Streamlit vs Plotly Dash: Which Is Better for Building Data Apps?

Name

Maryam Alavi

Updated on 11/14/2025

Building data apps doesn’t have to be like pulling teeth. With tools like Streamlit and Plotly Dash, you can go from notebook to interactive dashboard in surprisingly little time.

But when you’re starting a new project, the big question is:

Streamlit vs Dash – which one should I choose for my data app?

In this guide, we’ll compare them across:

• Ease of use
• Customization & UI flexibility
• Performance & scalability
• Deployment & ecosystem
• Real-world use cases

By the end, you’ll have a practical mental model for choosing the right tool for your next dashboard or internal tool.

TL;DR – Quick Comparison

If you’re in a rush, here’s the short version:

If you want something running today to show to stakeholders → Streamlit.
If you’re building a long-lived, enterprise-grade, highly customized dashboard → Dash.

Introduction to Streamlit and Plotly Dash

Before we dive into the details, let’s quickly recap what each framework is trying to do.

What is Streamlit?

Streamlit is an open-source Python library designed to make it dead simple to turn Python scripts into web apps.

Key ideas:

• You write plain Python, top to bottom.
• You sprinkle in st.* functions to create UI (sliders, buttons, charts, etc.).
• Streamlit handles the frontend, state, and reruns automatically.

It’s especially popular among:

• Data scientists working in notebooks
• ML engineers sharing model demos
• Teams building internal tools without front-end engineers

A minimal Streamlit app looks like this:

import streamlit as st
import pandas as pd
 
st.title("Sales Dashboard")
 
df = pd.read_csv("sales.csv")
st.write("Raw data")
st.dataframe(df)
 
region = st.selectbox("Select region", df["region"].unique())
filtered = df[df["region"] == region]
 
st.line_chart(
    filtered.set_index("date")["revenue"],
    use_container_width=True,
)

Save as app.py and run:

streamlit run app.py

You instantly get a clean, interactive web app.

What is Plotly Dash?

Plotly Dash is a web application framework built on top of:

• Flask (backend)
• React.js (frontend)
• Plotly.js (visualizations)

Dash gives you fine-grained control over the UI and interactions through:

• A declarative layout
• A callback system that ties components together
• A component ecosystem (core components, HTML components, DataTable, and more)

A minimal Dash app:

from dash import Dash, html, dcc, Input, Output
import plotly.express as px
import pandas as pd
 
df = pd.read_csv("sales.csv")
 
app = Dash(__name__)
 
app.layout = html.Div([
    html.H1("Sales Dashboard"),
    dcc.Dropdown(
        id="region",
        options=[{"label": r, "value": r} for r in df["region"].unique()],
        value=df["region"].unique()[0],
        clearable=False,
    ),
    dcc.Graph(id="revenue-chart"),
])
 
@app.callback(
    Output("revenue-chart", "figure"),
    Input("region", "value"),
)
def update_chart(region):
    filtered = df[df["region"] == region]
    fig = px.line(filtered, x="date", y="revenue", title=f"Revenue in {region}")
    return fig
 
if __name__ == "__main__":
    app.run_server(debug=True)

Dash requires more structure than Streamlit, but in return you get more control over layout, styling, and application behavior.

Ease of Use: Lowering the Barrier of Entry

Building a dashboard doesn’t have to feel like rocket science. But the learning curve matters a lot, especially for teams without dedicated frontend engineers.

Streamlit’s Straightforward Syntax

Streamlit feels like “notebook code + UI sprinkles”.

• You write Python top-to-bottom.
• There’s no need to think about routing, requests, or front-end frameworks.
• The mental model is: “When the user interacts, Streamlit reruns the script.”

Strengths:

• Very low friction for data scientists
• Rapid iteration – add a widget, hit save, see it live
• Great for hackathons, prototypes, and “I need this by tomorrow” demos

You can also add interactive widgets like sliders, checkboxes, and file uploaders with a single line each:

uploaded_file = st.file_uploader("Upload a CSV", type="csv")
if uploaded_file:
    df = pd.read_csv(uploaded_file)
    st.dataframe(df)

Plotly Dash’s Deeper Learning Curve

Dash gives you more power, but at the cost of a more explicit structure:

• You define a layout tree using html.* and dcc.* components.
• You glue components together with callbacks that declare Input and Output.
• You think in terms of “when this input changes, update that output.”

This requires:

• Some understanding of web app concepts (request/response, component tree)
• Comfort with working in a more “framework-y” style

However, once you get used to Dash’s model, it becomes a very solid foundation for complex apps that need predictable behavior and fine control.

Summary on Ease of Use:

• If you want fast results with minimal boilerplate, Streamlit wins.
• If you’re okay with a bit more structure for better long-term control, Dash is a good fit.

Customization & UI Flexibility: How Much Control Do You Need?

Both frameworks can produce clean dashboards, but they differ in how deeply you can customize the UI.

Streamlit’s Sweet Simplicity

Streamlit’s layout and widgets are intentionally opinionated:

• Built-in components for text, inputs, charts, and media
• Simple layout APIs like st.columns, st.tabs, and st.expander
• Easy integration with plotting libraries: Plotly, Matplotlib, Altair, etc.

Example with layout:

col1, col2 = st.columns(2)
 
with col1:
    st.metric("Total Revenue", "$1.2M")
with col2:
    st.metric("Active Users", "3,240")

You can do quite a lot with this, but:

• You don’t control every pixel.
• Custom animations, complex multi-level nav, or pixel-perfect designs are harder.
• True custom components are possible via the Streamlit Components API, but that’s extra work.

Streamlit is ideal if you’re okay with a polished, opinionated UI that “just works” rather than something heavily branded and bespoke.

Plotly Dash’s Granular Control

Dash is built on React, and its component model reflects that:

• You compose the UI using HTML-like components (html.Div, html.H1, etc.).
• You can use external CSS frameworks (Bootstrap, Tailwind, custom CSS).
• You can build or plug in fully custom components in React.

This means:

• You can create highly customized, brand-aligned dashboards.
• You can implement advanced layouts: multi-page apps, complex navigation, persistent sidebars, etc.
• You can control interactions at a very granular level through callbacks.

If your app must match a design system or live alongside other web products, Dash gives you more flexibility out of the box.

Performance & Scalability: Handling the Heat

Both frameworks can feel snappy for small apps, but things look different at scale.

Streamlit Performance and Scalability

Streamlit uses WebSockets to push updates to the browser. The app “reruns” the script from top to bottom whenever the user interacts.

This is fine for:

• Small to medium dashboards
• Prototypes and internal tools
• Light to moderate traffic

For heavier workloads, you need to pay attention to:

• Caching expensive computations using the newer APIs:

import streamlit as st
 
@st.cache_data
def load_data(path):
    # Expensive I/O or computation here
    ...
 
@st.cache_resource
def get_model():
    # Load ML model once and reuse
    ...

• Avoiding unnecessary recomputations
• Moving heavy logic to background jobs / APIs when traffic grows

Streamlit can handle production workloads, but you need to architect around the “rerun” model and be mindful of state.

Plotly Dash’s Scalability

Dash is built on Flask and integrates naturally with the broader Python web ecosystem:

• Run behind Gunicorn, uWSGI, or other WSGI servers
• Containerize via Docker and scale horizontally
• Integrate with existing authentication, logging, and monitoring

Dash’s callback model fits well with:

• Larger, multi-user applications
• Apps that need finer control over resource usage
• Architectures where Dash is one piece of a larger system (microservices, etc.)

Components like Dash DataTable and advanced Plotly visualizations are designed to work with large datasets, server-side callbacks, and pagination.

Performance summary:

• Streamlit: fast to build, good for light to medium workloads, requires thoughtful caching for heavier apps.
• Dash: more work upfront, but naturally aligns with traditional scaling patterns for web apps.

Deployment & Ecosystem

You’ve built something great. How easy is it to share with your team or the world?

Deploying Streamlit Apps

Popular options:

• Streamlit Community Cloud – simple, free-ish way to deploy apps directly from GitHub.
• Hugging Face Spaces – very easy for demos and public apps.
• Docker + your favorite cloud – package your app into a container and deploy on AWS, GCP, Azure, etc.
• Traditional servers / internal infrastructure.

The deployment story is very friendly for data teams who don’t want to manage a complex web stack.

Deploying Dash Apps

Dash apps are “just” Flask apps with extra magic:

• You can deploy on any infrastructure that runs Python/Flask.
• Use Docker, Kubernetes, or traditional application servers.
• Integrate with corporate SSO, reverse proxies, and existing APIs.

For larger organizations, Dash Enterprise (commercial offering) adds:

• Authentication
• User management
• App galleries
• One-click deployment tools

If you already have web infrastructure and DevOps practices, Dash slides nicely into that ecosystem.

Community and Support

Streamlit Community

• Vibrant, fast-growing community of data scientists and ML folks.
• Lots of blog posts, tutorials, and example apps.
• Active Discord/forum support.
• Ecosystem of Streamlit Components built by the community.

Plotly Dash Community

• Longer history, especially in enterprise and BI settings.
• Strong official documentation and examples.
• Plotly forums are very active, especially around complex visualizations.
• Commercial support available via Plotly for organizations that need SLAs.

Both are well-supported; the “feel” of the communities differs slightly:

• Streamlit skews towards data science prototyping & AI demos.
• Dash skews towards analytics engineering & production dashboards.

Real-World Applications: When to Choose What

Let’s make this concrete.

Use Cases for Streamlit

Choose Streamlit when you want to:

1. Build quick prototypes Need something fast to show stakeholders, validate an idea, or get feedback? Streamlit is ideal.

2. Explore and share data interactively Turn your notebook into a small app to let others filter, drill down, and visualize data.

3. Demo machine learning models Upload a CSV, call a model, visualize predictions, or expose sliders for hyperparameters.

4. Create lightweight internal tools Need a simple KPI dashboard or form-like app for a small team? Streamlit gets you there with minimal friction.

Use Cases for Plotly Dash

Choose Dash when you need to:

1. Build complex, multi-page dashboards You have multiple tabs, views, and cross-filtering between charts.

2. Support enterprise workflows Authentication, role-based access, integration with internal systems, consistent design system.

3. Create highly customized UIs You care deeply about branding, layout, and custom interactions that go beyond standard widgets.

4. Scale as part of a larger web platform Your Dash app lives behind load balancers, talks to other services, and needs to follow your org’s web standards.

FAQ: Streamlit vs Plotly Dash

Is Streamlit easier than Dash?

Yes. Streamlit is generally easier to learn because it feels like regular Python scripting with some extra functions. Dash requires you to think in terms of layouts and callbacks, which is slightly more advanced but more powerful.

Which is better for enterprise apps?

For enterprise-grade, long-lived apps with complex requirements, Dash often wins because of:

• Integration with existing web stacks
• Dash Enterprise (for organizations that want commercial support)
• Strong alignment with traditional web deployment patterns

That said, many companies successfully use Streamlit in production too.

Which is better for machine learning demos?

Streamlit is usually the better choice for ML demos and experimentation:

• Faster to go from model to UI
• Easy integration with file upload, charts, and text
• Simple to share with non-technical stakeholders

Can I use Plotly charts in Streamlit?

Yes. You can directly use Plotly figures in Streamlit:

import streamlit as st
import plotly.express as px
 
fig = px.scatter(...)
st.plotly_chart(fig, use_container_width=True)

So if you like Plotly’s visualizations but prefer Streamlit’s simplicity, you don’t have to choose.

Conclusion: The Final Verdict

There’s no absolute winner in the Streamlit vs Plotly Dash debate. Instead, each shines in different scenarios:

• Pick Streamlit if you value:

  • Speed of development
  • Low learning curve
  • Simple deployment and sharing
  • Prototypes, data exploration, ML demos

• Pick Plotly Dash if you value:

  • Fine-grained UI and layout control
  • Multi-page, complex dashboards
  • Enterprise integration and scalability
  • Long-term, production-facing applications

Ultimately, both are fantastic tools. Many teams even use both:

• Streamlit for quick experiments and internal tools
• Dash for flagship dashboards and enterprise-facing apps

Whichever you choose, you’re in good hands. Happy app building!