Streamlit
Streamlit is an open-source Python framework that enables rapid development of interactive web applications for data analysis, visualization, and machine learning model presentation. Thanks to its simplicity and intuitive syntax, Streamlit is widely used by analysts, data scientists, and research teams.
Why Streamlit
Strengths of Streamlit
Rapid Prototyping
Enables quick application development without requiring knowledge of HTML, CSS, or JavaScript.
Python Ecosystem Integration
Easy integration with popular libraries (Pandas, NumPy, Matplotlib, Plotly, scikit-learn, TensorFlow, PyTorch).
Interactivity
Ability to add interactive widgets (sliders, buttons, forms) without complex code.
Automatic Refresh
Code changes are immediately visible in the application through hot-reload functionality.
Easy Deployment
Simple deployment to Streamlit Community Cloud, on-premises servers, or cloud platforms (AWS, GCP, Azure).
Openness and Development
Active community, rapid development, and regular framework updates.
Consider an alternative when
- UI Customization Limitations – — Fewer options for advanced appearance customization compared to frontend frameworks (React, Vue).
- Limited Support for Advanced Web Applications – — Streamlit works best for prototypes, dashboards, and analytical tools, but is not designed for building large, complex production systems.
- Performance with High User Load – — Performance limitations may arise with high traffic and multiple simultaneous users.
- Python Ecosystem Dependency – — The framework is primarily designed for Python-based applications.
What we do with Streamlit
Full platform lifecycle
IT Systems Maintenance and
IT Systems Maintenance and Development (web applications, internet services)
Design and Programming Work
Design and Programming Work (creating new functionalities, expanding existing systems)
User Support and Software
User Support and Software Engineering Consulting
Software Quality Assessment
Software Quality Assessment (testing, audits, optimization)
IT Project Management
IT Project Management (project leadership, consultations, workshops)
DevOps Services
DevOps Services (server monitoring, updates, security, infrastructure maintenance)
Integrations and Ecosystem
Streamlit integrates with libraries for data analysis, machine learning, visualization, and model deployment tools (MLflow, DVC). Functionality can be extended using custom components (Streamlit Components) and integration with APIs and databases. Streamlit Community Cloud enables easy online application sharing.
Comparison with Other Platforms
- Dash: Dash (Plotly) offers greater customization capabilities and supports more complex applications, but Streamlit is simpler and faster for prototyping.
- Shiny: Shiny (R) is popular in the R environment, while Streamlit is the leader among Python tools.
- Gradio: Gradio focuses on rapid interface creation for ML models, while Streamlit is more versatile.
Summary
Streamlit is an ideal tool for rapid prototyping, building dashboards, and presenting data analysis results. It is well-suited for data science teams, research groups, and anywhere where quick implementation and ease of use are priorities. Before choosing, it’s important to analyze project requirements and usage scale.
Other technologies
All technologies →
Not sure if Streamlit is the right choice?
We will advise honestly — even if another stack is a better fit.