Palantir Foundry Reviews

I have used Palantir Foundry for three years.

My main use case for Palantir Foundry involves data engineering-related work, specifically ingesting data from multiple sources, cleaning, transforming, and loading it into ontology.

For multiple projects I have worked on, the process with Palantir Foundry follows the same basic flow: getting the data from multiple sources such as an API or AWS S3, different databases, and file systems. We ingest the data from those sources, then perform cleanup on the data, and then load it into ontology from which users will be using the data.

The best features Palantir Foundry offers include being a one-platform system with integrated scheduling, all the necessary DevOps utility, and GitHub integration which allows us to create multiple test branches within the system. Another great aspect is the end-to-end data lineage availability for the system.

The most valuable feature I find in Palantir Foundry is the ability to go back in time and view data, which you can call time travel, where we can see the data at a previous point in time. This helps in data validation or rollbacks in case of any issues. I think this was the best feature I appreciated along with the integrated lineage system.

Palantir Foundry has positively impacted an organization where I previously worked, as it was a platform where developers, DevOps, and users could make changes together.

One way Palantir Foundry can be improved is by addressing issues with back-end changes. There were cases where changes made to Palantir Foundry would cause failures across all platforms. This situation caused problems and downtime for developers and data users due to changes made by the Palantir team. It results in downtime. Another concern is that, as a developer, I do not have a back-end view of what is happening in Palantir; it feels like a black box. It would be better if developers had that visibility.

An improvement would be that in case of any changes done by the Palantir team, those changes need to be tested thoroughly so there are no downstream impacts, ensuring that the business is not affected by any modifications in the system.

I have been working in my current field for over ten years.

I was previously using Teradata. I switched from Teradata to Palantir Foundry due to an organizational decision.

I do not have anything else to add about my main use case or how I use Palantir Foundry day-to-day.

I have not worked on governance, so I do not have much insight into the governance and security capabilities of Palantir Foundry.

I cannot give my opinion on the accuracy and reliability of output because I have not used the AI capabilities.

For day-to-day operations, this was a data migration project for an ERP rollout implementation where we automated the data pipelines for the migration.

We mainly used the low-code solution that they had for the data engineering pipeline, which is a pipeline builder that helped us ramp up the project timelines rapidly. It had automated refreshes from both the source and the target, and everything was automated once it was built. It had everything in place from analytics, data, and solution to BI solution. The low-code solutions made our lives easier because not everybody is too technical to get started and the barrier to entry is very low. In that sense, we had a lot of pre-built solutions offered which helped us ease out our timelines and bring non-technical stakeholders into the work to get things done.

Even the integration was mostly pre-built. All we had to do was use point and click solutions. It was quite smooth to begin with and we were able to do a lot of use cases, including building a front-end application, BI application, and troubleshooting. Tracking the lineage of the data is also very robust.

Palantir Foundry had a really cleaner UI for us to get started on the project. It had everything in place from analytics, data, and solution to our BI solution. The low-code solutions made our lives easier because not everybody is too technical to get started and the barrier to entry is very low. In that sense, we had a lot of pre-built solutions offered which helped us ease out our timelines and bring non-technical stakeholders into the work to get things done. Palantir Foundry has everything integrated from end to end. We did not have to do a lot of things outside. We could just integrate everything that we did from a data standpoint into Palantir Foundry. Even the integration was mostly pre-built. All we had to do was use point and click solutions. It was quite smooth to begin with and we were able to do a lot of use cases, including building a front-end application, BI application, and troubleshooting. Tracking the lineage of the data is also very robust.

In terms of improvements, it helped us improve our data migration timelines by approximately 60 percent and improved the data accuracy and addressed the issues upfront by approximately 85 percent. It helped us standardize our data solution for the project.

There are some limitations when dealing with a large-scale project. It might not be very feasible to do so because we want to optimize it to a very micro level. There are some limitations to that when using Palantir Foundry.

I have used Palantir Foundry for around 18 months.

Scalability is good. On a scale of 10, I would give it about a seven.

Customer support is good if we are involved with an FDE for our project. I would say about eight in that regard.

We were using Excel. The reason we switched was due to issues with Excel because when dealing with large amounts of data, Excel is not very robust and we usually ran into data accuracy issues.

For our project, we usually needed about six employees without a Palantir Foundry solution. We saved approximately 20 to 35 percent in man-hours needed and the timing improved our project timelines by approximately 50 to 55 percent. We saw a good return on investment in that regard.

If we are using an Excel solution or something which is really manual, Palantir Foundry could help ease our lives and defining the right needs early on for our project could help us nail down those design details for the project and find the right solution in Palantir Foundry. I would rate this review an 8.

My main use case for Palantir Foundry is for my current client, where I am dealing with the customer claims system, using both the data engineering and UI sides of Palantir Foundry, managing the end-to-end application flow from the ingestion to the UI side.

With my current company, we have claims sent to the system by the dealers, as it is an automobile engineering company, and we track all these claims, creating ontologies for part returns, incidents, and recurring issues, which allows us to effectively manage the tracking process.

The best features Palantir Foundry offers are data transparency and data flow, allowing us to easily navigate and understand the flow of data from the ingestion stage to the UI stage, alongside the ontology feature which helps in understanding data in layman and business terms.

Palantir Foundry's ontology feature helps my team and the pre-analysts who use the UI workshop by making it easier for us to understand the objects and use cases, allowing us to identify what is behind the issues and gain business-level awareness.

Palantir Foundry has positively impacted our organization by making our processes straightforward and fast-moving, especially with features like AIP, an AI-powered assistance, and the low-code ability to create transformations quickly, which has benefited me significantly.

I am not very aware of the current advancements with Palantir Foundry, but I think improvements could come from proper AI assistance, such as giving prompts to execute the whole end-to-end flow or debugging issues, which would greatly enhance the current workflow.

We do have vast documentation, but I believe it should be more easily accessible, allowing me to quickly find the documents I need without navigating the entire Palantir Foundry learning system.

Palantir Foundry is very accurate, but I would have doubts about its reliability because there have been instances in my current job where we experience untimely application downtime, which has impacted the business significantly, so I think reliability needs improvement.

I have been using Palantir Foundry for three or more years.

Palantir Foundry is very stable.

Palantir Foundry's scalability is substantial; I manage huge data with large transformations, so I believe it is highly scalable.

Customer support is an area they need to improve on; currently, we create tickets, and they respond within twenty-four hours, but I think quicker responses are essential for such a fast-paced tool.

I have never used a similar solution before Palantir Foundry, which is where I got introduced to data engineering, making this my first tool in that field.

Palantir Foundry is far better than all the current competitors or tools out there, though there is still room for improvement. I believe Palantir Foundry is very well governed, as indicated by its use among large companies and government entities, which suggests it is safe and highly governed.

I think Palantir Foundry is deployed on-premise in my organization.

I do not have much insight on pricing, setup costs, and licensing, as I am just a developer.

I would recommend Palantir Foundry; it is a top-notch tool currently available in the market and is a valuable tool for all data engineers. I would rate Palantir Foundry an eight out of ten.

My main use case for Palantir Foundry involves end-to-end solutions, starting from data ingestion and transformation. I also use AIP logics for data transformation and ontology management.

In my day-to-day work, I create or modify back-end functions using TypeScript to create action types under the ontologies to add new rows, which means adding new objects or updating existing ones. I also use AIP logics to accomplish the same tasks.

While using AIP logics, I work with machine learning LLM prompts to get results based on text from various unstructured files, such as PDFs. I use the LLM to extract data for analysis in workshops for Contour.

Palantir Foundry offers many good features, including ontology management and user-friendliness. I find AIP Logic LLMs quite useful for small tasks, such as finding patterns or trends between two data sets. I frequently use the Object Explorer, now called Site Insight, which is useful for comparing data and performing sanity checks.

The accuracy and reliability of Palantir Foundry's output are quite good. During analysis, we find its accuracy satisfactory.

Palantir Foundry has positively impacted my organization by providing good support from Palantir teams, facilitating the development of many new solutions, building our UI and web applications, and significantly enhancing our productivity. Overall, we enjoy using it.

When issues arise, I often have to rely on Palantir support team rather than solving them myself, as some team features require heavy reliance on their support due to a lack of comprehensive documentation for us to configure things on our end.

Although there are enough tutorials and Palantir study materials available, we still face many struggles when working on projects within Palantir and often need to consult the support team, particularly regarding synchronization and visibility on back-end processes.

I would rate Palantir Foundry a seven because it needs to be more open. As a developer, I find the limited documentation and less resource availability restrictive compared to other options such as AWS.

My rating of seven is influenced by our heavy dependence on Palantir support engineering team to resolve issues that we cannot. The flexibility of some features, such as ontologies, could improve.

I have been using Palantir Foundry for the last nine months, since September.

Palantir Foundry is stable.

Its scalability is good.

Customer support is good, and we interact frequently with the support team.

I would rate customer support an eight.

My advice for others looking into using Palantir Foundry is to have customer support at the beginning, as venturing into it without much knowledge could be challenging.

I did not previously use a different solution before Palantir Foundry.

I see that there are metrics showing we have saved money using Palantir's features, indicating a return on investment and improved efficiency after implementing Palantir solutions.

We prefer developing our functions and modules in Palantir rather than using outside sources, leading to the recent development of two or three models in Palantir that previously relied on outside sources. This shift saves costs and improves the stability and consistency of our services.

The code repository is critical for us. Our entire back-end relies on it, and everything we do or write in functions is stored there, making us completely dependent on that repository.

I believe the governance and security of Palantir Foundry are quite secure, which played a key role in our choice of Palantir. While its AI capabilities are not as advanced as other leading tools such as ChatGPT, it is still useful and shows improvement, especially with the LLM feature on AIP Logics.

I rate Palantir Foundry a seven overall.

My main use case for Palantir Foundry is that we work on the ETL process and create agents and dashboards, so my work mainly involves ETL, Extract Transformation Load, and data processing.

A specific example of an ETL process I worked on with Palantir Foundry involves health sector data where we track various KPIs, such as how much time a patient adheres to a particular therapy. We start with raw data that we process using various transformations, and then we present that data in an aggregated form as a reporting table consumable by dashboards. This is how we used Palantir Foundry for the entire data transformation logic.

The best features Palantir Foundry offers are creating agents and dashboards, as there are many predefined models we can directly use when creating agents, which are very useful. Similarly, creating dashboards is easier in Palantir Foundry.

While creating agents in Palantir Foundry, we use CLAUDE, which simplifies the process significantly. We select the model from a dropdown and define the dataset the agent will utilize. It is a very straightforward task in Palantir Foundry.

We mainly used three things from Palantir Foundry—creating agents, dashboards, and ontology, which is a great feature that connects various data sources. Once the ontology is created, we can create our agents and dashboards and everything else needed.

Palantir Foundry has positively impacted my organization by saving time in creating agents and dashboards and definitely enhancing collaboration. The time consumed in Palantir Foundry is significantly less compared to creating agents using traditional coding languages.

Generally, if it takes around one week to create a full-fledged agent through coding, with Palantir Foundry it takes approximately one day, meaning a rough estimate of saving four days in a week. Regarding collaboration, when you work on a common platform, another great feature is that Palantir Foundry allows collaboration without needing separate Git accounts or other tools since everything is on a single platform.

When we were using ETL with Palantir Foundry, we found we had less freedom compared to Cloudera, where we had more liberty in using various configuration parameters of Spark, allowing us to tune our jobs accordingly. In Palantir Foundry, we realized that ETL is not a great use case while creating agents and dashboards was better. We faced many challenges in ETL that we did not face earlier in a big data Hadoop environment, such as Cloudera; we eventually removed that ETL process.

I feel that if developers had more freedom to experiment, it would enhance Palantir Foundry. Specifically, if the ETL process could provide more freedom concerning Spark configuration parameters, that would be beneficial.

I worked on three parameters: dashboards, ETL, and creating agents. The latter two functions really work well, but we faced challenges specifically in ETL. Additionally, the costs are substantial; I believe making Palantir Foundry more approachable for the general public would be beneficial.

I have been using Palantir Foundry for around one year.

Palantir Foundry is stable.

Its scalability is good and pretty scalable.

Customer support from Palantir Foundry is also good; we collaborated well with their team.

We were not using any different solution before; we were using Cloudera for ETL and big data and wanted to give Palantir Foundry a try.

We did not evaluate any other options before choosing Palantir Foundry, as these decisions were mandated by our company, and we heard good reviews about it.

I don't have information on return on investment metrics; it's too early for such statements, as we have only been using it for one year and spent much time experimenting, so I don't think we have ROI at this stage.

Regarding pricing, setup cost, and licensing, while those are determined by our admins and upper management, I know we are paying substantial charges, and if those could be reduced, it would be more accessible for people.

I would advise others to start using Palantir Foundry and experiment thoroughly before signing any contracts. My review rating for Palantir Foundry is six out of ten.

I used Palantir Foundry in a different role prior to my current role; however, I do not use it in my current position.

In my previous role, my main use case for Palantir Foundry was data extraction, ingestion, and cleansing for use with machine learning models. A specific use case involved extracting data from multiple public health sources of patients and providing healthcare providers with legal data and logistical data about healthcare providers to analyze which healthcare providers were guilty of fraudulent billing.

Other use cases included ingesting veteran data for different kinds of machine learning using clinical data for veterans; however, the primary use case most recently was fraud detection for prevention of fraud, waste, and abuse in federal health agencies using data from NPI, National Provider Identification, for various providers along with state licensure data for fraud detection.

Based on my experience, Palantir Foundry is extremely easy to learn and adjust to since it is primarily a closed system, meaning that all the functions for data ingestion, data cleaning, machine learning, and related tasks can be performed from within the same system. This closed nature made it easier for my junior engineers to use the system for working on the actual work of ingesting the data.

The Ontology was one of the more helpful features alongside ease of use. Palantir Foundry has impacted my organization positively; the general output is comparable to other systems, but the value came more from the speed of querying and building tools to extract and transform the data.

The main benefit was having something that was faster to get to the result set, resulting in less manual work and fairly automated processes. The Ontology system was helpful for our teams.

This system needs more powerful tools for the power user; I feel the system is very well designed for the introductory level but could have finer-grained controls for data engineering experts and machine learning experts at the power user level.

I have been working in my current field for eight months.

There were no issues with stability in our experience; we had no problems with that.

We use Palantir Foundry for fairly large data sets; however, we did not scale to full size since we were building POCs and supporting the federal government in their use cases, and we definitely did not have any issues with scalability on our side.

I cannot comment on customer support since I did not have to reach out to customer support for anything during my use of this system; however, I can comment on training and staff technology partnerships, as the Palantir trainers at the federal government were extremely friendly and very helpful.

We did not have a different solution for this particular use, as the federal client was using a different use case and using Palantir Foundry from the outset; ours was a different use case for which Palantir Foundry was the first technology selected.

Overall, I supervised a team that used Palantir Foundry, and the primary benefit here was that there was faster adoption of the technology and faster time to results, primarily with the technology enabling us to have quicker POCs, proof of concepts, using the data system.

I would say that there was significant time savings with Palantir Foundry; these may have translated into money savings, but typically we were more focused on the time savings.

I was not typically responsible for pricing, setup cost, or licensing, as I was primarily the leader of the project that implemented the data ingestion, cleaning, and machine learning tasks and led the team; pricing and infrastructure setup would have been handled by other technical leaders in other domains.

We did evaluate other options, but Palantir Foundry was the natural fit for this particular use case, so we did not evaluate other options such as Databricks particularly seriously.

My advice to others looking into using Palantir Foundry is to ensure that new users are taught fundamentals of data engineering, data ingestion, and data science before they go into the tool, so they do not have to learn it inside the tool but already come in knowing what to expect; learning from within the tool would create a more restricted learning system, and while the tool is powerful, it should not be used for learning systems but for developing systems. I provided this review a rating of eight.

My main use case for Palantir Foundry is that I am learning it through all the resources at learn.palantir.com, and I currently have one certificate while working towards two more certificates. Additionally, I am trying to create the ontology of the ticket management for the vertical of my company.

I am using Palantir Foundry to assign the best or the perfect developer for every ticket. When a client creates a ticket, there are many developers who could work on it, but some developers perform better with certain types of tickets, know the client, or have more available time. Currently, this decision is a human one, but I am creating an ontology in Foundry to enable an AI agent to propose the best choice.

I am very excited to create a more comprehensive ontology for my company.

In my experience, the best features Palantir Foundry offers include the ontology and the possibility to create a digital twin of your company. It operates in a way that allows you to integrate a vast amount of data from diverse sources and create an ontology that reflects the real aspects of your company, ultimately leading to the creation of applications, which become most impressive when combined with the ontology and data management.

Palantir Foundry has positively impacted my organization by significantly speeding up processes. I am starting with Foundry, and I believe that in the future, I will have KPIs. At this moment, the process of deciding which developer to assign to a ticket is very fast, which makes decision-making clearer, quicker, and more accurate.

I have faced some challenges while building the ontology and setting up the AI agent in Foundry. It is somewhat difficult when you start to work with Foundry, but I believe that now I am more comfortable with both the ontology and Foundry.

I think Palantir Foundry is a perfect system; however, it does require more time for learning. It is a very complicated platform, and you need to understand a lot about how to use it and the underlying thinking behind Foundry. I need time and extensive learning processes.

More onboarding processes or training processes would be great.

The issue is not whether you use LLMs like GPT or cloud services like Gemini. All LLMs and AI agents work as commodities within the ontology and applications.

I have been using Palantir Foundry for one year, as I started last April of 2024.

Palantir Foundry is stable in my experience, with no issues regarding downtime or reliability.

I believe that Palantir Foundry is scalable and can handle growing amounts of data and users without issues. I am confident there is no limit as long as you have the processing, computing, and data capabilities, although I do not know where that limit might be.

I only created a ticket once, and the customer support was incredible. They responded within a few minutes and resolved the ticket immediately with a call.

There are other software solutions for automation or data manipulation, such as Make, but none match the depth of concepts or the extensive functionalities of Palantir Foundry.

Creating an ontology requires you to consider the sources of your company's health.

It is too early to determine if I have seen a return on investment. I think that in a few months, I will have more insights.

It is very difficult to know the cost of the license. I do not really know the costs that I will incur in the future, as that area remains very unclear. I think it is important to clarify and provide information regarding pricing.

I would advise anyone looking into using Palantir Foundry to learn thoroughly, start with learn.palantir.com, and complete all courses while reading all documentation, as it is very important to grasp the system before starting to work with Foundry.

I rate Palantir Foundry a ten out of ten because I believe it represents the system operation of the internet. It is something truly new, a new technology offering a novel methodology for understanding and utilizing technology within companies. I consider it the best innovation I have seen in the last ten years.

The accuracy and reliability of Palantir Foundry's AI output is great.

My main use case for Palantir Foundry is using AI to connect the shipyard and build AI agents that improve the operational throughput.

A specific example of how I'm using Palantir Foundry to connect the shipyard and improve operations is that the entire asset ecosystem in Drydocks has been revamped by using Palantir Foundry. I have loaded all the IoT data from those assets into Palantir Foundry and am using it to track all the live updates from all the assets to ensure there are no outages and no shortage of materials to maintain these assets.

Regarding my main use case, I am using engineering drawings, which usually require a lot of human intervention and human study to build items when requirements come from clients. I am using state-of-the-art AI models in Palantir Foundry to read these engineering drawings and provide much quicker analysis and insights to the engineers before they review the drawing themselves.

The best feature Palantir Foundry offers is the Ontology. Ontology stands out for me because I have a connected shipyard vision. Ontology allows me to truly connect the shipyard, starting from tendering to engineering to production to quality control, with each of those elements properly connected using Ontology.

Palantir Foundry has positively impacted my organization by increasing the operational throughput of the yard, which focuses on building new ships or repairing existing ones. This entire process has been streamlined, though as far as the pilot is concerned, I have not fully gone into production yet.

Palantir Foundry can be improved by providing more third-party application support and more support for the Ontology software development kit to develop more native applications rather than just web applications.

A good feature Palantir Foundry can have is allowing different third-party application support. For example, if a company uses an internal ERP system, in addition to having API points connect to it, Palantir Foundry could read standard ERP systems as built-in functionality rather than requiring data to be sent back and forth.

I have been in the IT field for about five years and have been working in the Palantir Foundry ecosystem for two years.

I have measured the increase in operational throughput during the pilot phase by noting that I increased throughput by approximately 18 percent. This was measured by calculating how much I was able to complete during the pilot and how much time it saved when using AI models to perform the analysis. Extrapolating that to the entire yard, I reached a number around 18 percent.

Regarding Palantir Foundry's AI capabilities, I find that the governance and security are very solid, with all governance and security built in.

Regarding Palantir Foundry's AI capabilities, I find that the accuracy is as good as the data and the prompts, so the accuracy has less to do with Palantir Foundry itself and more to do with how I utilize these AI models.

My advice to others looking into using Palantir Foundry is to pilot it. I would rate this product 9 out of 10.

My main use case for Palantir Foundry is to build complete end-to-end workflows. I have worked on different POCs, a couple of POC projects, and some company projects with various scenarios. I have completed all the certifications, all the trainings, and most of the examples I have built on Palantir Foundry. I dive deep into Palantir Foundry and its architectural design to learn more, so I have covered most of its applications and I understand it well.

I can tell you about my personal project because the company project I cannot share with you. I have built my own finance OS where I manage all my finance transactions, my income, my expenses, and my budgets. I set my budgets and track all these things using Pipeline Builder, Ontology Manager, AIP Logic, Code Repo, Workshop modules, Automations, workflow diagram, and workflow lineage.

Using Palantir Foundry has helped me manage and build my finance OS because it is far easier. I got my records using AI agents and gained deep insights into my expense habits, my category-based expenses, and I also have a dashboard where I can see different graphs that show my expense and income records.

I would like to add that my main use case with the expense management is significant to my overall workflow.

In my experience, the best features Palantir Foundry offers include the Ontology Manager, which is one of the best tools. Process mining, which is called Machinery in Palantir Foundry, makes it very efficient and quick to track the status, and it is very easy to visualize everything. The third one is Quiver, which allows me to analyze the data and plot some graphs and insights in graphical form, making it very easy compared to other software. Additionally, the embedded AI agents are great, with a vast variety of models available, and we can also train our own model. The OSDK part is really very good because we can also create React applications, and with the help of AI, we can create websites entirely through coding. Lastly, there is FVE, which helps a lot in Palantir Foundry configurations and creating things.

Palantir Foundry has positively impacted my organization and personal projects by enabling very fast software development. We totally work on Palantir Foundry in my organization, so I have worked on many POCs and also analyzed that development in Palantir Foundry is very rapid and can be done easily, as it is not far more complex.

I have seen specific outcomes, such as saving a lot of my time using a no-code environment in most of the POCs, and Palantir Foundry helped me to build things faster. Creating visualizations and agents is far easier and quicker in Palantir Foundry. Embedding AI agents also helps, and making continuous changes is easier because it supports branching.

Palantir Foundry can be improved as Ontology Manager, while being a very advanced and useful tool, needs some fixing. In the actions, we do not have much leverage, and if we want something complex at another level, we have to go with Code Repos and other options. I suggest updating the features to create things with more flexibility. Additionally, AIP Logic has limited access, and we cannot do all the things inside AIP Logic, leading us to sometimes have to go for the code repo for a specific task. My suggestion is to also improve the AIP Logic tool.

I would like to add that widgets are something that could be improved.

I have been using Palantir Foundry for approximately one and a half years.

I choose eight out of ten because the software still needs a lot of updates to make it stable, and today, it is not stable.

I have mostly worked on the dev environment, so I do not have knowledge about the paid, enterprise-level applications that are not enabled in the dev environment. According to me, Autopilot is one new feature in Palantir Foundry that enables the user to view things in a very convenient mode. I would also like to say that the Ontology Manager itself is very unique and new; it is like a database, a DBMS, but it is different from DBMS.

My advice to others looking into using Palantir Foundry is that it is the new technology and rapidly growing technology. It is the future of development because it embeds multiple AI models, and the AI enabling feature is very fast and easy. It supports no-code plus AI-enabled coding features, has a vast variety of connectors, and a variety of tools to build a project quickly and rapidly without more employee cost. Therefore, I suggest everyone, my colleagues, and my friends to learn Palantir and use it. Palantir Foundry is built to handle a massive amount of data and can run very large operations, such as airports and complete government systems.

My overall review rating for Palantir Foundry is eight out of ten.

My main use case for Palantir Foundry is document processing, taking large volumes of documents, scanning and parsing them, and using AIP to generate insights and applications based on that work.

I continue to expand the functionality and leverage ontology to provide even more accurate and business-friendly metrics.

Palantir Foundry offers excellent features that make it very user-friendly with quick setup, allowing me to build applications rapidly and then extend those into production applications.

My day-to-day work involves working independently to stand up some applications or collaborating with engineers to conduct quick prototyping.

Palantir Foundry has positively impacted my organization by centralizing the way we build applications and standardizing business metric reporting. This centralization and standardized reporting have created more alignment across my team and the organization, greater productivity between teams, and quicker cycle times into production.

To improve Palantir Foundry, I would appreciate continued updates to the documentation and more tooltips, auto-correct features, or auto-validation on certain types of information.

I have been using Palantir Foundry for approximately two years.

Palantir Foundry is stable.

Palantir Foundry's scalability is very high, given that once the ontology and core applications are set up, it is easy to deploy those and manage the costs effectively.

Palantir Foundry's customer support is great.

My experience with pricing, setup cost, and licensing has been relatively easy, and the pricing made sense given the nature and scope of our work.

I have seen return on investment with time saved on overall development time from initial build to deployment, and overall engineering productivity has increased.

My advice to others looking into using Palantir Foundry is to ensure that their teams are aware of how to use the ontology most effectively and have a general understanding of best practices around AI application setup. I would rate my overall experience with Palantir Foundry as an 8 out of 10.