I primarily use Plotly Dash Enterprise to build, deploy, and manage production-grade data applications, especially in organizations where data science needs to be converted into usable tools for business users.

At a practical level, teams use Plotly Dash Enterprise to bridge the gap between notebooks and real-world applications. Instead of sharing static dashboards and raw code, organizations can create interactive web apps in Python and present them to stakeholders. Common applications include internal data apps, productionalizing data science, machine learning model interfaces, secure and scalable deployment, and collaboration across teams.

In essence, Plotly Dash Enterprise is used when a company wants to transform Python-based data work into secure, shareable, and scalable web applications for real users, not just analysts.

Plotly Dash Enterprise offers several main features including easy deployment and app hosting, enterprise authentication and security, an app manager with a centralized dashboard, CI/CD and DevOps integration, background jobs and task queues, scalability and performance, and built-in data science tools. Additional features include data and API integration with reusable components, design control, a snapshot engine, collaboration capabilities, and governance features.

The deployment and app hosting workflow in Plotly Dash Enterprise is the feature that typically makes the biggest real-world difference. Most teams do not struggle to build dashboards; they struggle to reliably ship and maintain them. This feature matters because it removes DevOps bottlenecks. Without it, deploying a Dash app typically means setting up a server, configuring Docker, and managing reverse proxies. With this feature, the idea-to-impact cycle is significantly shortened, allowing teams to build something in a day and deploy it on the same day.

The real value of Plotly Dash Enterprise is not any single feature in isolation, but how they all work together once the user base grows. There are several second-order features that are often appreciated more over time. Governance and visibility are underrated but become crucial as organizations scale.

Plotly Dash Enterprise enables faster delivery of data products. Before using Plotly Dash Enterprise, insights lived in notebooks and static dashboards, and sharing meant screenshots, exports, and ad-hoc links. After implementation, teams ship interactive apps in days instead of weeks. Stakeholders can directly use tools rather than just view results, representing the biggest shift from analysis to a usable product.

Better collaboration between teams is another significant benefit. Plotly Dash Enterprise creates a shared layer between data scientists, backend engineers, and business users. Additionally, organizations gain confidence with sensitive data. In many organizations, data access is a blocker, but apps can safely expose internal data with access controlled per user or team.

The impact of Plotly Dash Enterprise can be measured quite clearly when used properly. Development and deployment time typically shows a 50 to 80% faster time to production. Before implementation, deploying a data app would take one to three weeks, but after implementation, the same app is live in one to three days. The biggest reason for this improvement is the elimination of custom DevOps work per app. There is also a 60 to 90% reduction in repetitive reporting work. Previously, analytics teams would generate Excel or PDF reports weekly with the same queries running repeatedly, but after using Plotly Dash Enterprise, organizations have self-serve dashboards and scheduled reports where one internal app often replaces hours of weekly manual work. Decision-making is faster as well, taking minutes instead of days. Before, stakeholders would ask an analyst and wait for a report, but after implementation, stakeholders can directly filter data themselves and run scenarios instantly.

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.

I have been using Plotly Dash Enterprise since last year.

Plotly Dash Enterprise is deployed in my organization using a private cloud. I rate this product 9 out of 10.

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.

My main use case for Plotly Dash Enterprise is completely about the dashboards for all my web applications and for my energy forecast dashboards.

A specific example of how I use Plotly Dash Enterprise for my energy forecast dashboards is completely based on the requirement from the team, where there will be a dashboard based on Siemens standard with some dashboards showcasing the real-time interactive dashboards. The interactive dashboard works fine for us when compared to any other solution.

Regarding my main use case, I add that it is very interactive.

The best features Plotly Dash Enterprise offers are mainly the callbacks, which is what we are using. There are layouts and callbacks forming the logic, with interactivity involving dropdowns, drags as sliders, callback updates, and Plotly figures at real times. Everything is extremely easy to implement, and you just assign a widget to a variable, making it rapid for data science, internal tools, and simple interfaces. This makes it a very easy method to create a dashboard with Plotly Dash Enterprise.

The callbacks and interactive features have specifically helped my team with speed and collaboration. For example, clicking on a data point in graph A automatically filters the data shown in graph B, which represents cross-filtering. Interactive ranges between sliders and selectors are very useful, and when we use LaTeX support for technical notations like E=mc² in titles or labels for mathematical clarity with dynamic tooltips, as we apply extra variables, and HTML formatting like hover labels and HTML formatting.

I would like to add that the most important point is the interactivity provided.

To improve Plotly Dash Enterprise, I suggest that cross-filtering capabilities need significant improvement along with file uploads and downloading data as a CSV or in any other requested format, as we seek more features aligned with user requests.

Plotly Dash Enterprise positively impacts our organization as we have started projects completely with Plotly Dash Enterprise, implemented for the last four years, focusing on real-time data where we check the real-time data every ten seconds. Everything works fine without complications.

Needed improvements relate to enhancing user experience across various functionalities.

Some ways Plotly Dash Enterprise could be improved include customizing the HTML loading screen or implementing server-side rendering logic, like state management that involves Dash Patch and partial updates. Previously, to change one graph's color, the entire figure had to be sent back to the server. There should be a focus on mobile responsiveness and shifting from standard CSS to Dash Mantine Components and Dash Bootstrap while utilizing grid systems for large data bottlenecks.

I have been using Plotly Dash Enterprise for four years.

Plotly Dash Enterprise is stable.

The scalability of Plotly Dash Enterprise occurs in three layers: execution, data transport, and infrastructure, where background callbacks come into play. If Python is single-threaded, one user triggering a heavy calculation can block others. Regarding data scalability, the payload problem surfaces, along with our server-side output store that I have previously mentioned. Partial property updates result in network traffic reduction by up to ninety percent. For infrastructure scalability, we are thinking about Docker or Kubernetes while also utilizing Redis for a shared state, making auto-scaling based on CPU or RAM usage available.

I have not gone through customer support, as my role does not involve the management side.

I did not previously use any different solution before Plotly Dash Enterprise. When I entered Siemens, my first task was to learn and start using Plotly Dash Enterprise for the UI, and I have been working with it since then.

I have seen a return on investment with Plotly Dash Enterprise, particularly in terms of saved time, as Plotly Dash Enterprise has enabled significant efficiency.

Before choosing Plotly Dash Enterprise, my team did not evaluate other options, as they had already started with Plotly Dash Enterprise before I joined, which is when I learned and implemented it.

To improve Plotly Dash Enterprise, I suggest that cross-filtering capabilities need significant improvement along with file uploads and downloading data as a CSV or in any other requested format, as we seek more features aligned with user requests.

My advice for others looking into using Plotly Dash Enterprise is that it is very useful for implementing dashboards, so I always suggest Plotly Dash Enterprise for real-time and interactive dashboards across any application. In our new projects, we have forty-two sub-applications in our tool base, where tracking how and when tickets are created, resolved, or completed through an API-based tracker is essential, and we are training a few students in Plotly Dash Enterprise for this purpose.

I rate this product an eight out of ten.

My main use case for Plotly Dash Enterprise is building and deploying interactive, production-ready applications for business users. I primarily use it to convert complex data analyses into user-friendly dashboards that support decision-making. I have worked on various projects where we build performance dashboards, pulling data from multiple sources such as databases and ETL pipelines, using Python to process and transform data into interactive visualizations that cover different regions, products, and time series data.

Although I have not worked with client projects yet, I have securely deployed the applications for internal usage, enabling real-time updates for daily sales tracking. This application helps businesses grow, identify trends, track KPIs, and make faster decisions without relying on static reports.

My experience with Plotly Dash Enterprise helps bridge the gap between data engineering and business users. Beyond just dashboards, it turns backend data pipelines into interactive applications and reduces static reports such as Excel or PDF. Instead of sending daily reports, we can create live dashboards where users can explore data independently. This enterprise application is not only suitable for small business use cases but also integrates seamlessly with existing data ecosystems such as databases and ETL tools, making it powerful in real-world enterprise environments.

Overall, it is not just a visualization tool for me; it is a platform that delivers end-to-end data solutions for business growth. These data applications directly support business decisions and are user-friendly, allowing even beginners to easily understand and build automated pipelines for tracking reports or dashboards.

The features of Plotly Dash Enterprise that I have experienced include several powerful capabilities that make it suitable for enterprise use, such as easy deployment, security, interactive dashboards, scalability, performance, and collaboration, allowing for real-time sharing of reports with stakeholders. The centralized data platform holds dashboards and supports version control and app management, along with interactive capabilities such as KPIs and data pipelines connecting databases, ETL systems, and ML models, fitting well into modern data stacks.

Overall, these features assist me in building secure, scalable, and interactive data applications, making it easier for business users to access insights without any technical background.

In my organization, I have noticed that the dashboards provided by Plotly Dash Enterprise have had a very positive impact. I recommend it for faster decision-making, reduced manual efforts, and self-service analytics for business users, enabling them to drill down, analyze, and have real-time visibility while integrating seamlessly with data pipelines. After deploying sales dashboards, reporting time has been reduced from several hours to almost real-time access. These main features are crucial for our client-side projects, and it aids in moving from static reports to interactive ones, helping to speed up the reporting process.

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.

I have been using Plotly Dash Enterprise for approximately two to three years.

Plotly Dash Enterprise demonstrates reliable stability.

The scalability of Plotly Dash Enterprise is dependent on how we design and deploy our applications. It is a SaaS-based tool, capable of horizontal scaling with built-in Kubernetes and containers. We can scale by adding more instances to handle multiple users efficiently, with the ability to support hundreds to thousands of users with proper backend performance control. While scalability challenges and bottleneck issues exist, our limited experience in that area means we have not faced them extensively.

The customer support for Plotly Dash Enterprise is commendable, as it considers all elements necessary for enterprise-grade projects. They assist with installation, deployment, performance tuning, scalable architecture, and troubleshooting, which are valuable for initial setups and production-ready configurations. Plotly also manages hosting concerns by handling upgrades, monitoring, and maintenance.

We previously used Power BI and Superset for user-friendliness and a simple ecosystem environment. We switched to Plotly Dash Enterprise because we sought tools that are more user-friendly, effective for business use cases, cost-effective, and capable of handling large data scales. Upon identifying this tool, we implemented it in our proof of concept.

We have seen a return on investment with Plotly Dash Enterprise, notably in time savings, productivity, and faster decision-making. Ad-hoc analyses that used to take days have been reduced significantly, with one case where the team saved seven to ten days per month. The faster creation and iteration of dashboards have led to less back-and-forth communication between the business and data teams, less dependency on other teams, and substantial cost savings.

The pricing for Plotly Dash Enterprise is based on custom and variable factors, with no fixed public prices. It depends on the number of users, deployment type, support level, and scale. The enterprise price can reach around one hundred thousand dollars per year, varying according to organizational size, and different licensing models are available based on platform access, security, and admin control features.

Before finalizing our choice, we evaluated several alternatives, focusing on tools with data visualization and scalability features. While I have experience with Microsoft Power BI and find it to be great for standard dashboards and business reports, we chose Plotly Dash Enterprise for its flexibility, Python integration, and ability to build fully customized data applications that better matched our requirements.

I advise others to understand that Plotly Dash Enterprise is not a typical BI tool such as Microsoft Power BI. It is more than just dashboards; it is a custom data application that integrates with Python, ML models, APIs, and complex workflows for user interactions. I would rate this product an eight overall.

My main use case for Plotly Dash Enterprise is to build dashboards. A specific example of a dashboard I built is an analysis on the used cars market in the United States. I used a Kaggle data source to get all the data that I needed and used Plotly to create different charts and graphs. The experience of building those charts and graphs with Plotly Dash Enterprise was good, and the visuals were very good to look at.

What I appreciated most about the visuals was the customization and simplicity. The best features Plotly Dash Enterprise offers include designing beautiful apps without using CSS or HTML and also the control access. The control access feature helps my team by using authentication code, so we can ensure only the people who should have access can view the dashboard. Plotly Dash Enterprise has positively impacted my organization by giving us valuable inputs through the interactive visuals, which we can use to make concrete decisions that help us improve our top line or bottom line.

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

I have been using Plotly Dash Enterprise for a few weeks.

Plotly Dash Enterprise is stable in my experience.

Plotly Dash Enterprise's scalability is pretty good. I have not personally seen Plotly Dash Enterprise handling increased loads or more users effectively, so I cannot provide more details about what makes scalability critical for me.

I did not need to use customer support for Plotly Dash Enterprise.

My advice to others looking into using Plotly Dash Enterprise is to go for it. I would rate this review an 8.

I have been using Plotly Dash Enterprise for quite some time, and I have used it for internal testing, making dashboards, and creating the UI for internal chatbots.

Primarily, since the UI is straightforward and Plotly Dash Enterprise allows us to use Python, we use it internally. I use it within my team to create UIs for chatbots. I also create KPI dashboards, many data dashboards, and business dashboards.

For one of our clients, we integrated a Power BI dashboard within Plotly Dash Enterprise UI. Along with that, we also enabled a chatbot to be built on the side using Plotly Dash Enterprise.

The best features Plotly Dash Enterprise offers are the fact that it is built on Python and the fact that we can customize many things. CSS can be added, and JavaScript can be added. There is significant customizability that is offered by Plotly Dash Enterprise.

As an end user, you want the dashboard or the chatbot UI to look interactive. In order to make it more interactive, we use custom CSS and JavaScript. That is how I feel that Plotly Dash Enterprise is a really good tool. All you have to do is create an assets folder, and anything inside that folder can be used by the application very quickly.

The DAQ feature is amazing because you get all these cool LEDs, displays, dials, and toggle switches. Many of the components, such as the Bootstrap components and the different themes that are offered by Plotly Dash Enterprise, are amazing. Overall, I think a person who is not that involved in UI development can start here because ultimately, it is Python code that helps us build UIs. It is a really good application.

For my internal demos and personal projects, I notice specific outcomes such as better visualization. It has been much easier to create many good and amazing dashboards easily because I am myself a Python coder, and I do not prefer to code in JavaScript and React. Creating things in Python is easier. Obviously, faster prototyping occurs, and better visualizations are achieved. Easier collaboration is something I am uncertain about because I usually end up opening a dev tunnel to all the demos that I make, and then other people can access 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.

I have been using Plotly Dash Enterprise for four years.

On a scale of one to ten, I would rate Plotly Dash Enterprise a solid nine. Honestly, nothing can bring anything to a perfect ten because there is always some scope of improvement. The fact that I have given it a nine means that it works for me, so I cannot generalize any score. At the moment, I feel that Plotly Dash Enterprise can definitely increase its reach and become a topic that is more discussed, compared to now, which is basically nothing. In my current company, many people do not know what Plotly Dash Enterprise is and what all Plotly Dash Enterprise can do.

The documentation for Plotly Dash Enterprise is amazing because I have gone through the documentation, and it is straightforward. Anyone trying to go through Plotly Dash Enterprise can understand it. I would rate this product a nine out of ten.

My company builds products. With the help of Plotly, the dashboards and reports my company builds are used by our clients. My company uses Plotly to present our data to clients so that they can understand and improve their performance.

My company uses Plotly as it is a very light and flexible tool. We use the free version of the solution based on our requirements. With a bit of technical skills, anyone can use Plotly. Any data analyst can use Plotly with a very small amount of training.

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.

I have been using Plotly for nearly more than an year. My company is a customer of Plotly. I use the latest version of the solution.

It is a stable solution. Stability-wise, I rate the solution a seven or eight out of ten.

I have not encountered any troubles while using the product, but I feel that it has some feature limitations.

It is a scalable solution. Scalability-wise, I rate the solution a nine to ten out of ten.

In my company, there are around five resources that use the product. My company's clients use the product a lot on a daily basis.

I use the product twice a week in my company before I get any call from our clients.

My company gets a lot of support from blogs and GitHub. Everything is good with the technical support. I rate the technical support an eight or nine out of ten.

My company has been using Power BI for many years. My company has experience with Tableau aswell.

My company switched to Plotly from Power BI since the former allowed the flexible use of certain views. Plotly's link with Python language made it here for our company to get the views that we wanted. Plotly was used to overcome certain limitations related to views, especially when it comes to the creation of custom views.

I rate the product's initial setup phase a nine or ten on a scale of one to ten, where one is a difficult setup process, and ten is an easy setup process.

The solution is deployed on a private cloud.

The solution can be deployed in a day or two since I had to set up a lot of custom views., after which the deployment process can be managed in a few minutes.

One of our company's in-house teams managed the product's deployment phase.

Plotly has been a good product, and it has only been a few months since my company started using it. Based on the scalability and client adaptability, my company should be able to get a fair return on investment.

It is an open-source tool. My company hasn't explored Plotly's premium services yet, but I believe the prices are on the premium side.

I rate the product price as five or six on a scale of one to ten, where one is a low price, and ten is a high price.

I am only in the picture when there is some decision-making process that happens with Plotly. Plotly is mainly used by the team in my company.

There is hardly any requirement for maintenance until and unless we change the views and colors or add additional views. Maybe a few hours are required every month to take care of the product's maintenance phase.

I rate the overall tool an eight out of ten.

In my approach to data analysis and presentation, especially when dealing with a substantial amount of code for data processing, I find Plotly to be a valuable tool. After running the code and obtaining results, I focus on presenting these results using it acceptably.

When reviewing and interpreting data, I typically examine it in a tabular form to understand its structure. However, when explaining or presenting the findings to others, I often use visualization tools such as Plotly. These tools allow me to create visual representations of datasets.

The tool needs to improve its pie charts. Regarding Plotly, I find the online documentation somewhat challenging to navigate, especially when dealing with many parameters. Searching through thousands of parameters can be a tedious and less straightforward process. While the documentation is available online and serves its purpose, I often encounter difficulties in finding the information.

I haven't contacted the tool's support.

In my experience, I primarily work with R when using Plotly. However, I understand that the integration of Plotly with Python is also excellent. I'm working with a sales and marketing consultancy that has operated for 20-30 years. They are transitioning to incorporating machine learning, AI, and statistical analysis into their traditional methods. The consultancy primarily works with B2B companies, recognizing that B2B firms with a limited pool of potential customers tend to be less engaged in social media than B2C companies. The focus is on platforms like LinkedIn, Facebook, and Instagram while acknowledging that TikTok is not considered valuable for their analysis.

The consultancy actively uses large language models to analyze various aspects, such as social media engagement, and assess how companies present themselves. In statistical and analytical terms, we aim to create impactful analyses for our clients. The approach involves comparing the target company with four to six similar companies, keeping the identity of these competitors confidential. Our goal is to assist clients in understanding their position in the market and provide tailored insights using modern tools. The focus is on identifying areas of weakness and highlighting strengths to refine sales and marketing efforts.

To make this process effective, we utilize Plotly, as they offer impactful presentations. Building databases of company data and conducting analyses on various aspects like websites, social media, and other marketing presentations, we present comparative analyses of how a particular company stacks up against its competitors.

I rate the overall product a seven out of ten.

I am using the solution for creating dashboards that can be deployed on web-based applications. It involves data analytics, graph representation, and table representation.

The good thing about Plotly is that it is based on Python. I know Python, so I can use it very easily. The level of interactivity that Plotly has is very useful. Users can play with the data and modify the filters of the images and tables.

The level of interactivity that the product provides is valuable to me.

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.

I have been using the solution for one year.

Compared to Tableau and Power BI, the product is less stable.

Compared to Tableau and Power BI, the product is less scalable. Three to four people in my organization use the solution.

I have been in touch with the support team through GitHub for some issues with my applications. My experience was very good. The team members are very good. They usually respond within two or three days. I'm happy with that. The solution has a really good community on GitHub. Most of my questions are already answered there. They usually respond within two or three days if it is something new.

The initial setup was moderately complex the first time. I use AWS to deploy my app on the web.

The deployment of the app does not take too much time. It depends on our level of expertise in both AWS and Plotly. If we want to deploy an app on the web, it must satisfy some criteria. As long as we maintain those criteria, it is easy to deploy on the web. It is a little bit costly to deploy the app. If we want to deploy it on the web, we must have the paid version of AWS. We cannot use a free version.

It took me almost a day to do all the setup, setting, and everything in Plotly and AWS. After that, it takes only half an hour to deploy the app on AWS. I did the deployment myself. While deploying the product, I had to change my code a little bit to maintain specific criteria. Then, I created some additional files for the application, like the asset files, the folder containing all the images, and all the things we will share. Then we need to zip everything together and go to AWS. We choose the services we wanted to use and set up our instances on AWS Connect. Finally, we upload the application onto AWS.

I only use the free tier of the product. I haven't studied the paid version, but I don't think it will offer a lot because it's all on Python. All that it offers is a desktop version of the software. I'm not sure how useful it's going to be for those who are coding Python.

I want to explore Tableau and Power BI. My organization did not have access to these solutions. As long as someone does not have access to the paid version of Power BI and Tableau, Plotly is the best option for them. Overall, I rate the product an eight out of ten.