Posit Reviews

My main use case for Posit is that previously I was using it for Crop Science at Bayer Crop Science to do the R model development, EDA, and for all the data scientists to conduct field exploration, and now, as part of Bayer Pharma, we are enabling Posit platform, including Posit Workbench, Connect, and Package Manager for all the Pharma users to do their EDA, model development, and drug innovation.

They are using a statistical computing environment, but some users are moving to open source, so we are using Posit, and there are close to 200 or 300 users within Bayer who are using Posit on a day-to-day basis.

A specific example of how my team has used Posit recently is by deploying multiple Shiny applications, which are used internally for the drug innovation platform, for oncology, and for the omics platform, where users do their data analysis using R and publish it as a Shiny application in Connect, along with ongoing internal iterations and projects, for which we use Posit.

Uniquely to our workflow, some users in the statistical computing environment do their analysis in SAS and then run their workflow, such as an R script, within Posit environment to manage all the FDA submissions and reporting. Additionally, a few users have Workbench jobs that require huge memory and CPU capacity to run jobs for oncology reports and other tasks.

Posit has positively impacted my organization by definitely increasing the productivity of data scientists, saving a lot of time, and moving all biostatisticians and data scientists from their desktop to the cloud, which helps scale their work and collaborate to achieve better results for the company overall.

We have multiple data sources enabled with Posit; for example, we can integrate Databricks, Snowflake, and the internal file system from on-prem and AWS S3, which saves us a lot of time. The productivity could be noted as increased by 35 to 40% due to having a single place where they can access data and collaborate in their teams.

The best features Posit offers include functionality that accommodates all users from the data science community and biostatisticians, with open source IDE on the desktop and now with the URL to different IDEs including RStudio Pro, Positron, VS Code, and Jupyter, which I think is a great feature, alongside one-click publishing for Shiny applications or Streamlit applications, accommodating both R and Python users, and there is constant improvement in Posit product.

Posit can be improved as they are constantly listening to our feedback; currently, we have Databricks and Snowflake support, and we have been trying to ask for more in the LLM space, as previously only Anthropic and GitHub were available, and now we see AWS Bedrock has been added. They continue to engage with the user community across multiple customers and keep adding features. If they keep progressing in this innovation space, it would be fantastic for a company Posit to adopt more Posit.

An improvement needed for Posit that I haven't mentioned yet is possibly providing more user-level training for vendors such as us; not just monthly, but at least quarterly deep dives into their features would be beneficial.

I have been using Posit since 2016, including the open source of RStudio, and then from 2018 as an RStudio company, which means I have been using Posit for close to 8 to 9 years or more.

Posit is indeed stable.

Posit's scalability is commendable, as we are running in EKS, so we can easily scale anywhere from 20 to 200 users with both the IDEs and app deployment.

The customer support for Posit is really good, as whenever we encounter any issues or require assistance with configuration, we email support@posit.co and receive an immediate response.

Previously, we used an open-source solution, and when we transitioned to Posit, we noticed a lot of support and internal development has been added; we see clear differences with the licensing, but maintaining the platform is quite easy and stable.

Our team is pretty happy with what we pay regarding pricing, setup cost, and licensing.

Before choosing Posit, we evaluated other options including Domino and Databricks, and in terms of R and application publishing with Shiny, Posit seemed to be a valid and good option regarding costs, licensing, and all else.

These features make my work easier and more efficient because, before Posit, we used to build this platform in ECS on AWS and host a Shiny container to publish Shiny applications, tying it to our internal Akana for authentication; whereas now, with Posit, there is a one-click publish option on the IDE and to Connect, and it easily integrates with OAuth including Microsoft Entra ID, allowing clinical experts or anyone to easily share applications within the company, which helps scale and share Shiny or Streamlit applications as well as any Python and R applications.

My advice for others looking into using Posit is that if you are having an R workshop and also include Python, and need a single place for developing and deploying applications without much platform overhead, please consider Posit. I would rate this product a 10 out of 10.

My main use case for Posit is data science workflows, automation, and using it for model building and data exploration.

A specific example of how I use Posit for model building and automation in my workflow is that we use it for a forecasting model for retail customers to estimate the effects on volume of changing any number of input levers such as price, distribution, or promotion activities.

The best features Posit offers are the embedded RStudio and Python version control, which can sometimes be a main gotcha for Python users.

Python version control has helped my team because we never have to install Python at the user level; it is all already installed at the system level. The RStudio integration is great because I can tap Posit workbench server compute, which is significantly higher than our laptops, to scale our processes up if we are working with a particularly large dataset.

Posit has positively impacted my organization by helping us schedule processes to run automatically. It is also a very fast way to get a visualization in front of a stakeholder versus having to rely on a Power BI or a Tableau dashboard. Data scientists can build visualizations themselves and get them in front of stakeholders very quickly.

In terms of a quality standpoint, Posit has helped out quite significantly.

Posit could have better integrations with a couple of components. Posit has an integration with Snowflake, but an integration with Redshift and a better integration with Databricks would be very helpful.

I have been using Posit for ten years.

Posit is stable.

Posit's scalability is quite good. Our deployment is on an EC2 instance, and we can easily configure the environment to increase in size if needed.

Posit customer support is good. You have a dedicated sales rep associated with your account.

I would say instead of a cost savings, Posit has provided revenue generation. For example, the forecasting tool allows us to meet with relevant retail partners and show it to them, and it increases their business share with Southern Glazers.

My experience with pricing, setup cost, and licensing is a fairly good experience. Posit does seating based in batches, so for example, you could purchase ten seats, fifteen seats, or twenty seats. The pricing was very transparent and they have three different product lines, or at least they did when we purchased it. We selected two of them, and it was overall a pleasant experience.

My advice for others looking into using Posit includes a couple of points. First, make sure that the environment is sized correctly, so a rule of thumb could be like twenty gigabytes of memory for every user you plan on onboarding. Another point is making sure that you integrate SSO in the platform so that people can use their Windows sign-ins or their Mac sign-ins, whatever system you have, versus a third-party credential. Finally, make sure that you install several versions of R and Python on there because sometimes you might have a need for an older version of a package, and you need to manually roll back to a previous version of R or Python to be able to utilize it.

I would rate this review a nine.

My main use case for Posit is to support data science teams by managing and maintaining Posit platform infrastructure. For example, I work as an administrator at Posit Workbench and Posit Connect environments, ensuring that they are stable, scalable, and accessible for users who run R and Python workloads, while also managing deployment, monitoring, and troubleshooting of applications hosted on Posit Workbench.

Our users primarily run data science and analytics workloads using both R and Python on Posit. Typical workloads include statistical analysis, data exploration, and building predictive models. A common workflow involves data scientists using Posit Workbench to analyze large datasets, develop models in R or Python, and publish results as dashboards or APIs through Posit Connect, which could be interactive Shiny applications or Quarto reports or Python-based APIs that help teams visualize insights or automate parts of the analysis process.

Our team interacts with Posit not just from the user perspective, but also from the platform operations standpoint. We manage the infrastructure, ensure high availability, and monitor performance so that data scientists can focus on their analytical tasks without worrying about the underlying environment, particularly since they are also working on drug discovery. I belong to the pharma domain, so Posit is very useful for them. We also support deployment workflows, help troubleshoot issues, and integrate Posit with other enterprise systems such as authentication services and cloud infrastructure, ensuring the platform is reliable, secure, and scalable for all users.

One of the best features Posit offers is the integrated environment that benefits data science teams. Posit Workbench provides a powerful and flexible development environment for both R and Python, making it easy for users to work on data analysis, modeling, and visualization within a single platform. Another standout feature is Posit Connect, which simplifies deploying and sharing data science outputs such as Shiny dashboards, Quarto reports, and APIs, bridging the gap between development and production by allowing teams to publish their work for a wider audience. From an operations perspective, the platform also provides strong capabilities for ensuring secure access through enterprise integrations like authentication systems.

The integrated environment significantly helps our team in day-to-day operations. It allows users to work with both R and Python in a consistent workspace, reducing setup time and facilitating team collaboration on projects. Since everything is centralized through Posit Workbench and Posit Connect, users can develop, test, and deploy their work without needing multiple disconnected tools. From an operations standpoint, the enterprise integration, such as authentication through our identity system, ensures secure access and proper user management, simplifying onboarding and offboarding processes. Additionally, the centralized platform makes troubleshooting easier, enabling us to monitor applications, review logs, and quickly identify issues when something goes wrong, ultimately improving collaboration, platform reliability, and operational efficiency for our team.

One small but handy feature is how easy it is to deploy and manage applications through Posit Connect. Users can quickly publish dashboard reports or APIs, with the platform managing versioning, access control, and scheduling, making it convenient for teams to share insights without complex deployment setups. Another useful feature from an operational standpoint is the monitoring and logging capabilities, which help us quickly identify performance issues and troubleshoot problems with hosted applications, providing great value in an enterprise environment. Additionally, in Posit Workbench, the project sharing option allows us to directly input the username or the SID to share our working projects.

Posit positively impacts our organization by providing a centralized and reliable platform specifically for data science workflows. It allows our data scientists and analysts to run, develop, and share their work within a consistent environment, improving efficiency and reducing time spent on environment setup or infrastructure issues. One specific improvement we have noticed is that teams can quickly turn their analyses into deployable applications like dashboards or reports using Posit Connect, which makes insights more accessible to business stakeholders and accelerates decision-making. From an operations standpoint, it also helps us manage and monitor the platform more effectively, ensuring high availability and secure access for users across the organization.

Posit works well for our use cases, but a few areas could be improved. One area is resource management and visibility for hosted applications. In environments with many applications and users, it can sometimes be challenging to quickly identify which workloads are consuming the most resources. Additionally, improving deployment workflows and automation options for larger enterprise environments could further streamline how teams manage and update applications at scale.

One improvement that could smooth day-to-day operations is enhancing observability and monitoring capabilities within the platform. While current logging and metrics are useful, having more built-in dashboards or deeper insights into application performance and resource utilization would help the platform team manage the environment more efficiently. Another area for improvement is automation and deployment workflows for larger enterprise environments. Features that simplify CI/CD integration or bulk management of applications would make it easier to maintain and scale the platform as usage grows. While the platform is strong, enhancements in operational and monitoring areas would make it even more effective for the enterprise team.

I have started working at MSD more than six months ago, and I hold over ten years of experience in my current field.

Posit is stable in my experience.

Posit is efficiently scalable, and we are happy with that.

The customer support for Posit is good.

I did not use a different solution before Posit.

We have seen a positive return on our investment with Posit, mainly through time savings and improved productivity. Posit simplifies the deployment of analytics outputs and reduces time spent on environment setup and troubleshooting, allowing teams to focus more on analysis and delivering insights.

My experience with pricing, setup cost, and licensing for Posit has been great, as I would describe it as generally positive. The licensing is straightforward for our enterprise environment and aligns well with our teams.

Before choosing Posit, we did not evaluate other options.

I have covered everything that matters most to me regarding Posit. I would rate this review an eight out of ten.

My main use of Posit is to deploy Python Flask applications for internal use of tools within our company. I deployed a search query and engine that scientists can use to access a database, query results of research literature that is out there, and run an AI model on these results to help them create better systematic reviews.

Seamless deployment of Python web apps was a significant advantage. Being able to manage the server and web application made my job considerably easier and made it easier for others to use and utilize the tools. With Posit Connect, we were able to incorporate AI models into our tool, and Posit was able to successfully handle the computing and processing necessary to run these applications for multiple users simultaneously. This allowed for seamless integration and wide acceptance of the tool.

As the only programmer on a team of biological scientists, it made the process considerably easier to have a tool that would organize everything and make the whole process more seamless. For the AI model integration, this was built into the Python application. Since Posit is able to install the libraries that come with Python within our requirements file, this made it very easy to have the same environment for all users and to quickly update versions or debug and re-upload new versions of the Flask app as necessary.

Posit has significantly increased the productivity of our team by allowing us to incorporate AI tools and other computational tools to reduce the stress and workload of scientists so that they can continue to do science without having to worry about organization and structure.

One of the downsides of using Posit Connect was the need for licenses for each individual user. This did become tedious to get the interested users' licenses to use the tool. We had a lot of interest but not a lot of trial licenses for people to try out the tool and know if this would be worth their time or effort. A trial system for licenses that would make it easier to try out people's tools would be a great benefit to better adoption.

The time savings is an incredible feature and the horizontal deployability is great, but development on it can be somewhat challenging and it does seem to have a few areas where it could use some improvement to make it more robust.

I have used Posit for about six months.

Posit is very scalable.

Customer support was satisfactory.

Neutral

Since adopting Posit, by using the tool, we have saved about 57% on the amount of time needed to compile a systematic review. This was previously done completely manually. By incorporating a Python app, we were able to allow one scientist at a time to achieve this time savings, but by placing this web app on Posit, we are able to allow access to many scientists at the same time. The time saved average was about 57%. I do not have any metrics on money saved or fewer employees being needed.

We evaluated Snowflake. Posit was good for being able to deploy the Flask web app. Snowflake required containerization and that was a step that we were not ready for at the time. To be able to get this into the hands of scientists faster, Posit was the better option at the time.

Trial and error is very easy with this tool, so it is great for developing and sharing new features with a close-knit team. I would rate this product an 8 out of 10.

My main use case for Posit is that I support Posit Workbench, Posit Connect, and Posit Package Manager. I manage the installation, upgrade, and user support for these products. For example, I support users with issues such as login and creating new sessions.

Posit Workbench serves as a shared IDE for users and stands out as one of the best features Posit offers. Posit Connect functions as a dashboard for publishing applications and sharing them with colleagues, giving users the option to run applications with different access levels. I have control over users who can publish applications, allowing some to only be viewers while others can be both publishers and viewers.

Posit Package Manager acts as a proxy for repositories, primarily supporting internally developed packages. This feature works well whenever a user has a new version of their package. RSBM functions as a CI/CD pipeline; whenever a user commits or creates a new tag on their package's source code, it automatically generates a new version on the server. Without it, automation tools such as Jenkins would be needed.

Posit Workbench supports both R and Python, and I accommodate many users with HPC, allowing them to log in and choose resources. I have user profiles that impose resource limitations for some users, while others can access larger resources. Posit Workbench supports multiple versions of R through environment modules.

The publishing dashboard helps users share applications that perform calculations and analyses on data sets, enabling them to collaborate without needing to install NGINX for every application. Posit Connect simplifies this by providing control over who can access, contribute to, or modify applications, which is a great feature.

Recently, Posit Workbench supports MCP to integrate AI models, but unfortunately, it is only available with Positron IDE, which currently lacks support for environment and HPC modules. I am waiting for a new version that will support these features, with promises from support indicating it might arrive in the first quarter.

Posit has positively impacted my organization, which is actually a customer I support. I am managing multiple clusters across different regions with a large number of users utilizing Posit Workbench for data manipulation and analysis. Posit Workbench excellently facilitates application publishing and sharing, including predefined applications that serve as learning tools for users to connect with Posit Connect and create their own applications. Users can test their scripts on Posit Workbench and publish them to Posit Connect once satisfied. There are different targets for publishing, such as test servers for user feedback before moving to production. Posit Connect also maintains version history, allowing users to roll back to prior versions if bugs arise. Recently, Posit Package Manager has started integrating MCP, but there is a need for further integration with AI tools.

Posit could be improved through more AI integration. The documentation recently added an AI module for search functionality, but I hope for it to provide specific issue resolutions and suggestions, not just basic searches.

I have been using Posit for approximately two years.

Posit is stable.

Posit's scalability is good enough for my needs, managing different clusters across various regions for development, testing, and production. I have recently added a new cluster in a third region and have room for more worker nodes in Posit Workbench. While Posit Connect can scale vertically, horizontal scaling may be a prospective feature to consider.

Customer support for Posit is responsive but sometimes slow. I encounter delays when quick responses are needed for urgent issues.

Neutral

I am not certain about the return on investment metrics, as that falls outside my scope, but my customer heavily relies on Posit applications. They may consider moving away from Posit Package Manager for their in-house Artifactory but still plan to integrate Posit Connect and Posit Workbench into their workflows.

I cannot speak much to pricing, setup costs, or licensing challenges since I joined after those arrangements were made, but I use the same licenses for upgrades or server migrations, and the integration remains straightforward.

My advice is to start with a small number of resources, such as one master node and one worker node, before adding more based on user demand. If considering cloud options, note that sensitive data often must remain on-premise due to upload limitations. My overall review rating for Posit is 8 out of 10.

My main use case for Posit is performing statistical analysis, data filtration, data management, and fetching raw data from EDC tools.

I use Posit for performing statistical analysis using various statistical tests, and I also create apps using the Shiny package that Posit provides. I use Posit for many other data-related tools as well.

I have created some apps that are ready to use, which are click-and-run type applications. A non-statistician does not need to write any code; they simply click and run, and the analysis results are downloaded into PDF and Word files.

Posit fits perfectly in our workflow because it has so many functionalities and features that a statistician will really appreciate.

The best feature Posit offers is Shiny app management, Shiny, and many other statistical and app development tools.

Shiny app management, such as shinyapp.io, helps me administer the applications I have created using Shiny, including providing access to users, setting time periods, and controlling how much the app will be used by someone.

I also use Posit Studio to perform statistical analysis by writing code.

The version control of Posit is not very good, and there are so many updates coming from Posit. That could be one improvement Posit can make.

I have been using Posit for more than three years.

Posit is very stable.

Posit's scalability is very high and very good.

Customer support of Posit is excellent.

We have not used any different solution before Posit.

We have obviously seen a return on the investment. In comparison to other tools, we have saved money, and by using Shiny apps, we save time as well. We need fewer expert statistical experts, and normal people can perform analysis.

Posit is a very useful tool, and while it is an open-source tool, it sometimes offers a free environment, and the costing of Posit is very good in comparison to other statistical software.

Posit has saved and reduced the cost of performing statistical analysis in comparison to other software.

We evaluated SAS and SPSS before choosing Posit.

I would advise others to check the full capability of Posit because Posit has expanded so many things. Posit has many features and many apps to provide, and Posit is very competitive in the current market scenario.

The experience was very positive, and I am happy with the costing.

I chose a rating of 10 out of 10 for Posit because of the features Posit provides and the costing Posit provides, which is really unrealistic and appreciated in the current market scenario. Everything else is really good.

My overall rating for this review is 10 out of 10.

My main use case for Posit involves coding, executing, and deployment.

A quick specific example of how I use Posit for coding, executing, or deployment is coding in R and running it on Posit Workbench.

I typically run all kinds of projects or analyses on R through Posit Workbench, mostly associated with multi-omics datasets, including data science, statistical analysis, and reporting.

The best features Posit offers are that it is very intuitive. The interface is what makes Posit feel intuitive for me, and I find the interface helpful.

Posit has impacted my organization positively because it is very intuitive and easy to work with, and it also uses R, which is the standard for R-based coding in the cloud.

Posit has its Jupyter Notebook-based interface, which can be improved significantly. There is a way I could use Copilot-related features on Posit Workbench, but due to enterprise limitations and privacy issues, those are blocked out, so that could definitely be improved.

I have been using Posit for more than 10 years.

Posit is stable in my experience.

As for Posit's scalability, I would rate it a seven.

I have not interacted with customer support that much, but I would give it a rating on the higher end.

Positive

In a previous company, I used AWS's JupyterLab and those solutions before switching to Posit.

I was not fully involved in the pricing, setup cost, and licensing process because it was done for the whole company, but I can say it is on the pricier side compared to some of the other solutions.

I have seen a return on investment because Posit is the standard in the field for coding in R and deploying applications like Shiny apps, so a lot of time is saved with a very quality product. Although some other tools associated with AWS work more seamlessly with AWS and are very good for Python-based coding, Posit is still the standard for R-based coding.

I did not evaluate other options before choosing Posit in my current company.

I would rate Posit an eight on a scale of one to ten. I rate it an eight because I never give a ten; there is always room for improvement. My advice for others looking into using Posit is that it is seamless and it is a pretty well-baked cloud-based solution that is quite good. Posit was purchased through the AWS Marketplace for the whole company through a B2B enterprise solution. My overall review rating for Posit is eight.

Neutral

Our primary use case for Posit involves end-to-end data processing. We start by pulling data from various sources like CSV, Excel, Snowflake, and Databricks. We clean the data, analyze it, and visualize it using tools like ggplot, Shiny dashboards, or R markdown documents. We also use Posit for building models and sharing findings for better insight within the team.

Posit has significantly improved our ability to collaborate. We can use it to share results and findings instantly within the team and develop Shiny applications for external review. This has led to quicker project iterations and enhanced internal communication and collaboration.

I have found Posit’s integration with R, such as the autocomplete feature and the help panel, to be invaluable. These features allow a smooth user experience and eliminate the need to switch between programs for assistance. Additionally, the platform’s ability to build and test Shiny applications is highly beneficial.

I would like to see improvements in SSH capabilities. The potential to use local environments on powerful cloud resources through SSH, as seen in other tools like Visual Studio Code, would enhance Posit’s usability for hybrid environments.

I have been using Posit for about ten years, including both the free RStudio desktop version and the enterprise version.

Posit is generally very stable. It becomes unresponsive only with excessively large datasets or complex computations when our computing resources fall short. Otherwise, it provides a reliable working environment.

The scalability is excellent. Using AWS SageMaker, we can easily expand from simple setups to more powerful configurations with multiple GPUs and extensive memory resources. This flexibility accommodates various project requirements.

The customer service is responsive, with ticket responses typically within 24 hours on business days. We also have an account executive for direct communication, which is very beneficial, although their availability is limited to standard business hours.

Positive

At a previous company, we used different software. It did not meet our needs. As an R user, RStudio naturally complements my workflow, making Posit a suitable choice.

The initial setup of the desktop version of Posit is straightforward and easy. However, the cloud setup for Posit Workbench was handled by the technical team.

Our technical team handled the setup of Posit Workbench on the cloud and described it as straightforward.

One of our significant projects using Posit helped business partners prioritize tasks more effectively, saving the company approximately $3 million. This illustrates substantial organizational value and ROI.

Currently, the cost of Posit is reasonable and affordable, although there is an anticipated increase in pricing.

Other analytic software solutions were not evaluated extensively, as our team primarily consists of R users, making Posit a logical choice.

I would recommend new users to engage with Posit's sales and support teams, as they provide excellent assistance and insights into the product. Posit offers reasonably priced solutions compared to competitors, making it an advantageous choice for data science teams.

I'd rate the solution nine out of ten.

We use Posit to deploy applications developed in R and Python. Our team creates applications and models, which they then host on GitHub. We assist them in deploying these models and provide the necessary access and support.

The most valuable feature of Posit is that it allows users to host different models in one place, making it easy to share with colleagues and teammates.

I have been working with the product for two to three years. 

We can easily add users by providing AD group access and assigning different access levels—publisher, administrator, or viewer—based on their needs. My company has two instances: production and development. We have around 140 users for production, while for development, we have 180. 

I regularly connect with support whenever there are any issues. However, there is a time lag due to time zone differences. 

If you're setting up Posit for the first time, it's quite easy. They provide a detailed admin guide with step-by-step instructions, making the deployment process straightforward.