If you've worked with Streamlit, you know the magic of building data apps in pure Python without touching HTML, CSS, or JavaScript. But what if you're in the .NET ecosystem?
Enter Ivy - a framework that brings that same developer experience to C#, with some compelling differences that make it worth a closer look.
The Core Philosophy
Both Streamlit and Ivy share a fundamental belief: developers shouldn't need to be full-stack experts to build interactive web applications. They achieve this through server-side rendering with real-time WebSocket communication to a provided front-end renderer. Both are cross-platform and support hot-reloading.
But here's where they diverge:
| Aspect | Streamlit | Ivy |
|---|---|---|
| Language | Python | C# |
| Target Use Case | Data apps, ML prototypes, dashboards | Enterprise internal tools, back-office systems, customer portals |
| Architecture | Script-based execution model | React-inspired component model |
| State Model | Top-to-bottom re-execution | Reactive hooks (UseState, UseEffect) |
Developer Experience: A Tale of Two Paradigms
Streamlit's Approach
Streamlit runs your Python script top-to-bottom on every interaction. State is managed through st.session_state:
import streamlit as st
if 'count' not in st.session_state:
st.session_state.count = 0
st.title("Counter App")
if st.button("+1"):
st.session_state.count += 1
st.write(f"Count: {st.session_state.count}")
Ivy's Approach
Ivy uses React-style hooks and a component-based architecture:
[App]
public class CounterApp : ViewBase
{
public override object? Build()
{
var count = UseState(0);
return Layout.Vertical()
| Text.H1("Counter App")
| new Button("+1", onClick:
_ => count.Set(count.Value + 1))
| $"Count: {count.Value}";
}
}
If you've used React, Ivy feels immediately familiar. The UseState, UseEffect, and UseService hooks work exactly as you'd expect.
State Management: Script vs. Components
This is perhaps the most significant architectural difference.
Streamlit re-runs your entire script on every user interaction. This is brilliantly simple for beginners, but can become problematic for complex applications. Every variable outside session_state resets, and execution order matters enormously.
Ivy only re-renders components when their state changes. State is scoped to views, preserved across re-renders, and survives hot reloads during development. You get fine-grained control:
public override object? Build()
{
var searchTerm = UseState("");
var results = UseState<List<User>>([]);
// Effect runs only when searchTerm changes
UseEffect(() => {
results.Set(SearchUsers(searchTerm.Value));
}, [searchTerm]);
return Layout.Vertical()
| searchTerm.ToTextInput(placeholder: "Search...")
| results.Value.ToTable();
}
Widget Libraries: Different Priorities
Streamlit is primarily built for computer scientists and shines in data visualization and ML workflows:
- Native chart support (Altair, Plotly, Matplotlib)
- st.dataframe for exploring pandas DataFrames
Ivy is designed to be the perfect framework for building internal tools and prioritizes enterprise UI patterns:
- Comprehensive form inputs with validation
- DataTable with sorting, AI-enabled filtering, and pagination
- Graphs and other building blocks to build dashboards
- Kanban boards
- Chat interfaces for AI applications
Integrations
Streamlit has Streamlit Community Cloud and Snowflake integration, but enterprise authentication and complex deployments typically require additional infrastructure work.
Ivy is explicitly designed for internal tools at scale:
Database Integrations: Native support for SQL Server, PostgreSQL, MySQL, Oracle, Snowflake, ClickHouse, BigQuery, and more - all through Entity Framework Core.
Authentication: Built-in support for Microsoft Entra ID, Supabase Auth, and Basic Auth (more is coming).
Deployment: One-command Docker deployment to AWS, Azure, or GCP.
The Bottom Line
Streamlit democratized data app development for Python developers. Ivy does the same for the .NET ecosystem—but with a more sophisticated component model that scales better for complex enterprise applications.
If you're building a quick data dashboard or ML prototype, Streamlit remains excellent. If you're building internal tools that need enterprise authentication, complex forms, and long-term maintainability, Ivy offers a compelling alternative that doesn't require learning a separate frontend framework.
Both frameworks prove the same point: you don't need to be a full-stack expert to build production-quality web applications. The best choice depends on your ecosystem, your team's skills, and the complexity of what you're building.
Ready to try Ivy? Get started in under a minute
First, make sure you have installed the .NET 9 SDK.
dotnet tool install -g Ivy.Console
ivy init --hello
dotnet watch
Resources: