Shiny vs Streamlit for Data Apps in 2026: Which Should You Use?

Building data applications used to mean learning Flask or Django, or hiring a frontend developer. Today, Streamlit and Shiny let you go from data to deployed app in hours, not weeks.

This guide compares both frameworks as they stand in 2026, helping you decide which to use for your next data application.

The Short Answer

Use Shiny if you work in R. Use Streamlit if you work in Python. This is the simplest way to think about it, but the reality has some nuance.

Shiny has been around since 2012 and remains the go-to for R users who need interactive web applications. Streamlit, released in 2019 and acquired by Snowflake in 2022, has become the dominant choice for Python data scientists building quick prototypes and production apps.

Both frameworks let you write pure R or Python code—no HTML, CSS, or JavaScript required. But they differ in architecture, flexibility, and ecosystem.

Core Architecture

Shiny

Shiny is a full web application framework built specifically for R. It uses a reactive programming model where you define UI and server as separate but connected pieces:

library(shiny)

ui <- fluidPage(
  sliderInput("bins", "Number of bins:", min = 1, max = 50, value = 30),
  plotOutput("distPlot")
)

server <- function(input, output) {
  output$distPlot <- renderPlot({
    hist(faithful$eruptions, breaks = input$bins, col = "darkgray", border = "white")
  })
}

shinyApp(ui, server)

The reactive system automatically tracks dependencies and re-runs code only when inputs change. This is powerful but has a learning curve—you need to understand reactive expressions, observers, and the dependency graph.

Streamlit

Streamlit takes a different approach. It runs a top-to-bottom script every time an interaction happens:

import streamlit as st
import pandas as pd
import numpy as np

st.title("Histogram Demo")

bins = st.slider("Number of bins", min_value=1, max=50, value=30)

hist_values = np.histogram(faithful["eruptions"], bins=bins)[0]

st.bar_chart(hist_values)

Every time the user changes the slider, Streamlit re-runs the entire script from top to bottom. This is simpler to understand—no reactive graph to learn—but can be slower for complex apps because it re-runs everything.

What You Get Out of the Box

Shiny Ecosystem

Shiny comes with the shiny package, but its real power is the ecosystem around it:

• shinyjs — Add JavaScript interactions
• bslib — Bootstrap 5 theming built-in
• shinylive — Run Shiny in the browser without a Python/R server
• posit.co cloud — Free hosting with minimal setup
• Shiny for Python — A Python version of Shiny that mirrors the R package

The R ecosystem has hundreds of packages that integrate with Shiny—DT for DataTables, leaflet for maps, plotly for interactive charts. If you already use R for analysis, Shiny connects easily.

Streamlit Ecosystem

Streamlit has a growing but younger ecosystem:

• stlite components — Community-built components
• snowflake — Enterprise features from the parent company
• Streamlit Community Cloud — Free hosting
• Python integration — Works with pandas, scikit-learn, PyTorch, LangChain, and most Python data tools

Streamlit’s Python-native feel means it works naturally with the broader Python data stack. If you use scikit-learn, PyTorch, or LangChain, Streamlit fits your workflow.

Building Real Applications

State Management

Shiny stores state in reactiveValues(), giving you explicit control over what gets saved and when:

server <- function(input, output, session) {
  values <- reactiveValues(counter = 0)
  
  observeEvent(input$increment, {
    values$counter <- values$counter + 1
  })
  
  output$count <- renderText({
    paste("Count:", values$counter)
  })
}

Streamlit stores state in st.session_state, which works similarly but feels more like a dictionary:

if "counter" not in st.session_state:
    st.session_state.counter = 0

if st.button("Increment"):
    st.session_state.counter += 1

st.write(f"Count: {st.session_state.counter}")

Both approaches work, but Shiny’s reactive model can feel more natural for complex state machines, while Streamlit’s session_state is easier for simple cases.

Layout and Styling

Shiny’s fluidPage(), sidebarLayout(), and bslib theming give you structured layouts:

fluidPage(
  theme = bslib::bs_theme(bootswatch = "cerulean"),
  titlePanel("My App"),
  sidebarLayout(
    sidebarPanel(sliderInput("bins", "Bins", 1, 50, 30)),
    mainPanel(plotOutput("dist"))
  )
)

Streamlit uses a column-based layout with st.columns() and container options:

st.title("My App")
col1, col2 = st.columns([1, 3])
with col1:
    bins = st.slider("Bins", 1, 50, 30)
with col2:
    st.histogram(data, bins=bins)

Streamlit’s approach is faster to write for simple layouts, but Shiny gives you more control over complex nested layouts.

Data Caching

Both frameworks cache data to avoid re-computation:

Shiny:

data <- reactive({
  Sys.sleep(2)  # Simulate slow read
  read.csv("data.csv")
})

Streamlit:

@st.cache_data
def load_data():
    time.sleep(2)  # Simulate slow read
    return pd.read_csv("data.csv")

Streamlit’s decorator syntax is more explicit. Shiny’s reactive caching is more automatic but can be harder to debug.

Performance Considerations

For simple apps, both frameworks feel snappy. For production apps with many users:

Aspect	Shiny	Streamlit
Startup time	Slower (R runtime)	Faster (Python runtime)
Re-render strategy	Reactive graph	Full script re-run
Memory	R’s memory model	Python’s memory model
Scaling	Posit Connect handles well	Snowflake handles well
Debugging	R tools	Python tools

Shiny re-runs only what changed. Streamlit re-runs everything but has optimized caching. For compute-heavy apps, Shiny’s fine-grained reactivity can be more efficient.

Deployment

Both frameworks deploy to hosted platforms with minimal configuration:

Shiny → Posit Connect, Shiny Server, any Docker container
Streamlit → Streamlit Community Cloud, Snowflake, any Docker container

If you are already in the Posit/RStudio ecosystem, Shiny deployment is seamless. If you are in the Python/Snowflake world, Streamlit fits naturally.

When to Choose Shiny

1. You work primarily in R — Native R integration is smoother
2. You need fine-grained reactivity — Complex interactive dashboards benefit from Shiny’s reactive graph
3. Your team uses the tidyverse — dplyr, tidyr, ggplot2 integrate naturally
4. You need advanced customization — Shiny gives you more control over the frontend
5. You want Shiny for Python — The Python version is maturing and offers similar functionality

When to Choose Streamlit

1. You work primarily in Python — Native Python experience
2. You want the fastest path from script to app — Streamlit’s simple paradigm is quick
3. You use scikit-learn, PyTorch, or LangChain — Python ML ecosystem integration
4. You need to share prototypes fast — Community Cloud is free and instant
5. Your team knows Python but not R — No R knowledge required

Hybrid Approaches

A few teams use both:

• Shiny for Python — The Python implementation of Shiny lets R-familiar teams work in Python
• Shiny to Streamlit bridging — Some projects render Shiny components inside Streamlit using st.rshiny()
• microservices — Build Streamlit frontends that call R APIs

Most teams pick one and stick with it. The interoperability exists but adds complexity.

What You Should Do

1. If you are in R: start with Shiny. The ecosystem and deployment story are mature.
2. If you are in Python: start with Streamlit. The fastest path to a working app.
3. If you need to compare for a project decision: Build a small prototype in both and test with real users.
4. If you might need both: Design your data processing as separate services that either framework can call.
5. Do not overthink it. Both frameworks work well. The choice matters less than shipping your app.

In 2026, both Shiny and Streamlit are production-ready. Your language of choice should be the primary decision factor.

See Also

• shiny-getting-started — Build your first Shiny app
• r-plumber-api — Build REST APIs with plumber
• r-shiny-modules — Organise large Shiny apps