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Streamsync VS Streamlit

Compare Streamsync VS Streamlit and see what are their differences

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Streamsync logo Streamsync

Streamsync is an open-source framework for creating data apps. Build user interfaces using a visual editor; write the backend code in Python.

Streamlit logo Streamlit

Turn python scripts into beautiful ML tools
  • Streamsync Landing page
    Landing page //
    2023-05-04

It's fast.

  • Streamsync enables significantly lower response times, when compared to Streamlit.
  • It only runs the user script once.
  • It uses WebSockets to keep frontend and backend states in sync.

It's neat.

  • Streamsync uses state-driven, reactive user interfaces. A data app's user interface is strictly separated from its logic.
  • It uses a consistent yet customisable UI design system.
  • No caching needed; the script runs once and things remain in memory. You can use globals and module attributes to store app-wide data.
  • Predictable flow of execution. Event handlers are plain, easily testable Python functions. No re-runs, no strange decorators.

Check out a live demo of an app.

  • Streamlit Landing page
    Landing page //
    2023-10-07

Streamsync features and specs

No features have been listed yet.

Streamlit features and specs

  • Ease of Use
    Streamlit's API is extremely intuitive and easy to learn, which makes it accessible for developers of varying experience levels. The simplicity allows for rapid development and less time spent on complex front-end coding.
  • Interactive Widgets
    It provides a set of interactive widgets that make it simple to add complex functionalities like sliders, buttons, and file uploaders to your application with minimal code.
  • Real-time Feedback
    Streamlit supports real-time data updates, allowing users to see changes instantly. This is particularly useful for data analysis and machine learning applications where live data visualization is crucial.
  • Integration with Machine Learning Libraries
    Streamlit integrates seamlessly with popular machine learning libraries like TensorFlow, PyTorch, and scikit-learn, making it a great tool for showcasing machine learning models and results.
  • Open Source
    Being an open-source project, Streamlit is free to use and comes with the support and contributions of an active community. This means continuous improvements and a wealth of shared resources.

Possible disadvantages of Streamlit

  • Limited Customization
    Streamlit offers limited customization options compared to traditional web frameworks. This can be a hindrance if you need a highly customized UI/UX for your application.
  • Performance Issues
    For more complex or resource-intensive applications, Streamlit may suffer from performance drawbacks. It is not designed for high-performance computing out of the box.
  • Scalability
    Streamlit is not well-suited for large-scale applications requiring major backend architecture or for scenarios demanding high scalability and concurrency.
  • Limited Widget Style Options
    The styling and customization options for widgets are somewhat limited, meaning your application's look and feel might be more constrained compared to using other front-end frameworks.
  • Deployment Complexity
    While Streamlit provides some deployment options, deploying Streamlit apps in a production environment can sometimes require additional effort and knowledge, especially for those unfamiliar with web deployment practices.

Analysis of Streamlit

Overall verdict

  • Overall, Streamlit is well-regarded for its ease of use, speed of development, and ability to create clean and professional-looking applications without in-depth web development knowledge. It provides a seamless bridge between complex data analysis and user-friendly presentation, which can be highly beneficial for a wide range of use cases.

Why this product is good

  • Streamlit is a popular choice for quickly building and deploying data applications and interactive dashboards with minimal code. It is designed to be user-friendly, allowing data scientists and engineers to transform their scripts into shareable web apps. It supports real-time updates, is highly customizable, and integrates well with Python libraries like NumPy, Pandas, and Matplotlib, making it an attractive option for many developers working within the Python ecosystem.

Recommended for

    Streamlit is ideal for data scientists, analysts, and developers looking to rapidly prototype and deploy data-driven applications. It is recommended for those who prioritize simplicity, quick deployment, and seamless integration with Python code. Individuals or teams interested in building dashboards, ML model sharing platforms, or interactive reports will find Streamlit particularly useful.

Streamsync videos

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Streamlit videos

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My thoughts on web frameworks in Python and R (PyWebIO vs Streamlit vs R Shiny)

More videos:

  • Review - 1/4: What is Streamlit
  • Tutorial - How to Build a Streamlit App (Beginner level Streamlit tutorial) - Part 1

Category Popularity

0-100% (relative to Streamsync and Streamlit)

Streamsync

Streamlit

Developer Tools
10 10%
Developer Tools
90% 90
Application And Data
100 100%
Application And Data
0% 0
Productivity
3 3%
Productivity
97% 97
Python Web Framework
100 100%
Python Web Framework
0% 0

User comments

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Social recommendations and mentions

Based on our record, Streamlit seems to be a lot more popular than Streamsync. While we know about 209 links to Streamlit, we've tracked only 2 mentions of Streamsync. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Streamsync mentions (2)

Streamlit mentions (209)

  • Build Code-RAGent, an agent for your codebase
    The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by... - Source: dev.to / 28 days ago
  • How AI is Transforming Front-End Development in 2025!
    Streamlit.io: Great documentation and reusable components to integrate with your AI application for rapid python front-end AI development. - Source: dev.to / about 1 month ago
  • Querying DynamoDB with Natural Language Using MCP
    The agent uses MCP to translate this into a DynamoDB query. Then, using Streamlit UI, results are returned in a structured format, making it easy to use. - Source: dev.to / 3 months ago
  • Can I run this LLM?
    It's powered by something called "Streamlit" (https://streamlit.io). > A faster way to build and share data apps Website doesn't even load for me. I don't even know what to say...welcome to web dev 2025 edition. - Source: Hacker News / 3 months ago
  • Vaadin Flow for AdminUI
    Since Vaadin is Java-focused, its major benefits are best realized within that ecosystem. If you're using .NET, Blazor might be a better fit, while in the Python world, a lightweight alternative could be Streamlit. - Source: dev.to / 3 months ago
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What are some alternatives?

When comparing Streamsync and Streamlit, you can also consider the following products

Anvil.works - Build seriously powerful web apps with all the flexibility of Python. No web development experience required.

Panel - High-level app and dashboarding solution for Python

Recut - Edit silence out of videos automatically

AutoGPT Plugins - Plugins to enhance the functionality of ChatGPT

FastAPI - FastAPI is an Open Source, modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

Dash by Plotly - Dash is a Python framework for building analytical web applications. No JavaScript required.

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