Plotly Dash Enterprise Reviews

My main use case for Plotly Dash Enterprise is to create financial models, which I use for creating models that are mostly Python-based. A specific example of a financial model I built using Plotly Dash Enterprise was to create a model that would go through the revenues, the EBITDA, and the growth rates, based on the financials of a company, so that I can forecast the upcoming five-year revenue and EBITDA margins.

The most useful feature that I found helpful in Plotly Dash Enterprise was that it was very handy; I can resize and realign the models and dashboards that I have created on Plotly. I can resize it for my phone or mobile screens and change it accordingly.

The resizing and realigning feature for mobile screens has been very helpful, especially since I had to present it to our investors quite a few times, and while going to those meetings, it was very useful. I have used it more than ten times.

Plotly Dash Enterprise has made the creation of financial models and dashboards very handy and very easy for my organization, as I used to work on Power BI and Tableau, which used to take some time. After using Plotly Dash Enterprise, the time required to create such models and dashboards has reduced significantly.

In terms of hours, a model that was taking about eight to nine hours to build is now built in about three to four hours with Plotly Dash Enterprise. I think Plotly Dash Enterprise is providing everything that an analyst requires.

There is nothing about the features that needs improvement.

It has been about one and a half years that I have been working in my current field.

Plotly Dash Enterprise is stable.

Plotly Dash Enterprise has helped me do a large scale of work with large-scale data.

Customer support was not required while I was using Plotly Dash Enterprise, so I consider that a positive sign.

I did not previously use any other solution before Plotly Dash Enterprise.

I did not go through the pricing, setup cost, and licensing during my time, as I was just using this enterprise; these were mostly handled by the deal desk team.

I just use Plotly Dash Enterprise for my work; there is no other business relationship with the vendor.

Plotly Dash Enterprise saved my time by approximately four to five hours.

I did not go through the pricing, setup cost, and licensing during my time, as I was just using this enterprise; these were mostly handled by the deal desk team.

I did not evaluate anything other than Plotly Dash Enterprise before choosing it.

I have used Plotly Dash Enterprise quite a number of times. I have used Plotly Dash Enterprise for financial models. Plotly Dash Enterprise is deployed in my organization on-premises. I would say that Plotly Dash Enterprise is a great platform to build financial models, and for traders, it is really helpful to go through the metrics of financials and the analysis of technical charts. I gave this product a rating of 8.

I had been using Plotly Dash Enterprise for only one time, around three or four months, not more than that. At that time, I was working for an automotive client who requested a dashboard with thermal metrics of their electric vehicle buses. I used Plotly Dash as an option to create a dashboard to embed all the plots and their respective metrics, having a data frame as a summary that showed which vehicles were of concern and which vehicles were performing well. This was the application I intended to make, but we ended up making it in Django instead.

We ended up switching to Django instead of continuing with Plotly Dash Enterprise for that project because we had a resource who had expertise in Django at that time. He joined recently, so we switched from Plotly to Django because no one was fully aware of Plotly. The skill is not very commonly used compared to Django, which is a pretty common skill set among ML engineers.

I faced some challenges as Plotly plots were slow. Plotly Dash Enterprise offers great themes and a good user interface, which I liked compared to Django. However, since it was used by experts, we did not consider the front-end part as much.

Plotly Dash Enterprise helped me to visualize data better as the color schemas provided were very contrasting, and anyone new who saw the data could interpret it much quicker compared to any other platform. I am confident about that. However, we did not save any time, and slowness was not a major drawback actually because we wanted to keep it simple. That is why we switched to Django.

Plotly Dash Enterprise impacted my organization positively as it was an option while I worked, and it was really promising. We switched to Django to keep it simple. That was the reason, and while I used it, it was a bit slow, and the plots were also taking some time to render. We ended up using Plotly plots in Django. It was different, but this is my overall experience.

I do not have that expertise regarding how Plotly Dash Enterprise can be improved. I think they could provide a version which is very simple, or maybe it is available and I have not used it, which I am not sure about. It could be marketed better, and different front ends could be marketed better to different audiences to increase the audience base.

There are no improvements needed for Plotly Dash Enterprise that I have not mentioned. I gave it a seven because it did not fit my use case exactly at that time, and I was also not well-informed about it. I was limited by the knowledge I knew up to that point, and it could have more features. Plotly is mostly seen as an application to attract for attractive front ends and interactive attractiveness, so it could be more fast. I did not use Plotly Dash Enterprise enough to evaluate its governance and security.

I have been working in my current field for five years.

Plotly Dash Enterprise is stable.

We did not go to that extent regarding customer support.

I previously used Django before considering Plotly Dash Enterprise, and we switched for simplicity and speed.

I evaluated other options before choosing Plotly Dash Enterprise, specifically Django, Flask, and FastAPI.

Any web application provider such as Django or Flask could have provided the same. The impact is not unique, but the impact is there on its own.

Plotly Dash Enterprise was considered for deployment in my organization on a private cloud, specifically Amazon Web Services. We used AWS for our private cloud deployment. We did not purchase Plotly Dash Enterprise through the AWS Marketplace, as I previously stated. We did not deploy it, but we considered it as an option. We deployed the Django version instead.

My advice to others looking into using Plotly Dash Enterprise is to use it for applications where the end user is a customer. I gave this product a rating of seven.

My main use case when I tried Plotly Dash Enterprise was creating insightful results for my needs.

I was visualizing data for an e-commerce platform's marketing data analysis, and Plotly Dash Enterprise helped me by providing a robust visualization tool.

I don't have much else to add about my use case or how I used Plotly Dash Enterprise for my e-commerce marketing data analysis. It is regular usage to visualize the data, find the flaws, where the e-commerce platform lags, and where it is not performing well.

Plotly Dash Enterprise is for data visualization, and I hope to create meaningful insights through this process.

In my opinion, the best feature Plotly Dash Enterprise offers is versatility. It is versatile to use in every case, and that is what I feel is a good feature, along with how it is being used.

When I say versatility, I mean it is easy to adapt for various kinds of products, and that is what I mean.

Plotly Dash Enterprise has positively impacted my work by making my analysis easier. It is also quite easier to draw insights rather than regular coding.

I did notice specific outcomes, such as making more accurate decisions and improving data-driven decision making using Plotly.

I don't feel there are any significant challenges, but being in the race is very important. Plotly could add better AI-based features to make it improved.

I wish for features such as auto detection of data and auto analysis to be included.

I have been using Plotly Dash Enterprise once or twice.

In my experience, Plotly Dash Enterprise is stable with no crashes or issues.

I'm not sure about its scalability since I did it only for a small dataset and haven't tested it on larger projects.

I haven't interacted with customer support for Plotly Dash Enterprise at all.

I previously used a different solution for data visualization. I do code using Python.

Setting it up on my local machine was straightforward.

As a student, I haven't seen a return on investment or any metrics or examples such as saving time or resources.

My experience with pricing, setup cost, and licensing for Plotly Dash Enterprise was okay, but I do feel that you could offer a free tier.

Before choosing Plotly Dash Enterprise, I evaluated other options such as Power BI.

On a scale of one to ten, I think that rating Plotly Dash Enterprise is subjective, so I don't know if I could give a number. I chose seven because it works on synthetic data, which I tried to work on, but I don't know how far it would work well for non-synthetic data. That is why I rated it seven.

My advice to others looking into using Plotly Dash Enterprise is that it is a good one. I would say it is beneficial for real-world applications. In companies, it would make your work easier if you are in HR or anything where you need to put a lot of visualizations to do your daily work. I give Plotly Dash Enterprise an overall rating of seven.

On-premises

My main use case for Plotly Dash Enterprise is for data analytics, especially where we are involved in the analytics and visualization for better decision-making.

When I use Plotly Dash Enterprise day-to-day, it typically starts with building interactive dashboards and analytics applications using Python without requiring any heavy front-end development. Being in a market research company, our team uses it to turn raw survey or business data into live visual dashboards that help clients and our teams to monitor insights in real time, leading to better decision-making.

For some of my colleagues, especially those coming from market research operations, the transition to Plotly Dash Enterprise needed more structured training because they heavily depended on Excel manual reporting and static PowerPoints before using Plotly Dash Enterprise. Their main challenges were understanding the live dashboards; I have no problem with this because I have used Power BI, but it was a problem for them to understand live dashboards instead of static files, interpreting interactive charts, using filters correctly, and trusting automated data uploads rather than manually checking everything. Fifty members from operations needed to adapt to real-time KPI monitoring, recruiters' performance tracking, and automated quota management.

I have been familiar with Plotly Dash Enterprise for about two and a half years.

When we implemented Plotly Dash Enterprise, the timeline for getting everything up and running depends on the complexity of data, the needs for automation, and the scope of the dashboard. The typical timeline is around two to five days for simple charts, just survey tracking, and for Excel or CSV file uploads and some basic filters. However, for a professional internal dashboard, it might take around one to three weeks or one to five weeks. If we require a full operational enterprise system, it can take up to three months, including aspects such as cloud deployment, role-based access, machine learning integration, scalability, multiple pipelines, live APIs, and quality checks.

Adoption of Plotly Dash Enterprise across my organization is not limited to just one person or a small task; it is commonly used across multiple departments, but the way it is used can differ depending on each team's needs.

For example, when I work with the US team from my country, we have operations, business development, sales, and HR. The Operations team may use it for survey tracking, recruiter performance, and fieldwork status, while management may use it for KPIs, revenue trends, and project progress. As research analyst teams, we use it for predictive modeling, quality control, fraud detection, and advanced visual analytics. Client servicing teams, such as business development, may use client-facing dashboards to share live insights and reports with the end clients.

My background was mostly beneficial as Plotly Dash Enterprise is Python-based and relatively intuitive for me since I already work with data analysis tools such as Pandas and SQL, but most of my colleagues were not in the same situation. I would rate this product highly based on its capabilities and impact on our organization.

My main use case for Plotly Dash Enterprise is building important internal dashboards to visualize operational data, specifically around production and RPA models and tracking data matrix trends and node preferences. I built a specific dashboard with Plotly Dash Enterprise that helped my team by automating the error rates and data constraint scores with a traffic light system, which represents a huge improvement over manual Excel tracking. My production team responded positively to having that real-time dashboard, and it changed their workflow and efficiency.

The dashboard really streamlined our production meetings and allowed the team to focus on solving issues instead of hunting for them. Being able to see problems in real-time must have saved a lot of time and energy.

Plotly Dash Enterprise has positively impacted my organization, and I have seen outcomes like time saved, errors reduced, and improvements in decision-making. These improvements affected my team and company by changing how people collaborated and how quickly issues were resolved.

The best features Plotly Dash Enterprise offers include the Python stack, CI/CD integration, and security features. CI/CD and role-based access controls helped my team significantly by changing our deployment process and the way we managed permissions.

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.

I have been working with Plotly Dash Enterprise for the last seven months.

Before choosing Plotly Dash Enterprise, I evaluated other options, and I can confirm that Plotly Dash Enterprise is stable.

Plotly Dash Enterprise's scalability is satisfactory.

The customer support is commendable.

I previously used a different solution before Plotly Dash Enterprise and switched because of its advantages.

I have seen a return on investment with Plotly Dash Enterprise and can share relevant metrics such as money saved, fewer employees needed, or time saved.

I rate Plotly Dash Enterprise a nine on a scale of one to ten because of what stood out most and what kept it from being a perfect ten. My overall review rating for Plotly Dash Enterprise is nine.

Public Cloud

Amazon Web Services (AWS)

I have been using Plotly Dash Enterprise for more than four years, developing customized dashboards and analysis using Plotly on Python for use within my organization.

I use these dashboards for analysis within my organization primarily for audit, quarter audit, and annual audit purposes, where we work on sharing the data and managers take decisions to enhance performance for these quarters or annual years.

The dashboard is actually part of ISO certifications, which is an important aspect of my main use case and how the dashboards help my team.

Plotly Dash Enterprise offers many different types and dynamic charts, and Dash is easy to develop and fast to deploy, making it one of the best features.

These features help my work because I am a senior professional in Python, and most of the time I want to develop everything on the same platform, so you do not need different types of technologies such as front-end or others. With minimum knowledge of front-end or JavaScript, you can develop a full dashboard and make it dynamic with filters and updated live data while you browse. For integration, if you know Python, you can integrate with different databases, collect data, and clean from various databases while using the dashboard to publish and integrate with older systems in the industries.

There are no specific outcomes, but there is considerable improvement because the data was shared. Many issues and insights from the dashboard may lead to different decisions and improve everything.

Improvements include cost-saving from open sources, plus a considerable amount of customization and money savings. You can develop it on your premises or use it as a managed service.

Plotly Dash Enterprise can improve by providing strong certifications and courses so people can develop better.

I have been using Plotly Dash Enterprise for more than four years.

The customer service is good, so I have no additional comments on user experience, documentation, or support.

I give Plotly Dash Enterprise a rating of eight out of ten.

I chose eight out of ten because when you calculate the score from both the back-end and front-end perspectives, Plotly Dash Enterprise demonstrates strong governance and security with fewer backdoors and vulnerabilities because it is enterprise-grade.

The accuracy and reliability of output from Plotly Dash Enterprise are good because it relates to the code itself, but the clear rules and lines of work that update and provide insights make this evident.

Most of the time, I advise others looking into using Plotly Dash Enterprise that it is a fast and strong Python library. Plotly and Dash are powerful and can improve quickly in data analysis without requiring back-end knowledge. My overall rating for this product is eight out of ten.

Private Cloud

Other

Our main use case for Plotly Dash Enterprise was to collect data from medical devices for another project. We were collecting very large datasets from medical devices for cancer diagnosis. The data was substantial, so we needed to analyze it and create dashboards to show our stakeholders the details of this data. We decided to create dashboards to help them understand and analyze the data in a better way.

For one of those dashboards built with Plotly Dash Enterprise, the data was for cancer, and we were collecting the data from the medical devices using our backend. We then used the collected data to create our main dashboards for business intelligence. It was not exactly business intelligence in the traditional sense; rather, we used the visualization from this data to create exploratory data analysis before the machine learning phase, so we could use this data in the machine learning phase. We needed visualization that was like a regression diagram, which was a scatter plot. We used it previously in machine learning to understand the relation between the X and the Y variables. The scatter plot was the exact visualization we employed.

The best features Plotly Dash Enterprise offers include the callback functions, which I found to be better than the Streamlit equivalent.

Plotly Dash Enterprise has positively impacted my organization in terms of performance and efficiency, which were genuinely better than the other competitors. The development for it was more complex than Streamlit, though some people thought it was easier than Streamlit. Regarding performance, it was truly better. The visualization and the components moved faster without the very high latency that we experienced with other solutions. That was really excellent.

Plotly Dash Enterprise can be improved by enhancing the documentation and providing examples about how to use it and templates that can be used. I did not mean that templates would be the magic solution, but they could help beginners. I understand that for some enterprises, this is not the best option.

I have been using Plotly Dash Enterprise for about a year while working on creating business intelligence dashboards. Previously, we used Streamlit, and then we decided to migrate to Plotly because it was more flexible for our case and better for the callback functions and the features Plotly Dash Enterprise offers. We started working on that migration, and I took some courses to facilitate the transition.

I initially thought it was not the best idea and that we should stay with Streamlit. After trying Plotly Dash Enterprise, it was truly excellent. I advise enterprises that want to move to it that it is a very good option, reliable, and secure. I believe it fulfills all the compliance requirements or certificates that may be needed for any region, particularly the main regions. The accuracy is dependent on the development work.

I do not have anything else to add about my main use case or how I used Plotly Dash Enterprise in my project, though I think in the future I will use it in our pharmacy because we are trying to build agents using our pharmacy data. We have very large datasets as well, so we need to create business intelligence dashboards. In the next period, I will research whether it is useful to use Plotly Dash Enterprise with agentic AI to build dashboards based on natural language queries. I will check the details and see if it is a good fit.

The deployment process with Plotly Dash Enterprise is better and easier compared to my experience with Streamlit because Streamlit has many hidden details in the documentation, making it unclear how to deploy anything. Plotly Dash Enterprise was better, and I received clearer information from it.

Plotly Dash Enterprise's accuracy and reliability of the output are dependent on the developer's work. If the developer has any bugs, they will appear in the output, but the framework and platform itself are accurate and reliable.

I would rate this product a 9 out of 10.

Public Cloud

Amazon Web Services (AWS)

Our main use case for Plotly Dash Enterprise is for data analytics, as we primarily involve it in analytics and visualization for better decision-making.

When I use Plotly Dash Enterprise day-to-day, it typically begins with building interactive dashboards and analytics applications. As a data scientist and research analyst, I utilize Python without requiring heavy front-end development. In my day-to-day work at a market research company, our team uses it to turn raw survey or business data into live visual dashboards that help our clients and teams monitor insights in real time, which leads to better decision-making.

Adoption of Plotly Dash Enterprise across my organization is not limited to one person or small tasks. It is commonly used across multiple departments, though the way it is used varies based on each team's needs. For example, the operations team in our country utilizes it for survey tracking, recruiter performance, and fieldwork status, while management uses it for KPIs, revenue trends, and project progress. As a research analyst, our team uses it for predictive modeling, quality control, fraud detection, and advanced visual analytics. Client servicing teams such as business development employ it for client-facing dashboards to share live insights and reports with end clients.

When my team started using Plotly Dash Enterprise, I found it relatively intuitive because it is Python-based and I already work with data analysis tools such as Pandas and SQL. However, most of my colleagues needed more structured training since they were heavily dependent on Excel manual reporting and static PowerPoints before using Plotly Dash Enterprise. The main challenges for them included understanding live dashboards instead of static files, interpreting interactive charts, using filters correctly, and trusting automated data uploads rather than manually checking everything, particularly for 50 members from operations who needed to adapt to real-time KPI monitoring, recruiter performance tracking, and automated quota management.

When we first implemented Plotly Dash Enterprise, the timeline for everything to get up and running depends on the complexity of data, needs for automation, and the scope of the dashboard. Typically, it takes around two to five days for simple charts, for survey tracking only, and for Excel or CSV file uploads with some basic filters. However, if we need a professional internal dashboard, it might take around one to three weeks or one to five weeks. For a full operational enterprise system, it can take up to three months, which includes cloud deployment, role-based access, machine learning integration, scalability, multiple pipelines, live APIs, and quality checks.

I would recommend Plotly Dash Enterprise to other organizations.