What needs improvement with Plotly?

I found Plotly Dash Enterprise very useful, but there are a few areas that could be improved. The main limitation was the learning curve during the setup and deployment. Building visualizations in Python was straightforward, but moving from a local notebook or prototype into a deployed enterprise app required more understanding, and the onboarding part was really complex. I think a guided approach would really help. Additionally, ready-made templates such as templates for KPI dashboards, research dashboards, and comparison dashboards would be another improvement. Stronger collaboration features such as built-in commenting, version history, or something similar would also be beneficial. Regarding needed improvements, the commenting and version history are important because a lot of researchers and analysts are comfortable using Python for data analysis, but not necessarily with enterprise deployment workflows. While the visualization side of Plotly Dash Enterprise is powerful, the transition from a local notebook to a production-style dashboard environment can feel intimidating at first. More beginner-friendly examples, step-by-step deployment walkthroughs, and guides would really help. I also think having more practical examples around environment setup would definitely benefit users. One additional improvement area would be making day-to-day dashboard maintenance simpler for growing teams. As dashboards become larger and more widely used, organizing apps and managing versions become more important. I think stronger built-in project organization and governance features could help teams manage dashboards.

One area where Plotly Dash Enterprise can be improved is the learning curve for beginners. It can take time to understand the callbacks and app structures. Debugging can sometimes be tricky, especially for complex apps. Improving documentation and providing more built-in templates or examples would make it easier for new users to get started. Another improvement could be around performance optimization in Plotly. For example, with a large dataset, a dashboard can sometimes become slow. Better built-in support for handling big data efficiently would help. Additionally, smoother integration with other data tools and cloud services would make it easier to fit into different tech stacks.

Plotly Dash Enterprise could improve by lowering the learning curve for new users and offering more modern UI/UX tooling out of the box, as while deployment is still strong, feedback cycles can still be improved. We sometimes see a gap between how developers build dashboards and how business users request changes, so a built-in feedback or annotation system directly inside apps, such as commenting on charts or layouts, would make iteration cycles faster. Plotly Dash Enterprise can benefit from stronger low-code capabilities, a faster prototyping experience, more consistent UI/UX tooling, and better debugging.

Plotly Dash Enterprise works well for most cases, but for some large data sets, it can be a bit laggy. Improvements can be made in that area.

I think Plotly Dash Enterprise can be improved because the customization gets tricky fast. Even simple tweaks such as fonts or spacing require digging into nested dictionaries. Styling also feels inconsistent across the chart types, which sometimes makes it harder to maintain a uniform look. I faced problems where legends and axis labels tend to overlap often, especially with large datasets or longer label names, which looks very messy. Fixing it is not very straightforward, so that is something problematic. The layout versus the trace structure is also confusing at first, but then it takes a while to figure out what goes where. If these things were improved, it would be better.

Adding documentation AI to Plotly Dash Enterprise would be more useful than trying to figure it out through certain AI, LLM, or chatbots for certain functions that I would prefer to use in custom cases. A chatbot that could work for custom expectations and needs would be the most helpful improvement.

Plotly Dash Enterprise can be improved by being more modularized, and more animation features should be included. Multiple charts with multiple trajectories in one chart should be available. For instance, when measuring three Y axes with one X axis, I think Plotly Dash Enterprise could develop further in that area. I sometimes find that the documentation is not comprehensive and more detailed documentation should be available for the benefit of other companies using it. Support is great, and I have no concerns there. Integration is very good. The only area for improvement is documentation.

I would like to discuss improvements needed for Plotly Dash Enterprise, specifically regarding licensing and documentation that I wish were different. I have additional improvements needed for Plotly Dash Enterprise, particularly regarding my experience with pricing, setup cost, and licensing.

It could have developed a more gradual learning curve. It is still accessible to non-technical users, but I think it could be more accessible to non-technical users.

Plotly Dash Enterprise is already a strong product, but there are meaningful areas for improvement. Developer experience is one area. Building a complex UI in Dash can feel verbose and slower compared to modern frameworks. Improvements could include better state management similar to React hooks, visual debugging tools for callbacks, and a cleaner abstraction of complex interactions. Faster prototyping is another improvement area. For quick experiments, Dash is slower than alternatives because it has more boilerplate code and requires a more structured layout upfront. A rapid mode for quick dashboards that uses less code, enables faster interactions, and includes more built-in high-level components would be beneficial. UI/UX components and the design system could also be enhanced. The out-of-the-box UI components currently feel basic and limited compared to modern design components, and styling often requires extra effort. Improvements would include a richer component library with tables, layouts, and forms, along with a built-in theming system similar to Material-UI or Tailwind-style presets. Real-time and streaming support represents a final area for improvement. Handling real-time data, live updates, and streaming is not as smooth as it could be. Native improvements would include integrating native WebSocket support and providing easier real-time pipelines.

There are definitely a few areas where Plotly Dash Enterprise could improve to become even more effective. Currently, most dashboards need to be built from scratch, so having more ready-made templates, such as those for sales, finance, or monitoring dashboards, would significantly speed up development. A more guided user interface and low-code features would help with onboarding for beginners and non-technical users, making the platform more accessible. While the visualization capabilities are flexible, some advanced charts require extra customization, so more out-of-the-box visual components similar to those found in Power BI or Tableau would be beneficial. Additionally, performance optimization tools for large-scale apps need to be improved, as performance tuning requires manual intervention. Enhancements in version control could make deeper interactions with CI/CD pipelines and tracking smoother for enterprise workflows. Lastly, production pricing flexibility is essential, as current pricing models seem more geared towards large organizations, which may limit accessibility for smaller teams and startups.

Plotly Dash Enterprise is pretty good, but it could benefit from more marketing so that more people are aware of it.

Nothing comes to mind at the moment about how Plotly Dash Enterprise can be improved. It is simply the fact that people do not discuss Plotly Dash Enterprise much, and it is such a good tool. I feel that Plotly should focus more on how they can improve the product's reach to other people as well. The capabilities of Plotly Dash Enterprise have not been discussed that much in the communities. It is a really good platform.

If you have to make very fine refinements to the dashboard in terms of deployment in Plotly Dash Enterprise, which is actually a great feature when you are hosting it, but if you have to make some slight tweaks to it and if there is a problem that arises, it is really hard because a lot of the things are black-boxed. That is one of the shortcomings. For large data sets in Plotly Dash Enterprise, it can be slow sometimes due to many callbacks that are there. For large data sets, it may require different callbacks, which slows the dashboards down significantly. For static data, I think it requires caching and different optimization that has to be done to make the dashboard responsive. If that can be handled a little better, that would be a plus. Plotly Dash Enterprise could probably improve on the latency that is there for big data sets. Because I have used Plotly in Jupyter Notebook itself, and I have seen the package; the package size is very big as compared to traditional libraries in Jupyter Notebook, such as Matplotlib or Seaborn. If that initial payload, or the size of the package, can be reduced, I think the latency can also be reduced.

The main improvement I can think of is that while creating charts, it gives you a certain format of how it could look. If you want to create something extra and go more vivid and creative with how the actual chart would look, it allows for that option but could be improved to be more artistic or aesthetically pleasing. This sort of format is missing, and I think it would be beneficial to the analytics team if it can be more interactive, with the capability of D3.js, and give us more control over how our actual dashboard would look to achieve a more aesthetic appearance. The strict format of how you can shape those charts and that extra nuance you need to keep in code to get the exact possible results are the reasons behind my rating. The rest of the features provided by Plotly are extremely good.

I believe that the price of the tool's paid services is on the premium side, so the pricing model can be considered for improvement.

The solution cannot be deployed on the website and shared with others through its own platform. We need a third-party platform to share the application that we develop. In Tableau and Power BI, we can simply share the website with others as long as we use the paid version. However, for Plotly, we need to deploy our app on a cloud-based service like AWS or other third-party websites to share with others.