Streamlit vs Taipy 2026: Best Python Web App Framework?

The Streamlit vs Taipy debate has shifted a lot since these tools first gained traction. Both are Python web app frameworks that turn scripts into interactive apps without writing HTML or JavaScript. Both are free, open-source (Apache 2.0), and actively maintained. The gap between them, and the gaps each one has closed, makes the 2026 comparison very different from even two years ago.

This article covers current versions (Streamlit v1.59.0, Taipy v4.1.1), production features, pricing, self-hosting, and a decision framework to help you pick the right tool for your Python data apps. If you want a broader view of the ecosystem, see our comprehensive guide to Python web frameworks.

What’s changed since 2024: Streamlit vs Taipy at a glance

Both tools have shipped major releases since January 2024. Streamlit went from ~v1.29 to v1.59.0; Taipy went from ~v2.x to v4.1.1. Here are the headline changes for each.

Key differences between Streamlit and Taipy in 2026

The old “10 differences” list from the original article had several items that were wrong or outdated. Here’s a structured comparison across the dimensions that actually matter.

Execution model

Taipy uses callbacks to re-run only the code path affected by a user action. If you change one input, only that callback executes. The rest of the app doesn’t re-run. This is fundamentally different from Streamlit’s default top-to-bottom re-run model. Streamlit mitigated this with @st.fragment (v1.42+), which lets you isolate sections for partial re-runs, and fragments can now write to outside containers (v1.59). But Taipy’s callback-first architecture is still more efficient for complex data pipelines.

Async and concurrency

Taipy has always supported both synchronous and asynchronous calls with separate GUI and Core threads. The UI never freezes during backend computation. Streamlit closed the gap significantly: @st.fragment(parallel=True) (v1.58) enables concurrent fragment execution, and the Starlette/Uvicorn server (v1.57) provides proper async request handling. For most internal tools, the difference is now negligible. For compute-heavy ML pipelines, Taipy’s thread separation still matters.

Layout and design

Streamlit added horizontal flex containers (v1.48), width parameters (v1.46+), advanced theming without CSS (v1.44+), custom light and dark themes with reusable theme files (v1.51), st.space for spacing (v1.51), and st.bottom pinned container (v1.57). The “can’t customize Streamlit” criticism is outdated. Taipy still offers more explicit layout control through its markdown-based page syntax, which some developers prefer for complex multi-page apps.

Data handling

Taipy’s data decimation and pipeline management remain strengths for big data and ML workloads. Its scenario management system lets you version, track, and compare data pipelines, something Streamlit doesn’t attempt. Streamlit improved with st.data_editor enhancements (column sorting, pinning, search/filter), session-scoped caching (v1.53), and st.cache_resource with cleanup hooks (v1.53).

Chat and AI features

Both tools now have chat UI components. Streamlit has st.chat_input with file uploads (v1.43), audio input (v1.52), paste support (v1.59), and st.write_stream for OpenAI/LLM streaming. Taipy added its Chat control in v4.0. Streamlit has become the go-to for AI and chat app demos. If you’re building an LLM front-end, Streamlit’s chat primitives are more mature.

For another angle on how Streamlit compares to other tools, see our Streamlit vs NiceGUI comparison.

Streamlit’s new production features (v1.42 to v1.59)

The biggest criticism of Streamlit in 2024 was “great for prototypes, not for production.” That’s no longer accurate. Here’s what changed.

Native authentication with st.login()

Streamlit v1.42 (February 2025) added st.login(), st.logout(), and st.user with OIDC support. You can gate any Streamlit app behind Google, Microsoft Entra, Okta, Auth0, or Keycloak. Free, no enterprise license.

Configuration goes in .streamlit/secrets.toml:

# .streamlit/secrets.toml
[auth]
redirect_uri = "http://localhost:8501/oauth2callback"
cookie_secret = "your-random-secret-here"
client_id = "your-client-id"
client_secret = "your-client-secret"
server_metadata_url = "https://accounts.google.com/.well-known/openid-configuration"

Then in your app:

import streamlit as st

if not st.user.is_logged_in:
    st.button("Log in with Google", on_click=st.login)
    st.stop()

st.title(f"Welcome, {st.user.name}")
st.button("Log out", on_click=st.logout)

If you’re building AI-powered apps with authentication, check out our guide on integrating Groq API with Streamlit and building AI agents with Agno and Streamlit.

Async and parallel execution with Starlette

Streamlit v1.57 (April 2026) replaced Tornado with Starlette/Uvicorn as the web server. This brings:

• Proper ASGI compatibility: better reverse-proxy behavior with Caddy, Nginx, Traefik
• Mountable inside FastAPI: st.App ASGI entry point for custom routes
• App.run() entry point (v1.59): run with python app.py instead of streamlit run

Then v1.58 added @st.fragment(parallel=True):

@st.fragment(parallel=True)
def background_chart():
    # This runs in parallel without blocking the UI
    data = expensive_query()
    st.line_chart(data)

What to watch out for: The Tornado to Starlette migration may break custom Streamlit components that relied on Tornado APIs. If you have custom components, test them against v1.57+ before upgrading in production. Auth cookie persistence had issues in v1.57, fixed in v1.58.

Layout, theming, and design upgrades

Streamlit now offers meaningful design customization:

• Horizontal flex containers (v1.48): alignment, direction, gap parameters
• Width parameters for most elements (v1.46+)
• Advanced theming (v1.44+): custom fonts, colors, roundness, chart colors, heading sizes via TOML config
• Custom light and dark themes with reusable theme files (v1.51)
• st.space for vertical/horizontal spacing (v1.51)
• st.bottom pinned container (v1.57): great for chat input bars

AI and chat features

Streamlit added several AI and chat features:

• st.chat_input with file uploads (v1.43), audio input (v1.52), paste support (v1.59)
• st.write_stream for OpenAI streaming (v1.43+), now supporting Responses API (v1.59)
• st.skeleton loading placeholders (v1.59)
• st.mermaid_chart (v1.59)
• st.pdf rendering (v1.49)
• Bundled AI coding skills (streamlit skills, v1.58)

Taipy’s architecture and enterprise advantages (v3.0 to v4.1)

Taipy’s core architectural strengths haven’t changed, but the packaging and feature set have.

Taipy’s callback model vs Streamlit’s fragment model

Taipy’s callbacks only re-execute the code path affected by a user action. This is a fundamentally different execution model from Streamlit’s top-to-bottom re-run. Combined with separate GUI and Core threads, the UI never freezes during backend computation.

Why this matters: if you’re building a data pipeline app where a user changes one parameter and needs to re-run a specific calculation, Taipy only runs that calculation. Streamlit’s @st.fragment isolates sections for partial re-runs, which helps. But it’s a patch on top of the re-run model, not a different architecture.

For simple dashboards with a few widgets, the difference is negligible. For complex multi-step data workflows with large datasets, Taipy’s approach uses less compute and feels snappier.

Taipy 4.0 package restructure

Taipy 4.0 (October 2024) merged 6 separate packages into a single taipy package:

# Old (Taipy 3.x) - 6 packages
pip install taipy taipy-config taipy-common taipy-gui taipy-core taipy-templates taipy-rest

# New (Taipy 4.x) - 1 package
pip install taipy

Taipy 4.1 (February 2026) added EventProcessor for event-driven processing, MockState for testing, async callbacks, and chart rendering performance improvements.

Taipy Enterprise: auth, SSO, and Designer

Taipy’s Community edition (Apache 2.0) is free and self-hostable. Authentication, SSO, ACL management, Taipy Designer (a drag-drop GUI builder), dedicated support, and platform integrations (Databricks, Snowflake, Dataiku, AWS SageMaker) are Enterprise-only features. Pricing requires contacting sales. No public pricing is available.

This is a cost consideration: Streamlit’s OIDC auth is free. Taipy’s auth requires an Enterprise license. If you need to gate your app behind SSO and you’re cost-conscious, Streamlit has a clear advantage.

Updated Taipy code example

Here’s a working Taipy 4.x example with the unified package. Note: verify against the latest Taipy version, as the 4.x API may have further changes.

import taipy as tp
from taipy.gui import Gui, State

# Define callback
def on_submit(state: State):
    state.message = f"Hello {state.name}!"

# Define page
name = ""
message = ""

page = """
# Taipy Hello App

Enter your name: <|{name}|input|>

<|Submit|button|on_action=on_submit|>

Message: <|{message}|text|>
"""

if __name__ == "__main__":
    gui = Gui(page)
    gui.run(title="Taipy Hello App", port=8080)

Pricing and self-hosting: running Streamlit and Taipy on a VPS

Both tools are free and open-source for self-hosting. Both run comfortably on a $5-10/month VPS. A Hetzner Cloud VPS at the CX22 tier (2 vCPU, 4 GB RAM) handles moderate traffic for either framework. Hostinger VPS is another budget option with NVMe storage if you prefer a different provider.

Streamlit Community Cloud vs self-hosting

Streamlit Community Cloud: free, up to 3 apps, public GitHub repos only. Good for demos and portfolios. For anything private or production-grade, you need to self-host. Streamlit in Snowflake uses Snowflake credits (enterprise pricing).

For self-hosting, the Starlette server (v1.57) makes reverse-proxy setups cleaner. Proper ASGI means no more Tornado quirks behind Caddy or Nginx. Python version note: Streamlit dropped Python 3.9 support. Taipy supports Python 3.9-3.12.

Docker deployment for both tools

Both tools Dockerize cleanly. Streamlit’s Starlette server gives you proper ASGI, which plays well with reverse proxies. Taipy Community requires self-hosting (no managed cloud option). Both need a process manager (Docker restart policy or systemd) for production.

For a full guide on running Python apps in containers, see how to run any Python app in Docker. If you’re setting up a Python project from scratch, setting up Python projects with uv is the modern approach. For Dokploy-based deployments, check deploying Python projects with Dokploy.

# docker-compose.yml
services:
  streamlit:
    image: python:3.12-slim
    working_dir: /app
    volumes:
      - .:/app
    ports:
      - "8501:8501"
    command: >
      sh -c "pip install streamlit &&
             streamlit run app.py
             --server.port=8501
             --server.address=0.0.0.0
             --server.headless=true"
    restart: unless-stopped

# docker-compose.yml
services:
  taipy:
    image: python:3.12-slim
    working_dir: /app
    volumes:
      - .:/app
    ports:
      - "8080:8080"
    command: >
      sh -c "pip install taipy &&
             python app.py"
    restart: unless-stopped

For a production-grade Streamlit deployment with TLS and tunneling, see our guide to deploy Streamlit on a VPS with Cloudflare Tunnels.

When to choose Streamlit vs Taipy: decision guide

No hand-waving. Here are the clear scenarios.

Choose Streamlit when

• Building AI/chat interfaces or LLM demos (native chat components, st.write_stream)
• Need free OIDC authentication without enterprise licensing
• Want the largest community, most tutorials, easiest hiring
• Building internal tools where rapid iteration matters more than fine-grained reactivity
• Already in the Snowflake ecosystem

Choose Taipy when

• Building data pipelines or ML workflows with complex scenario management
• Need granular reactivity: callbacks re-run only what changed
• Processing large datasets where data decimation matters
• Building multi-page production apps where separate GUI/Core threads prevent UI freezes
• Need Jupyter Notebook integration for data science workflows
• Willing to pay for Enterprise features (auth, SSO, Designer)

Use both when

• Prototype in Streamlit (faster iteration), production in Taipy (better backend management)
• Use Streamlit for quick internal dashboards, Taipy for customer-facing data products

Alternatives to Streamlit and Taipy for Python data apps

The Python web app ecosystem has grown. Here are the other tools worth knowing about:

• Gradio (Hugging Face): best for ML model demos and Hugging Face Spaces integration. Strong chat interface support and MCP integration. If you’re building a quick model demo, Gradio is often the fastest path.
• Reflex (formerly Pynecone): full-stack web apps in pure Python with more control over routing and state. Closer to a traditional web framework than a dashboard tool.
• Shiny for Python (Posit): reactive execution model from the R ecosystem. Scales better than Streamlit for complex reactive apps. Worth a look if you come from R.
• Dash (Plotly): production-oriented with explicit callback architecture. Strong for data visualization. More verbose than Streamlit but more predictable at scale.
• Marimo: reactive notebooks with WASM support. Interesting hybrid between notebook and web app.
• NiceGUI: based on FastAPI and Vue/Quasar, more web-dev-oriented. See our Streamlit vs NiceGUI comparison and NiceGUI Python UI framework guide.

For a broader view, FastHTML for building Python web UIs is another option if you want something closer to raw HTML with Python convenience.

Quick start: Streamlit vs Taipy code examples

Side-by-side examples you can copy, paste, and run.

import streamlit as st
import pandas as pd

st.set_page_config(page_title="Data Explorer", layout="wide")

# Optional: uncomment for OIDC auth
# if not st.user.is_logged_in:
#     st.button("Log in", on_click=st.login)
#     st.stop()

st.title("Data Explorer")

with st.sidebar:
    st.page_link("app.py", label="Home", icon="🏠")
    dataset = st.selectbox("Dataset", ["Iris", "Tips", "Penguins"])

data = getattr(pd, f"read_csv", lambda x: pd.DataFrame())(
    f"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/{dataset}.csv"
)

# Fragment for partial re-runs
@st.fragment
def data_section():
    col1, col2 = st.columns(2)
    with col1:
        st.subheader("Raw Data")
        st.dataframe(data, use_container_width=True, height=400)
    with col2:
        st.subheader("Chart")
        numeric_cols = data.select_dtypes("number").columns
        if len(numeric_cols) >= 1:
            st.line_chart(data[numeric_cols[:3]], use_container_width=True)

data_section()

with st.expander("Column Statistics"):
    st.dataframe(data.describe())

from taipy.gui import Gui, State
import pandas as pd

# Callback: only re-runs when triggered
def on_dataset_change(state: State):
    url = f"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/{state.dataset}.csv"
    state.data = pd.read_csv(url)

# Initial state
dataset = "iris"
data = pd.read_csv(
    f"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/{dataset}.csv"
)

# Page definition
page = """
# Data Explorer

Dataset: <|{dataset}|selector|lov=iris;tips;penguins|on_change=on_dataset_change|>

## Raw Data
<|{data}|table|height=400px|>

## Statistics
<|{data.describe()}|table|>
"""

if __name__ == "__main__":
    gui = Gui(page)
    gui.run(title="Data Explorer", port=8080)

Conclusion

Both Streamlit and Taipy have matured significantly since 2024. Streamlit closed the production gap with native authentication, a Starlette server, parallel fragments, and advanced theming. It remains the easier on-ramp with the larger community and the richer AI/chat feature set. Taipy retains architectural advantages for data-heavy, reactive applications. Its callback model, separate threads, and scenario management are hard to replicate with Streamlit’s re-run approach.

The “right” choice depends on your use case. The decision guide in Section 7 gives you the actionable framework. For most internal tools and AI demos, Streamlit is the default pick. For complex data pipelines and ML workflows, Taipy is worth the steeper learning curve.

If you’re ready to deploy, here’s how to get Streamlit running on your VPS:

Frequently asked questions