Streamlit vs Dash in 2026: Which Python Dashboard Framework to Use

The two production-grade Python dashboard frameworks are Streamlit (acquired by Snowflake, now the default for data scientists) and Dash by Plotly (the long-established choice for enterprise analytics). They look similar from a distance - both turn pandas DataFrames into web apps without you writing JavaScript - but they differ in almost every architectural decision once you start building. This guide is the apples-to-apples comparison: the same KPI dashboard built in both frameworks, plus six axes of trade-off and a migration playbook for moving between them.

TL;DR

Two-line summary:

• Default to Streamlit. It ships faster, the API is friendlier, and ~80% of dashboards never need anything Dash gives you.
• Reach for Dash when you outgrow Streamlit's rerun model - fine-grained callbacks, AG Grid, multi-page enterprise apps, or when module-global state collisions start biting in production.

The Same KPI Dashboard, Both Frameworks

Both apps below load the same 50,000-row sample CSV (orders by date / region / product), filter on a region multiselect and a date range, and render two charts plus a KPI row. Same data, same UX, same Plotly. The code is what differs.

In Streamlit (~45 lines)

# streamlit_app.py
from pathlib import Path
import pandas as pd
import plotly.express as px
import streamlit as st

DATA = Path(__file__).parent / "data" / "sample_kpi_data.csv"
st.set_page_config(page_title="Revenue", layout="wide")


@st.cache_data
def load() -> pd.DataFrame:
    return pd.read_csv(DATA, parse_dates=["order_date"])


def main() -> None:
    df = load()

    with st.sidebar:
        st.title("Revenue")
        date_range = st.date_input(
            "Date range",
            (df["order_date"].min().date(), df["order_date"].max().date()),
        )
        regions = st.multiselect(
            "Regions",
            sorted(df["region"].unique()),
            default=sorted(df["region"].unique()),
        )

    start, end = date_range
    f = df[
        (df["order_date"].dt.date.between(start, end))
        & df["region"].isin(regions)
    ]

    cols = st.columns(3)
    cols[0].metric("Revenue", f"${f['revenue'].sum() / 1_000_000:.2f}M")
    cols[1].metric("Orders", f"{len(f):,}")
    cols[2].metric("Avg order value", f"${f['revenue'].mean():,.0f}")

    monthly = (
        f.assign(month=f["order_date"].dt.to_period("M").dt.to_timestamp())
        .groupby("month", as_index=False)["revenue"]
        .sum()
    )
    by_region = f.groupby("region", as_index=False)["revenue"].sum()

    left, right = st.columns(2)
    left.plotly_chart(
        px.line(monthly, x="month", y="revenue", title="Revenue by month"),
        use_container_width=True,
    )
    right.plotly_chart(
        px.bar(by_region, x="region", y="revenue", title="Revenue by region"),
        use_container_width=True,
    )


if __name__ == "__main__":
    main()

Run: streamlit run streamlit_app.py. Open http://localhost:8501.

In Dash (~80 lines)

# app.py
from dash import Dash, dcc, html, Input, Output, callback
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.express as px

df = pd.read_csv("data/sample_kpi_data.csv", parse_dates=["order_date"])

app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server  # for gunicorn

app.layout = dbc.Container([
    dbc.Row([dbc.Col(html.H1("Revenue"), width=12)], class_name="my-3"),

    dbc.Row([
        dbc.Col([
            html.Label("Date range"),
            dcc.DatePickerRange(
                id="date-range",
                start_date=df["order_date"].min(),
                end_date=df["order_date"].max(),
            ),
        ], width=6),
        dbc.Col([
            html.Label("Regions"),
            dcc.Dropdown(
                id="region-filter",
                options=sorted(df["region"].unique()),
                value=sorted(df["region"].unique()),
                multi=True,
            ),
        ], width=6),
    ], class_name="mb-3"),

    dbc.Row([
        dbc.Col(html.Div(id="kpi-revenue"), width=4),
        dbc.Col(html.Div(id="kpi-orders"), width=4),
        dbc.Col(html.Div(id="kpi-aov"), width=4),
    ], class_name="mb-3"),

    dbc.Row([
        dbc.Col(dcc.Graph(id="chart-monthly"), width=6),
        dbc.Col(dcc.Graph(id="chart-region"), width=6),
    ]),
], fluid=True)


@callback(
    Output("kpi-revenue", "children"),
    Output("kpi-orders", "children"),
    Output("kpi-aov", "children"),
    Output("chart-monthly", "figure"),
    Output("chart-region", "figure"),
    Input("region-filter", "value"),
    Input("date-range", "start_date"),
    Input("date-range", "end_date"),
)
def update(regions, start, end):
    f = df[df["region"].isin(regions) & df["order_date"].between(start, end)]
    monthly = (
        f.assign(month=f["order_date"].dt.to_period("M").dt.to_timestamp())
        .groupby("month", as_index=False)["revenue"]
        .sum()
    )
    by_region = f.groupby("region", as_index=False)["revenue"].sum()
    return (
        f"Revenue: ${f['revenue'].sum() / 1_000_000:.2f}M",
        f"Orders: {len(f):,}",
        f"AOV: ${f['revenue'].mean():,.0f}",
        px.line(monthly, x="month", y="revenue", title="Revenue by month"),
        px.bar(by_region, x="region", y="revenue", title="Revenue by region"),
    )


if __name__ == "__main__":
    app.run(debug=True)

Run: python app.py. Open http://127.0.0.1:8050.

Side-by-side observations

• Streamlit is ~45 lines, Dash is ~80. The roughly 2× LOC ratio holds across every dashboard I've ever built in both.
• Streamlit has zero callbacks. The script reruns top-to-bottom on every interaction; you write straight-line code that just works.
• Dash has one callback that batches all five outputs. This is the right Dash pattern - multiple outputs in one callback rather than one callback per output, so the browser does one round-trip per user action.
• Dash needs server = app.server to expose the Flask underneath for gunicorn. Streamlit doesn't (its CLI handles serving).
• Dash's layout is React-flavoured. dbc.Container > Row > Col instead of with st.sidebar: blocks. More familiar to React engineers, more verbose to Python engineers.

Six-Axis Deep Dive

1. Time to first dashboard

Streamlit wins, decisively. A pandas-fluent developer can have a working Streamlit dashboard in an hour. The same person needs a half-day to a day on Dash, mostly because the layout-then-callback mental model takes longer to internalise than top-to-bottom imperative code.

For prototyping (the scenario where you want to show stakeholders something today), Streamlit is hard to beat.

2. Customization ceiling

Dash wins, eventually. Streamlit's component palette is fixed: KPI metrics, tables, charts, sidebar, columns, tabs, expanders - that's most of it. You can write custom components but the API is undocumented and brittle. Dash sits on top of arbitrary HTML + Plotly, so you can do anything you can do in HTML/CSS - Bootstrap layouts, Mantine themes, custom JavaScript callbacks via clientside_callback, the works.

For 80% of dashboards, Streamlit's built-ins are enough. For the other 20%, Dash's escape hatches matter.

3. Performance with 50k+ rows

Roughly tied at the data layer; Dash wins at the table layer. Both run pandas the same way; both render Plotly the same way. The difference is:

• Streamlit's st.dataframe uses Apache Arrow + a glide-rendered table that handles ~10k rows well, melts past ~50k.
• Dash's dash-table is similar - fine to ~10k, hurts past that.
• Dash AG Grid (pip install dash-ag-grid) handles 100k+ rows with sort/filter/pivot/group, no browser strain. There is no equivalent in pure Streamlit; the closest is streamlit-aggrid (third-party) which works but is less actively maintained.

If your dashboard's centre-of-gravity is a big table, Dash + AG Grid is the better path. If it's charts and KPIs, both perform identically.

4. Multi-user session handling

Dash wins. Streamlit's st.session_state is per-session, but module-level globals (df = pd.read_csv(...) at the top of the file) are shared across all sessions in the same process. Mutate one and every user sees the mutation. This is a common production bug - you write what looks like clean code, deploy it, and one user's filter selection leaks into another user's view.

Dash's callbacks are stateless by design - each callback receives only the input values it declared. Per-user state lives in dcc.Store components scoped to the user's browser session. Cleaner separation, fewer cross-user surprises.

If you're shipping to production with multi-user traffic, this is the #1 reason to consider Dash over Streamlit.

5. Deployment story

Streamlit wins for hobby/internal; both are even for production.

• Streamlit: push to GitHub → Streamlit Community Cloud free tier → click Deploy. For Hugging Face Spaces, same one-click. Production: Docker + gunicorn-equivalent (streamlit run is not multi-process - scale horizontally with multiple instances).
• Dash: gunicorn + Render / Fly.io / your own Kubernetes. Or pay for Dash Enterprise for managed deployment with SSO, RBAC, and version control. The "click Deploy" experience is slightly more friction than Streamlit, but production scaling is cleaner because Dash is a normal Flask app.

For internal team dashboards, Streamlit's deployment story is unambiguously easier. For multi-tenant production traffic, neither has a particularly elegant story without paid tiers.

6. Ecosystem and component libraries

Dash wins on enterprise components; Streamlit wins on community/AI integrations.

• Dash has Dash AG Grid, Dash Mantine Components, Dash Bootstrap, Dash Cytoscape (graph viz), Dash Bio, Dash DAQ (instrumentation gauges), Dash Enterprise (SSO + RBAC), and a mature Plotly Enterprise consultancy ecosystem.
• Streamlit has streamlit-extras, streamlit-authenticator, streamlit-aggrid (third-party AG Grid wrapper), and a huge gallery of LangChain / LLM-shaped community components on Hugging Face.

For enterprise dashboards, Dash's component ecosystem is broader and deeper. For AI/LLM dashboards, Streamlit (and increasingly Gradio) is where the community lives.

When to Choose Neither

Both frameworks are great for what they're designed for, but neither is the right answer for every dashboard:

• AI/LLM dashboards (chat UIs, model demos): use Gradio instead. It's specifically shaped for this and renders better on Hugging Face Spaces.
• Pure-Python full-stack apps that need React-grade frontend behaviour: Reflex (formerly Pynecone) lets you write the whole stack in Python and compiles to React under the hood. Smaller community, sharper edges, but the right tool for some niches.
• Customer-facing multi-tenant SaaS analytics: building it in Streamlit / Dash is rarely the right call. You'll spend 4–8 weeks on the auth + RBAC + multi-tenancy + exports + scheduled-email layer that an embedded analytics platform (Databrain, Metabase, Cube) gives you out of the box. See Embedded Analytics in Python.
• Static reports (PDF, weekly email): use Quarto or a Jupyter-based pipeline. A live dashboard framework is overkill for "render to PDF, email it weekly".

Migration Playbook

Streamlit → Dash

You'll do this when:

• Module-global state collisions are biting in production (failure mode 2 from the Python dashboard guide)
• You need AG Grid for tables past 50k rows
• You need fine-grained "this filter only updates these three charts" control
• You want per-user RBAC enforced at the callback level

The migration is largely mechanical:

1. Lift each Streamlit widget to a Dash component: st.selectbox → dcc.Dropdown, st.slider → dcc.Slider, st.date_input → dcc.DatePickerRange, st.metric → a styled html.Div, st.plotly_chart → dcc.Graph.
2. Wrap your render logic in @callback with Input(...) for each filter and Output(...) for each chart/KPI.
3. Replace @st.cache_data with flask_caching or in-memory dicts keyed by callback inputs.
4. Replace st.session_state with dcc.Store components scoped to the browser session.
5. Wire up gunicorn: add server = app.server to app.py, write a one-line Dockerfile.

Budget: a 200-line Streamlit dashboard takes 3–5 days to faithfully port to Dash by an engineer who knows both.

Dash → Streamlit

Less common, but happens when:

• The Dash app has grown into a callback graph that's hard to reason about
• The team has shifted from a frontend-savvy engineer to data scientists who'd rather write straight-line Python
• The app is moving from customer-facing to internal-only (so you can give up Dash's multi-user state isolation)

The migration:

1. Flatten the layout: dbc.Container > Row > Col → st.columns(). Streamlit's layout is much simpler - most of the verbosity disappears.
2. Collapse callbacks into top-down imperative code. A callback that filters a DataFrame and produces three charts becomes 10 lines of straight-line Streamlit.
3. Wrap data loading in @st.cache_data. This is the single most important step - without it, every filter change reparses your data.
4. Move per-user state into st.session_state. Anything that should survive across reruns goes here.
5. Drop gunicorn. streamlit run app.py is your new entry point.

Budget: a 500-line Dash app collapses to 200 lines of Streamlit in 2–3 days, often with a clearer architecture.

A Quick Decision Tree

• Building an AI/LLM demo or chat UI? → Gradio.
• Customer-facing, multi-tenant, inside a SaaS product? → Embed an analytics platform, don't build.
• Internal dashboard, want to ship today? → Streamlit.
• Enterprise app with fine callback control, big tables, or strict per-user state isolation? → Dash.
• Have a working Streamlit app that's hitting state collisions in production? → Migrate to Dash (5-step playbook above).
• Have a Dash app that nobody on the team enjoys maintaining? → Migrate to Streamlit (5-step playbook above).

Next Steps

• Build a Python dashboard from scratch: Python Dashboard: The Complete 2026 Guide - full Streamlit walkthrough, Dash 3 walkthrough, Gradio mini-section, deployment, and "what actually breaks in production"
• Pick a chart library: 10 Best Python Chart Libraries for Dashboards - Plotly, Matplotlib, Bokeh, Altair, ECharts, Seaborn compared
• For customer-facing multi-tenant analytics: Embedded Analytics in Python - when DIY breaks down and you want token-scoped RLS out of the box
• The build vs. buy math: Embedded analytics build vs. buy
• Clone the working code: python-tutorial-scratch (Streamlit version of the dashboard above, runnable in three commands)

Covers Streamlit 1.55, Dash 3.x and 4.x, Plotly 6.x, pandas 3.0, Python 3.13. Last updated April 2026.

Rahul Pattamatta is co-founder of Databrain, an embedded analytics platform for SaaS.