Streamlit vs Dash (Plotly) in 2026: The Definitive Python Framework Comparison

Name

Soren Atelier

Updated on 2/17/2026

Choosing the wrong Python web framework for your data application can cost weeks of refactoring. You pick Streamlit for its simplicity, build a prototype, then discover it struggles under 50 concurrent users in production. Or you invest in Dash's callback architecture, only to realize your team of data scientists finds it too complex for iterative exploration.

This decision becomes more consequential as both frameworks mature. Streamlit (v1.42, February 2025) now supports advanced layouts and custom components, while Dash (v2.18, late 2024) has simplified its API significantly. The gap between them has narrowed, but they remain fundamentally different tools built for different workflows.

This guide provides a thorough, data-backed comparison of Streamlit and Plotly Dash across every dimension that matters: learning curve, performance, customization, deployment, enterprise readiness, and community. We also cover newer alternatives like Gradio, Panel, and Reflex so you can make an informed decision for your next project.

Quick Comparison: Streamlit vs Dash at a Glance

Before diving into details, here is a high-level overview of both frameworks as of early 2026:

What Is Streamlit?

Streamlit is a Python framework that turns data scripts into shareable web applications with minimal code. Created by Adrien Treuille, Thiago Teixeira, and Amanda Kelly in 2019, it was acquired by Snowflake in March 2022 for approximately $800 million.

Key technical facts:

• Architecture: Script-based. Your entire app re-runs from top to bottom on each interaction. Streamlit manages state through st.session_state and uses caching decorators (@st.cache_data, @st.cache_resource) to avoid redundant computation.
• Frontend: Built on React, but developers never touch React code. The UI is generated purely from Python.
• Current version: 1.42.x includes st.dialog, st.fragment (partial reruns), improved st.columns layout, and native chat elements for LLM applications.
• Ecosystem: Over 800 community components on the Streamlit Component Hub. First-class integration with Snowflake, Hugging Face Spaces, and major cloud providers.

Streamlit's strength is speed-to-deployment. A data scientist with no web development experience can go from a Jupyter notebook to a live web application in under an hour.

What Is Dash?

Dash is a Python framework for building analytical web applications, created by Plotly in 2017. It combines Flask on the backend with React.js on the frontend, using Plotly.js as its native charting engine.

Key technical facts:

• Architecture: Callback-driven. You define the layout declaratively, then write callback functions that react to user inputs. Dash does not re-run your entire script on each interaction -- only the relevant callbacks execute.
• Frontend: Built on React. Developers can create custom React components and use full CSS/HTML for layout control.
• Current version: 2.18.x includes Dash Pages for multipage routing, pattern-matching callbacks, long callbacks for background processing, and Dash AG Grid for enterprise data tables.
• Ecosystem: Deep integration with Plotly's ecosystem (Plotly.js, Plotly Express, Kaleido for static exports). The Dash Enterprise platform adds authentication, job queues, and deployment management.

Dash's strength is production readiness. Its callback model gives developers fine-grained control over what updates and when, which matters for applications serving hundreds of concurrent users.

Learning Curve and Developer Experience

The developer experience gap between Streamlit and Dash is the most debated topic in this comparison. Here is a concrete example: building an interactive scatter plot with a dropdown filter.

Streamlit version (12 lines):

import streamlit as st
import pandas as pd
import plotly.express as px
 
df = pd.read_csv("sales_data.csv")
 
category = st.selectbox("Select category", df["category"].unique())
filtered = df[df["category"] == category]
 
fig = px.scatter(filtered, x="revenue", y="profit", color="region",
                 title=f"Revenue vs Profit: {category}")
st.plotly_chart(fig, use_container_width=True)

Dash version (28 lines):

import dash
from dash import html, dcc, callback, Input, Output
import pandas as pd
import plotly.express as px
 
df = pd.read_csv("sales_data.csv")
 
app = dash.Dash(__name__)
 
app.layout = html.Div([
    html.H3("Revenue vs Profit"),
    dcc.Dropdown(
        id="category-dropdown",
        options=[{"label": c, "value": c} for c in df["category"].unique()],
        value=df["category"].unique()[0]
    ),
    dcc.Graph(id="scatter-plot")
])
 
@callback(
    Output("scatter-plot", "figure"),
    Input("category-dropdown", "value")
)
def update_chart(selected_category):
    filtered = df[df["category"] == selected_category]
    return px.scatter(filtered, x="revenue", y="profit", color="region",
                      title=f"Revenue vs Profit: {selected_category}")
 
if __name__ == "__main__":
    app.run(debug=True)

The Streamlit version is about 40% shorter and reads almost like pseudocode. However, the Dash version makes the data flow explicit: inputs, outputs, and the transformation between them are all clearly declared. This explicitness becomes valuable when applications grow to 20+ interactive components.

Performance and Scalability

Performance differences between Streamlit and Dash stem from their architectural models.

Streamlit's rerun model: Every user interaction triggers a full script rerun. Streamlit mitigates this with @st.cache_data and the newer @st.fragment decorator (which enables partial reruns of specific sections). For a single user or small team, this works well. Under load, each connected user maintains a separate server-side session, consuming memory proportional to the data loaded.

Dash's callback model: Only the specific callback triggered by a user interaction executes. This is inherently more efficient for complex applications because unrelated components do not re-compute. Dash also supports clientside callbacks (JavaScript execution in the browser) for zero-latency interactions.

For dashboards serving fewer than 50 concurrent users, both frameworks perform well. Beyond that threshold, Dash's callback architecture provides a meaningful advantage. For very high concurrency, both frameworks benefit from horizontal scaling behind a load balancer.

UI Customization and Layout Control

Dash gives developers significantly more control over visual presentation.

Streamlit provides a set of layout primitives: st.columns, st.tabs, st.expander, st.sidebar, and st.container. Since version 1.36, the st.html component allows injecting raw HTML. Custom theming is limited to a config.toml file that controls primary color, background, and font. You cannot apply CSS classes to individual Streamlit widgets without workarounds.

Dash exposes the full HTML/CSS model. Every Dash component accepts className and style props. You can use Bootstrap (dash-bootstrap-components), Mantine (dash-mantine-components), or any CSS framework. Custom React components can be built with the Dash Component Boilerplate and published as Python packages.

Deployment Options

Both frameworks offer multiple deployment paths, but the options differ substantially.

Streamlit Community Cloud is a standout feature: free hosting for public apps directly from a GitHub repository. For enterprises, Streamlit in Snowflake provides managed deployment within the Snowflake ecosystem, including data governance and access controls.

Dash Enterprise (paid) offers workspace management, app deployment pipelines, authentication, and design kits. For teams not using Dash Enterprise, Dash apps deploy as standard Flask applications, which makes them straightforward to containerize and serve behind Nginx or Gunicorn.

Enterprise Features

Enterprise adoption requires authentication, role-based access, audit logging, and integration with corporate identity providers. Here is how the two frameworks compare:

Neither framework provides enterprise features in its open-source edition. Streamlit leverages Snowflake's security infrastructure for enterprise use, while Dash relies on Dash Enterprise (a paid product). If you need authentication in the open-source version, Dash has an advantage because it runs on Flask, so you can integrate any Flask-compatible authentication middleware.

Community and Ecosystem

Streamlit's community has grown faster since 2022, partly driven by the AI/LLM boom. Many LLM demo applications (RAG chatbots, agent interfaces, model playgrounds) are built with Streamlit because of its native chat elements (st.chat_message, st.chat_input). Dash's community skews more toward enterprise analytics and scientific visualization.

When to Choose Streamlit

Streamlit is the stronger choice when:

• You are prototyping or iterating rapidly. The script-based model means you can go from idea to working demo in under an hour.
• Your team is data scientists, not web developers. No HTML, CSS, or JavaScript knowledge is required.
• You are building AI/LLM applications. Native chat elements, easy integration with LangChain, and one-click deployment to Hugging Face Spaces.
• You want free hosting. Streamlit Community Cloud provides free deployment for public repositories.
• Your app serves fewer than 50 concurrent users. Internal tools, team dashboards, and personal projects.
• You are in the Snowflake ecosystem. Streamlit in Snowflake provides a managed, governed experience.

When to Choose Dash

Dash is the stronger choice when:

• You need fine-grained UI control. Pixel-perfect layouts, custom CSS, branded dashboards for client delivery.
• Your application has complex interactivity. Pattern-matching callbacks, clientside callbacks, and multi-output updates.
• You are serving many concurrent users. Dash's callback model is more memory-efficient under load.
• You need to embed dashboards in other products. Dash apps are standard Flask apps that can be mounted into existing web applications.
• Your organization uses Dash Enterprise. Built-in auth, deployment pipelines, and design systems.
• You need maximum charting capability. Plotly.js is Dash's native engine, providing the deepest integration.

Alternative Frameworks to Consider

Streamlit and Dash are not the only options. Several newer frameworks have gained traction:

Gradio is the strongest alternative if your primary use case is demonstrating machine learning models. It provides pre-built interface components for images, audio, text, and tabular data. Its deep integration with Hugging Face makes it the default choice for model demos.

Panel is worth considering if you work across multiple visualization libraries. Unlike Streamlit (which wraps charts in iframes) or Dash (which is Plotly-native), Panel natively renders Bokeh, Matplotlib, Plotly, Altair, and HoloViews objects.

Reflex targets developers who want to build full-stack web applications entirely in Python. It compiles to a Next.js frontend, which means you get React performance without writing JavaScript. It is better suited for general web apps than for data dashboards specifically.

Building the Same Dashboard: Streamlit vs Dash

To make the comparison concrete, here is a realistic sales dashboard built in both frameworks. This example loads data, creates filters, displays KPI metrics, and renders an interactive chart.

Streamlit version:

import streamlit as st
import pandas as pd
import plotly.express as px
 
st.set_page_config(page_title="Sales Dashboard", layout="wide")
st.title("Sales Dashboard")
 
# Load data
df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])
 
# Sidebar filters
with st.sidebar:
    st.header("Filters")
    regions = st.multiselect("Region", df["region"].unique(), default=df["region"].unique())
    date_range = st.date_input("Date range",
                               value=(df["order_date"].min(), df["order_date"].max()))
 
# Apply filters
mask = (df["region"].isin(regions)) & \
       (df["order_date"].between(pd.Timestamp(date_range[0]), pd.Timestamp(date_range[1])))
filtered = df[mask]
 
# KPI row
col1, col2, col3 = st.columns(3)
col1.metric("Total Revenue", f"${filtered['revenue'].sum():,.0f}")
col2.metric("Total Orders", f"{len(filtered):,}")
col3.metric("Avg Order Value", f"${filtered['revenue'].mean():,.2f}")
 
# Charts side by side
left, right = st.columns(2)
with left:
    fig_bar = px.bar(filtered.groupby("category")["revenue"].sum().reset_index(),
                     x="category", y="revenue", title="Revenue by Category")
    st.plotly_chart(fig_bar, use_container_width=True)
 
with right:
    fig_line = px.line(filtered.groupby("order_date")["revenue"].sum().reset_index(),
                       x="order_date", y="revenue", title="Revenue Over Time")
    st.plotly_chart(fig_line, use_container_width=True)

Dash version:

import dash
from dash import html, dcc, callback, Input, Output
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.express as px
 
df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])
 
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
 
app.layout = dbc.Container([
    html.H1("Sales Dashboard", className="my-3"),
    dbc.Row([
        dbc.Col([
            html.Label("Region"),
            dcc.Dropdown(id="region-filter", multi=True,
                         options=[{"label": r, "value": r} for r in df["region"].unique()],
                         value=df["region"].unique().tolist()),
        ], width=4),
        dbc.Col([
            html.Label("Date Range"),
            dcc.DatePickerRange(id="date-filter",
                                start_date=df["order_date"].min(),
                                end_date=df["order_date"].max()),
        ], width=4),
    ], className="mb-3"),
    dbc.Row(id="kpi-row"),
    dbc.Row([
        dbc.Col(dcc.Graph(id="bar-chart"), width=6),
        dbc.Col(dcc.Graph(id="line-chart"), width=6),
    ]),
], fluid=True)
 
@callback(
    Output("kpi-row", "children"),
    Output("bar-chart", "figure"),
    Output("line-chart", "figure"),
    Input("region-filter", "value"),
    Input("date-filter", "start_date"),
    Input("date-filter", "end_date"),
)
def update_dashboard(regions, start_date, end_date):
    filtered = df[df["region"].isin(regions or []) &
                  df["order_date"].between(start_date, end_date)]
    kpis = dbc.Row([
        dbc.Col(dbc.Card(dbc.CardBody([
            html.H6("Total Revenue"), html.H3(f"${filtered['revenue'].sum():,.0f}")
        ])), width=4),
        dbc.Col(dbc.Card(dbc.CardBody([
            html.H6("Total Orders"), html.H3(f"{len(filtered):,}")
        ])), width=4),
        dbc.Col(dbc.Card(dbc.CardBody([
            html.H6("Avg Order Value"), html.H3(f"${filtered['revenue'].mean():,.2f}")
        ])), width=4),
    ])
    fig_bar = px.bar(filtered.groupby("category")["revenue"].sum().reset_index(),
                     x="category", y="revenue", title="Revenue by Category")
    fig_line = px.line(filtered.groupby("order_date")["revenue"].sum().reset_index(),
                       x="order_date", y="revenue", title="Revenue Over Time")
    return kpis, fig_bar, fig_line
 
if __name__ == "__main__":
    app.run(debug=True)

The Streamlit version is about 30 lines; the Dash version is about 55 lines. Both produce a functional, interactive dashboard. The tradeoffs are clear: Streamlit requires less code and no knowledge of HTML component structure, while Dash gives you explicit control over layout, styling, and update logic.

Visualize DataFrames Interactively with PyGWalker

If you want interactive, Tableau-style data exploration inside Streamlit without writing chart code at all, PyGWalker (opens in a new tab) is worth considering. It turns any pandas DataFrame into a drag-and-drop visual analytics interface.

import streamlit as st
import pandas as pd
from pygwalker.api.streamlit import StreamlitRenderer
 
df = pd.read_csv("sales_data.csv")
 
renderer = StreamlitRenderer(df, spec="./gw_config.json", spec_io_mode="rw")
renderer.explorer()

This gives you a fully interactive chart builder embedded in your Streamlit app -- no Plotly or Matplotlib code needed. Users can drag fields to axes, change chart types, add filters, and explore data visually. PyGWalker supports Streamlit, Jupyter, and standalone HTML export.

For data scientists who want the notebook-to-app workflow with even less code, RunCell (opens in a new tab) provides an AI agent for Jupyter that can help generate dashboard code, clean data, and build visualizations through natural language instructions.

Frequently Asked Questions

1. Is Streamlit faster than Dash for development?

Yes, for initial development and prototyping. Streamlit's script-based model requires roughly 40-60% less code than equivalent Dash applications. However, for large applications with many interactive components, Dash's explicit callback model can be easier to maintain and debug over time.

2. Can Dash handle more concurrent users than Streamlit?

Generally yes. Dash's callback architecture is more memory-efficient because it does not maintain full script state per session. A single Dash instance can comfortably serve 100-200 concurrent users, compared to 30-50 for Streamlit. Both frameworks can scale horizontally with load balancing.

3. Which framework is better for machine learning demos?

Streamlit is the most popular choice for ML demos because of its simplicity, native chat elements for LLM apps, and one-click deployment to Hugging Face Spaces. However, Gradio is also an excellent option specifically designed for ML model interfaces.

4. Can I use Plotly charts in Streamlit?

Yes. Streamlit has first-class support for Plotly via st.plotly_chart(). You get the same interactive Plotly charts in both frameworks. The difference is that Dash integrates more deeply with Plotly's callback system for cross-filtering and linked views.

5. Is Streamlit free for commercial use?

Yes. Streamlit is licensed under Apache 2.0, which permits commercial use. Streamlit Community Cloud provides free hosting for public apps. For private deployments, you can self-host or use Streamlit in Snowflake (paid). Dash is MIT-licensed (also free for commercial use), with Dash Enterprise as the paid offering.

6. Should I switch from Dash to Streamlit (or vice versa)?

Only if your current framework is creating significant friction. If your Dash app works well in production, there is no reason to rewrite it in Streamlit. If your Streamlit prototype needs to scale to hundreds of users with complex interactivity, evaluating Dash or Reflex is worthwhile. Both frameworks are actively maintained and have strong roadmaps.

7. What about Streamlit vs Plotly -- are they competitors?

Not exactly. Plotly is a charting library; Dash is Plotly's application framework. Streamlit is an application framework that supports many charting libraries including Plotly. So the real comparison is Streamlit vs Dash. You can (and many people do) use Plotly charts inside Streamlit applications.

Conclusion

The Streamlit vs Dash decision comes down to your priorities. Streamlit wins on developer experience, speed of iteration, and the AI/LLM application ecosystem. Dash wins on UI customization, production scalability, and enterprise tooling.

For most data science teams building internal tools, prototypes, or ML demos, Streamlit is the practical default. For teams building client-facing dashboards, embedded analytics, or applications serving hundreds of concurrent users, Dash provides the control and performance you need.

Both frameworks are open source, actively maintained, and backed by well-funded companies. You are not making a bad choice either way -- you are choosing which tradeoffs align with your project's requirements.

Related Guides

• Polars vs Pandas -- choosing the right DataFrame library for your Streamlit or Dash data backend
• FastAPI vs Flask -- API framework comparison for building backends behind your dashboards
• Pandas Pivot Table -- reshape data for dashboard cross-tabulation views