callbacks, and chart performance improvements. Its chart controls also support data decimation to reduce the amount of data sent to the browser when visualizing large datasets.

Voilà

Voilà remains an apt alternative when your starting point is already a notebook. It can run as a standalone application or as a Jupyter server extension, and the current docs focus on deployment, static files, idle-kernel culling, preheated kernels, cell execution timeouts, and notebook-to-dashboard publishing.

That makes it a fundamentally different kind of alternative from everything else on this list: not a new framework to learn, but a way to turn a notebook into a shareable app with minimal change. Teams that want notebook-and-app in the same environment, with collaboration and recurring execution built in, will find Deepnote a more complete answer. Voilà is the right call when the Jupyter notebook is already the product and the only thing needed is a clean way to surface it.

Pros:

• Minimal rewrite compared with adopting a new app framework
• Works as a Jupyter server extension, so it sits close to existing workflows
• Preheated kernels and execution timeout controls make deployment more predictable

Cons:

• No collaboration, scheduling, or agentic execution: it publishes notebooks, it doesn’t make them composable
• Limited to what Jupyter and ipywidgets can express; no custom component model
• For teams that want collaboration and apps in the same environment, this is the wrong level of the stack to solve at

FAQs

What is the best Streamlit alternative for teams?

Deepnote is the strongest team-oriented option in this list because it combines collaborative notebooks, app publishing, parameterized inputs, and project-aware AI in one workspace, and it can also host native Streamlit apps directly.

What is the Streamlit alternative for publishing data apps?

Deepnote is a strong fit when the app grows out of analysis and needs to stay connected to the underlying notebook. Teams can use inputs, data apps, scheduled runs, and APIs to turn notebook work into interactive outputs that others can use. This is different from building an app as a separate script: the code, context, outputs, and published experience stay part of the same workflow.

What is the Streamlit alternative if I already work in notebooks?

Deepnote is the better fit if your app starts from notebook work. It keeps analysis, collaboration, execution history, scheduling, and publishing in the same environment, so the notebook can become a data app without being rewritten into a separate app framework. If the only goal is to expose an existing Jupyter notebook with minimal changes, Voilà can work as a narrow publishing option, but it does not solve collaboration, orchestration, or agent-driven execution.