Why is python dash more suitable for large-scale services than streamlit when I want to use it at work?
hi @seojb
welcome to the community and thank you for the question.
Here are two resources that help answer that question.
- A recent article from a community member
- A side-by-side comparison on our website
As a summary comparing between Dash and Streamlit:
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Styling: Streamlit requires users to master CSS to make changes to the UI/UX elements, making it significantly more difficult to edit the UI / UX of the application. Their starter pack is decent, however, when you need branding, especially at work, it can get tough to make changes if you do not know CSS.
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Speed: The more elements you have in your Streamlit application, the slower the application will run due to the way Streamlit handles its UX/UI rendering. With Streamlit, application load times, especially for larger applications, are slower due to this reason.
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Deployment: Streamlit has no supported platform for deploying, authenticating, and managing applications. Streamlit Cloud is hosted on their cloud and has minimal capabilities. In contrast, Dash Enterprise provides an enterprise-grade platform that is installed within a customer’s VPC for deploying, scaling, and authenticating applications. Plus, the application framework itself provides easier UI customization, faster rendering, and scalability.
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End-to-end application management: With Dash Enterprise, it makes the entire lifecycle of data application development extremely easy for data analysts, scientists, or non-IT engineers. Plus, Dash Enterprise recently launched the capability to deploy Streamlit apps directly. So, both Dash and Streamlit developers can stay in one platform.
Here’s a quick visual comparison:
Category | Dash Enterprise | Streamlit |
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Built-in chart editor | ![]() |
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Built-in cross-filtering | ![]() |
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Built-in data aggregation | ![]() |
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Drag and drop layouts | ![]() |
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Custom PDF reports | ![]() |
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Saved views | ![]() |
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