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Streamlit VS Compose for Developers

Compare Streamlit VS Compose for Developers and see what are their differences

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  2. » Videos
  3. » Reviews
  4. » Alternatives

Streamlit logo Streamlit

Turn python scripts into beautiful ML tools

Compose for Developers logo Compose for Developers

Compose is an all-in-one platform that makes it incredibly fast and simple for a developer to build any kind of internal tool they need for their business.
  • Streamlit Landing page
    Landing page //
    2023-10-07
  • Compose for Developers Landing page
    Landing page //
    2025-02-12

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.

Compose for Developers features and specs

No features have been listed yet.

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.

Streamlit videos

+ Add

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

Compose for Developers videos

No Compose for Developers videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Streamlit and Compose for Developers)

Streamlit

Compose for Developers

Developer Tools
98 98%
Developer Tools
2% 2
Workflow Automation
0 0%
Workflow Automation
100% 100
Productivity
96 96%
Productivity
4% 4
No Code
0 0%
No Code
100% 100

User comments

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Social recommendations and mentions

Based on our record, Streamlit seems to be a lot more popular than Compose for Developers. While we know about 213 links to Streamlit, we've tracked only 1 mention of Compose for Developers. 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.

Streamlit mentions (213)

  • 🔥 Simulating Course Schedules 600x Faster with Web Workers in CourseCast
    The original CourseCast 2024 was prototyped and deployed as a Streamlit application written in Python. Students would input their course utilities and submit their simulation request to the Streamlit Community Cloud where:. - Source: dev.to / about 1 month ago
  • From PDF to Summary: Building an AI Agent with Python & Vector Databases - Basic
    Streamlit → Front-end & deployment. LangChain → LLM integration & chaining workflows. Hugging Face → Pre-trained AI models (e.g., Mixtral-8x7B). Supabase → Vector database for storing PDF embeddings. - Source: dev.to / about 2 months ago
  • Positron – A next-generation data science IDE
    ... And if you (the parent comment author) wants a really easy tool for creating a UI for the Python code, I'd recommend looking at Streamlit: https://streamlit.io/. - Source: Hacker News / 2 months ago
  • Predictive Maintenance Systems for Cleaning Robots: Boosting Efficiency Through Smart Tech
    Use Streamlit to visualize and test predictions interactively:. - Source: dev.to / 4 months ago
  • 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 / 5 months ago
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Compose for Developers mentions (1)

  • Show HN: Compose – Build internal tools faster, without leaving your codebase
    ``` Please let me know what you think! Website: https://composehq.com. - Source: Hacker News / 7 months ago

What are some alternatives?

When comparing Streamlit and Compose for Developers, 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.

Prefect.io - Prefect offers modern workflow orchestration tools for building, observing & reacting to data pipelines efficiently.

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.

Retool - Build custom internal tools in minutes.

150 ChatGPT 4.0 prompts for SEO - Unlock the power of AI to boost your website's visibility.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

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