Palantir Foundry Reviews

I use Palantir Foundry for my primary use case, which involves building and maintaining end-to-end pipelines and operational data products at UHG for our healthcare analytics team. I work on data ingestion and integration from multiple healthcare source systems into Foundry, and I design ELT/ETL pipelines using Code Repositories. My day-to-day work involves data transformation and optimization using Python, PySpark, and SQL. I spend most of my time developing scalable data workflows, automating our data processing, and collaborating with cross-functional stakeholders to deliver reliable healthcare data solutions within the Foundry ecosystem.

A specific project example I built with Palantir Foundry involved building a healthcare claims and member analytics pipeline at UHG. The goal of the project was to consolidate claims, eligibility, provider, and member data coming from multiple upstream systems into a unified operational data model that the business and analytics team used for reporting and care management insights. My work involved designing ingestion pipelines for large-scale healthcare datasets from APIs, SFTP sources, and relational databases. I developed PySpark-based transformation workflows in Foundry Code Repositories and created reusable datasets and modular pipeline components for downstream teams. I also implemented data quality validations and optimized pipeline performance. The overall solution helped business stakeholders get more reliable and near real-time visibility into healthcare operations and reporting metrics.

The main use case for that project was to help business stakeholders and analytics teams use this work for reporting and care management insights. One specific improvement I implemented was optimizing a transformation workflow that was processing millions of claims records daily. By redesigning partitioning logic and reducing unnecessary joins, I significantly improved pipeline execution time and reduced resource consumption.

These features had a very positive impact on our organization, especially in terms of operational efficiency, reliability, and faster decision-making. One major outcome was improved trust in the data because Foundry provides strong lineage, governance, and auditability. Business users became more confident in the reports and operational dashboards they were using. In healthcare, even small data inconsistencies can create major downstream issues, so having this transparent data flow and traceability significantly improved data reliability. Another impact was faster issue resolution. Earlier, troubleshooting pipeline failures or validating upstream dependencies required coordination across multiple teams and systems. With Foundry's lineage and monitoring capabilities, engineers can identify the actual root causes much faster, which reduced downtime and improved overall operational stability.

One measurable impact we saw was around pipeline optimization and troubleshooting efficiency. In one healthcare claims processing workflow at UHG, we were processing millions of records daily through Palantir Foundry pipelines. Initially, some transformation jobs were taking significantly longer because of inefficient joins and partitioning strategies. After optimizing the workflow in PySpark and redesigning parts of the pipeline, I reduced the execution time by roughly thirty to forty percent. This improved data availability for downstream reporting teams and reduced compute overhead. We also saw improvements in data quality management. The biggest measurable gains came from faster pipeline execution, improved data reliability and compliance visibility, and faster delivery of analytics and reporting solutions to business users.

Palantir Foundry is not just a data platform; it actually connects data engineering, analytics, operations, and decision-making all into one ecosystem. The best features are the Ontology layer, end-to-end data integration, Pipeline Builder, and Code Repositories. One feature I really value is the ability to track upstream and downstream dependencies because of its strong data lineage and governance. Foundry is excellent at turning analytics into operational actions. The operational workflows allow organizations to create applications and workflows directly connected to business operations, instead of only generating dashboards. It also supports large-scale distributed processing for scalability and collaboration. More recently, Foundry's integration with AIP and AI-driven workflows has become a major advantage.

Palantir Foundry's Ontology is probably the most powerful feature. Data lineage has also been extremely valuable in my day-to-day work, especially in a complex healthcare environment at UHG where there are many interconnected datasets and strict compliance requirements. With the Ontology layer, instead of working only with raw technical tables, I can model business entities like members, claims, providers, and encounters in a much more intuitive way. This makes collaboration easier between engineering teams and business stakeholders because everyone is speaking the same business language. It also reduces confusion when multiple teams are consuming the same dataset. From a data engineering perspective, the lineage capability saves a huge amount of troubleshooting time. If a downstream dashboard or pipeline fails, I can quickly trace which upstream dataset could have caused the issue, what transformation introduced the problem, and which downstream systems are impacted. In traditional environments, this kind of root cause analysis could take hours or even days across multiple tools. In Foundry, this dependency graph and lineage visualization makes it much faster and also more transparent.

One challenge regarding how Palantir Foundry can be improved is the learning curve. Foundry has a very broad ecosystem with Ontology, Pipeline Builder, Code Repositories, and AI integrations. For new engineers or business users onboarding, it can take time, especially if they are coming from more traditional data platforms. Better documentation, simplified onboarding paths, and more beginner-friendly examples would help accelerate adoption. Another area is debugging complexity. While lineage and monitoring are strong features, troubleshooting deeply interconnected pipelines can still become difficult in a large enterprise environment. Sometimes error logs and pipeline failure messages could be more descriptive or developer-friendly, especially for distributed PySpark jobs. Another pain point is customization limitations in certain UI-driven components. While low-code tools are great for rapid development, highly customized workflows sometimes still require engineering workarounds or deeper technical implementation.

The platform is extremely capable, but improvements around usability, debugging experience, DevOps flexibility, and ecosystem openness would make it even more effective for enterprise engineering teams.

I have been working as a Palantir Data Engineer for around three-plus years.

In my experience, Palantir Foundry has been a stable and reliable enterprise platform.

Palantir Foundry's scalability is very strong. We work with large volumes of healthcare data, and it has been able to handle all the large-scale ingestion, transformation, and distributed processing workflows effectively.

I have not faced any problems with customer support for Palantir Foundry. Customer support and engagement for Palantir has generally been strong, especially for enterprise customers with complex operational environments. This is something that stands out for Palantir.

Before working with Palantir, my teams typically handled data management using a combination of traditional big data and cloud-based tools rather than a single unified platform. For example, our organization often used separate solutions for data ingestion, ETL processing, governance, and access management. The common platforms in the ecosystem included tools such as DataBricks, Snowflake, Apache Spark-based environments, Airflow, and various cloud-native AWS services.

From an operational and engineering perspective, I have definitely seen a positive return on investment. One clear example was the pipeline optimization I mentioned, where we reduced execution time by thirty to forty percent. Another major return on investment area was faster troubleshooting with incident resolution.

The company I work for chose Palantir Foundry. From what I observed, one of the major differentiators is end-to-end governance and lineage. It has the ontology-driven business modeling that I mentioned. It also has good, strong compliance and auditability.

My advice would be to approach Palantir Foundry as an enterprise operational platform, not just a traditional data tool. The platform delivers the most value when organizations fully leverage its governance, operational workflows, lineage, and collaboration capabilities rather than using it only for basic ETL processing. The platform is extremely capable, but there are some improvements around DevOps flexibility, usability, and debugging experience that would make it even better. I rate this platform a nine out of ten.

My main use case for Palantir Foundry was that it served as our go-to platform for business analytics and AI workloads. We mostly used it for analytics and as an operational platform, specifically using Palantir Foundry's Works feature.

We used Palantir Foundry for operational needs and analytics, and there was a team using Palantir Foundry's built reports and dashboards to input data and then execute an approval process using Palantir Foundry's services. The reports were created on the platform following this workflow.

Our team used Palantir Foundry extensively in conjunction with Azure-based services such as Databricks, Azure Data Lake storage, and Azure Data Factory. There was excellent integration between these two services, which really helped automate many of the manual tasks that the downstream teams and business teams were handling.

One of the best features Palantir Foundry offers is its ontology-based approach. Palantir Foundry's ontology is a feature that gives a semantic layer on top of organizational data, and based on that, we can create operational dashboards and conduct analytics on top of that. It provides a very good understanding of the underlying data, and data dependencies can be managed through Palantir Foundry. Additionally, there are features such as Palantir Foundry's code repositories, which help in developing Python-based or JavaScript and TypeScript-based code for complex workloads. There is also a data lineage feature that helps track where data is being processed, where it is coming from, how it is being processed, and what changes occur at which stage.

I find data lineage and ontology most useful. All three features are valuable, but data lineage is the most useful. It helps track the data lifecycle from its origin and how we operate on the data and how it changed, so we can pinpoint the stage where it got altered and then remediate any discrepancies.

Palantir Foundry has impacted my organization positively because it helped significantly. It reduced the team's manual workload that they were doing before by collecting and gathering data and then performing cleaning and curation using an Excel-based approach. This was replaced by Palantir Foundry's code repositories-based and Parquet-based approach where we could easily connect to different sources and schedule pipelines and data set refreshes. This really helped the team.

Regarding time reduction, Palantir Foundry made a significant difference. Previously, the team used to take more than a week to gather data for a particular entity and make it ready for downstream use cases. That was reduced from seven days to close to 30 to 45 minutes because everything was automated using pipelines and schedules.

Palantir Foundry can be improved if cross-cloud integrations could be enhanced, as it would be very helpful and efficient. Currently, it has some limitations. From my experience in my last organization, there were some limitations where we could not connect directly to different cloud storage services, and we could not use those storage services for our backing data sets for the ontology. This area can be improved.

There is a learning curve to Palantir Foundry, and better documentation would really help because while documentation exists, not everything is covered. Better training programs would really help new learners adapt to and grasp Palantir Foundry concepts.

I have been working in my current field for the last five years, so I have more than five years of experience as a data engineer.

Before Palantir Foundry, we were using Azure Data Factory and Databricks. The reason for the switch was that Azure as a whole provided a data analytics tool called Power BI. However, that was not the right tool for the use case that the team was having. Because of this, we made a switch and developed an ontology-based dashboard using a workshop.

I cannot comment on the return on investment, but as a developer, I think Palantir Foundry really helped because I did not have to juggle around multiple services and everything was in place under one umbrella. It provided everything out of the box. For every use case need, there was a tool for that in Palantir Foundry. This meant lesser effort on trying to find what to use and much of the time was spent on development and brainstorming.

I am not much aware about how Palantir Foundry was purchased because that was not under our team's scope. That was something in another team's scope. I cannot answer on that.

My experience with pricing, setup cost, and licensing is limited. My team worked solely on Palantir Foundry for data engineering. We were mostly dealing with data pipelines, code repositories, workshops, and other components of Palantir Foundry such as ontology, and not setting up and deciding on the cost. However, we did perform some automations on Palantir Foundry which reduced the cost significantly for the team.

Regarding Palantir Foundry's AI capabilities, my team used Palantir Foundry for most of our governance and security purposes because it provides a robust and secure environment where data can be governed according to the organization's standards and needs.

For AI capabilities, I used AIP Assist which also worked well.

The accuracy and reliability of Palantir Foundry's AI capabilities are quite accurate, although it does hallucinate sometimes and I have to correct it in those scenarios. You cannot blindly trust the response given by the AI. You always have to have a correct understanding of what you have asked and whether you understand those things. Otherwise, you will simply use the AI response, and it might not be the right thing to do.

I would recommend doing proper research and calculating the cost that you might incur or you would be willing to pay for the Palantir Foundry services that you use. If you are already using one cloud platform such as Azure or AWS and you want to switch to Palantir Foundry for just one service, I would not recommend it. However, if you want to migrate your entire workload to Palantir Foundry, it will really be a cost saving and will be efficient. I would rate this review an 8 out of 10.

My main use case for Palantir Foundry was with respect to the customer onboarding process, which had been outsourced to multiple teams, and there were multiple data sets that had been outsourced to different teams. Some of the teams were third parties, which were creating a backlog and elongating the process. My job was to streamline all the data into one universe, utilizing Palantir Foundry to centralize the data and information in one place to find insights where we were having backlogs for smooth customer onboarding. My objective was to utilize Palantir Foundry's data centralization capabilities to identify backlogs and situations where we were lacking.

For example, in this process, data was coming from various teams responsible for different objectives. For onboarding a new customer, there is a process known as KYC, where we need to identify the customer's details, which are usually transferred to third parties, and some in-house KYC teams must review them. Generally, when third-party cases come to the banks, they are unable to find out why a particular customer is not being onboarded. I tried to merge the data into a central place to find out the reasons, such as why the data is not being updated from third-party sources. The information sometimes lacked advance details from third parties and other sources. Additionally, I was able to map the entities, including cases where customers were not onboarded due to specific reasons related to their past profiles with other banks. These scores were not highlighted by the sales representatives, who were told to meet targets. I was able to track these and identify the pain points of why a customer had not been onboarded or entertained by the banks.

One of the best features Palantir Foundry offers is the Ontology part. It allows mapping different data from different domains and teams into one place, creating relationships between them. For example, I was handling sales data, but there was also customer profile data, financial profile data, and previous transactions data, which I mapped in one place.

Palantir Foundry helped by creating a reporting system that was fast. Previously, we waited for at least a week, but now everything was available in one place with all insights.

Palantir Foundry's governance and security are good, which is a main aspect that people prefer. Providing online data of in-house security to clients would be beneficial amidst increasing cyberattacks. Palantir Foundry's use has been impactful as the company is a vendor for a banking client who utilized it effectively.

To improve Palantir Foundry, I would recommend integrating AI capabilities. Although AI is currently available, having simple general language query features would be useful for end-users. Palantir Foundry's unique selling point is creating a universe, which should remain, but adding a query option would be beneficial.

Palantir Foundry's reporting capabilities can also be enhanced to function more in the manner of a BI reporting tool, which would be a valuable addition.

One downside is the absence of many users familiar with the platform. The cost of using Palantir Foundry is another factor that clients consider, which I accounted for when rating the solution as eight out of ten. Palantir Foundry should address pricing to make it more competitive.

Training resources for Palantir Foundry should be increased to facilitate product growth and build a community that can expand its use.

I have used Palantir Foundry for one and a half years in one of the projects for my banking client, where I worked upon the creation of pipelines in Palantir Foundry, replacing their absolute systems.

Palantir Foundry's scalability is such that it can handle growing data and user needs easily. The implementation was not very complicated because our in-house domain data was not that large. I did not find any issues with stability.

Palantir Foundry's scalability is such that it can handle growing data and user needs easily. The implementation was not very complicated because our in-house domain data was not that large. I did not find any issues with scalability.

My experience with customer support for Palantir Foundry is that I have not interacted with them much. I did have a single interaction when I faced an issue with my login credentials. The chat system available was helpful, and the problem was solved efficiently. It was a satisfactory experience.

Before using Palantir Foundry, we had our in-house centralized reports, but there was no centralized system that connected different domains and data in the manner that Palantir Foundry does. This connectivity is a key benefit that Palantir Foundry brought to us.

Before choosing Palantir Foundry, we considered Microsoft Fabric as we were using Power BI. However, I found Palantir Foundry more useful and stable, leading us to choose it over the former.

Both time and money were saved since a project that would usually take six months was completed in two to three months. This allowed us to allocate time to other projects as well.

My experience with pricing, setup cost, and licensing for Palantir Foundry was challenging for us. Getting approval for pricing and setup cost was difficult. We obtained initial phase approval, but later budget requests for other projects were not accepted by the board. Palantir Foundry should reconsider its pricing strategy to become more competitive.

Before choosing Palantir Foundry, I truly thought of Microsoft Fabric because we were already using Power BI. In comparison to Palantir Foundry, I found it more useful, and it was stable, so we went with Palantir Foundry.

My advice to others looking into using Palantir Foundry is that they need to be clear on what aspect they want to utilize Palantir Foundry for, whether for app development or data connection. Without a clear objective, using Palantir Foundry might not be beneficial. Those with the need to integrate different partners and domains into a common platform will find it valuable.

Palantir Foundry should provide more training resources for people who are in the market so that the product can grow. Additionally, consideration should be given to building a community to expand Palantir Foundry's use. I rated this solution an eight out of ten.

I have been using Palantir Foundry for close to four years, just two months shy of four years.

My main use case for Palantir Foundry is primarily for data engineering purposes to handle big data. I started working in the pharma domain, but for the past year, I have been working in the auto components domain. Along with data engineering tasks, I am now handling some front-end applications as well. Earlier I was building use cases, but now I am building workflows as well using the Workshop application.

A specific example of a workflow I have built recently with Palantir Foundry is an audit platform. As I work for an auto component Tier 1 supplier company, they have numerous bills from many different suppliers for many different plants which need to be audited based on the agreed upon rates set in the contracts. The Workshop application takes data from users, which can be auditors or plant representatives. In five steps, we obtain approvals from different teams. We upload data using the front end of the Workshop application, accepting bills in the form of Excel and PDF, extracting data from them, and running rules to check if specified categories of bills are duplicated based on rules segregated by different countries. There are set rules for duplicate checks, and we also conduct audits by comparing with rates. If there is a permissible difference and values fall within the acceptable range, that is acceptable, but if values fall outside that range, there are expenses which are not expected in that category. There are rules to check for audits. The workflow then goes for approval at the plant stage where plant representatives verify the information, followed by final approval. We then generate a PDF showing that the bills have been reviewed by particular reviewers at particular times, and indicating whether the bill is acceptable to pay or must be rejected with rejection reasons. Notifications are sent to respective people for respective stages. The main task behind this is to replace the specific application or subscription we were using for that function with Palantir Foundry so we can integrate this system with other projects and obtain insights based on how many bills we identify as faulty or how many bills we process in a certain amount of time, allowing us to generate reports when we collect the data ourselves and maintain it on our side.

Regarding my use cases with Palantir Foundry, while not unique, we need to send emails to not only Palantir Foundry users but also users outside the organization. We have set up service user agents with PySpark code that gathers all the data and sends external and internal emails.

The best features Palantir Foundry offers are the ease of use and the availability of all the different functionalities in a single space, making it a very convenient application to use for varying different purposes such as the workflow I mentioned. Along with that, you can process large amounts of data to gain insights. Instead of going to many different applications, most of the functionality that you need is within Palantir Foundry. The convenience is a very significant factor, and the integration of AI features has made it even easier to quickly gather insights for small analyses and building dashboards as well. Even though it has a steep learning curve, once you learn it, it is a very convenient tool to have.

The AI features have helped me in my work in specific examples where they made a difference, such as identifying data from PDFs and not just performing OCR but extracting meaningful information. Another use case is matching the names of suppliers that may not match exactly but are meaningfully the same, finding such matches.

Palantir Foundry has impacted my organization positively by providing straightforward monetary benefits. We can build applications on our own that might not require separate subscriptions, and the data that we generate can help the business optimize their processes or support market strategy improvements and forecasting.

Regarding the features of Palantir Foundry, there are many areas for improvement. Even though Palantir Foundry is very convenient, there are still many things missing.

The basic functionality that Palantir Foundry provides is quite simplified and should ideally not change, but some other tasks that are very cumbersome to do have many missing features. One small issue is that checks take a lot of time in the code repository when the code becomes large, so there could be a way to decrease it temporarily.

I chose a rating of nine out of ten because there are some features that are missing. For example, exporting data from Palantir Foundry is very difficult and has many limitations. Additionally, while sending emails is straightforward, gathering the data and sending it to different recipients is a bit tricky and could be simplified. Basically, it is very convenient, but because there is still room for improvement, that is why I rate it at this level.

In my experience, Palantir Foundry is occasionally facing some issues, but most of the time it is quite stable.

Regarding Palantir Foundry's scalability, I think it depends a great deal on the developer as compared to the platform itself because if the developer has not set things the right way, then I do not think it can scale. However, there are some applications such as Workshop where there is no room for scalability without using developer-specific resources. For example, if the built-in widgets are working slowly, then there is no way to configure or scale it. It depends on the situation; it can and it cannot as well. Depending on what your use case is, you can have or may not have the scalability aspect.

For customer support with Palantir Foundry, it is rare that I need their support for the projects I am doing, but if there are some features or unexpected things happening, their support is available. They help until the issue is resolved, so they are doing a good job. This is positive considering the whole experience.

We have measured the amount in USD of the bills we are processing and the bills that we are finding that have issues. We can potentially identify if a supplier is a repeat offender and take judgment based on how much business is to be awarded to that particular supplier.

My advice for others looking into using Palantir Foundry is that it is a bit difficult to understand. For a new person coming into Palantir Foundry, whether they could be a fresher or aligned with the data engineering aspect, learning Palantir Foundry from scratch is a bit difficult as it has a steep learning curve. However, once they are familiarized with it, I think they will really appreciate it. I would rate this product nine out of ten.

I implemented end-to-end data pipelines on Palantir Foundry to extract data from different sources and create datasets as objects to build a workshop.

We have worked for many organizations, including Airbus and British Petroleum, where we used Palantir Foundry as the main platform to provide solutions to our clients.

Palantir Foundry helped in reducing the time for analyzing simple use cases. For example, for our media and entertainment clients, they wanted to know which particular show was getting popular or was at the top that week. The client was taking too much time to determine which show was getting to the top and which age group was watching those shows for longer periods. The data was scattered across different sources, and they were taking one week to analyze these things. When we used Palantir Foundry as a source, we used different data connections to collect all that data and bring it into Palantir Foundry. Then we performed transformations and joins on that data to make this possible. This reduced the time from one week to four hours.

From the very beginning, I have used Palantir Foundry as a data engineering technology. I have been using the technology for the last three years.

The source data was distributed across different sources. I used a Data Connection to extract data from different sources. We took all that data into Palantir Foundry and created a dataset. On that data, I applied different types of transformations using different Palantir Foundry technologies including Data Connections, Code Repository, and data pipeline. After enriching that data, we created the dataset in the object layer. On top of that object layer, we created some use cases or business cases to understand specific use cases.

I have used Palantir Foundry for the last three years. I have gone through all the platform applications and find it very useful. It creates an object dataset on top of our dataset, which is a real type of entity that makes Palantir Foundry different from other technology. In terms of security, it is a very good solution. I have also reviewed the new changes that have been applied to Palantir Foundry, such as AIP for AI use cases. These things make it different from other platforms.

I find Ontology to be the best feature of Palantir Foundry because in Ontology, our data lives as a real-life entity, which makes it very powerful.

In our data engineering projects, we have to deal with datasets. We have different types of data, so we have to enrich that data. Palantir Foundry has different types of transformations available, including Code Repository, data pipelines, and Ontology. If we create a dataset and want that dataset to provide information, then there is something called Ontology, where we specify the data as a real-life entity.

I want to mention Quiver and Contour for data visualization or checking our data to make better decisions on top of our data. These applications, including Code Repository, Quiver, and workshops, make Palantir Foundry powerful for a data engineer.

In terms of security, Palantir Foundry is the best.

For a beginner, it will be difficult to know about the use cases. But as you get used to the platform, it becomes good. I think users get used to it over time.

If we had more detailed training on the different types of platforms available, then it would be very easy to know the different use cases of Palantir Foundry so that we can provide our best use cases to our clients as well.

There are still a lot of changes required in Palantir Foundry to make it more usable or easy to use. It is a bit difficult for a new user coming to Palantir. It will be difficult for them to get used to it. Once you have applied some use cases for at least three or four months, then you will get used to it. You can provide many trainings or documentation on those use cases so we can get better results.

I have used Palantir Foundry for the last three years.

I did not face any type of this issue. Sometimes it goes down for a very minimal time, but they used to inform us before that. We just prepare accordingly.

Palantir Foundry is a scalable solution. They provide a scalable solution as well, so that is good.

I used to interact with the AIP chatbot, which helps us navigate certain documentation or any minimal use cases if I want to understand something. I did not contact any support team.

Before that, I used Azure as a cloud provider for implementing a data solution. I would choose Palantir Foundry over Azure in terms of security and Ontology.

I did not purchase Palantir Foundry. The client did all the configuration from their own side. We just used those Palantir Foundry credentials to provide the use cases.

I did not purchase Palantir Foundry from my side. Our company just provided all the credentials for the use cases to the client.

As of now, I can tell you about time. Palantir Foundry has reduced a very good amount of time to implement a data pipeline and process the data within four hours.

I did not evaluate anything. From the very first, I had chosen Palantir Foundry.

I will suggest Palantir Foundry because of the Ontology and for security purposes. For the AI capability as well, I can recommend it, but the team needs proper training for at least one or two months to get used to it. I rate this product overall as nine out of ten.

My name is Emy, and I come from Morocco. I was with Stellantis, and my key responsibility is to collaborate with our clients to centralize data in Palantir Foundry because their data is fragmented, and they want to put it in one platform. I am responsible for end-to-end data, performing the ETL process. I am also responsible for creating dashboards, KPIs, and other tasks like automation and sending notifications and engineering reports, and I can say that I covered 90% of Palantir Foundry while working with many services.

I was responsible for data engineering, meaning I treat the data that comes from other platforms like Databricks or Snowflake, making a pipeline ETL to get insight data and generate the object type.

We built a tool similar to Jira that is responsible for following up on requests and all issues our users find in Palantir Foundry. Our clients undertake many projects, tools, and dashboards, and we try to standardize them in one tool called release case, which we developed from scratch. For example, if a user finds a problem or wants to add a new feature, they can create a request, and then I receive a notification. I can switch the request to in progress and work on it, and when I finish, I will switch it to done and send a notification to the user that the feature is added.

In our case for the ETL process, we use PySpark in the Code Repository. After cleaning our data and getting good data, we switch it to the object type using ontology. We also use various services like Workshop to create dashboards, Slate for a custom homepage, and Automate to send notifications, take action, or schedule actions. Additionally, we use multiple data pipelines to check the links and interactions with the data set and show all the transformations and governance permissions. We also use Notepath to generate our PDFs or weekly reports. Other services such as Object Explorer help us to show our data and investigate issues to understand the data better, which is really useful. Moreover, we use TypeScript to generate the user interface (UI), which is great.

I must mention that we have found difficulties in generating reports because Notepath is limited. For example, you develop a dashboard using workshops, and when everything works well, the key reference or the client needs to generate a report based on that dashboard, but in Palantir Foundry, there is no solution currently available. I searched and found no solution. I sincerely hope to see that feature in the future, which would allow switching a workshop to a report.

The strength of Palantir Foundry is that there are no limitations. It is well organized, and the data is very secure. One strong point is that you can add restricted views on each data set by using marking and security levels. This helps us to organize critical data.

We save time in creating dashboards using Workshop and also in the KPIs. There is also another service, a no-code pipeline called Pipeline Builder, which I personally have not used, but it appears really useful for saving time and achieving good results.

Palantir Foundry offers AI, which is a really useful tool to create your agent or model, but I have not had a chance to use it due to a lack of permission. I also want them to add a forum for learners to practice more in Palantir Foundry. For example, I often find limitations and boundaries that slow down my learning and hinder my discovery of new features they add.

I believe that the AI or agent needs improvement because sometimes we face difficulties when looking for solutions, and when we ask the agent, AIP, it does not understand our queries and occasionally provides wrong solutions.

We really need to add a service dedicated to documentation because, in our case, we have developed many dashboards, but there is no documentation. I genuinely hope in the future, there will be a service purely for documentation that can be linked with a dashboard. When you go to the dashboards, it should provide you with all the coding history, all the added functions, and also the pipeline, offering deep insights into the project or dashboard.

I have been working as a data engineer for about three years since 2021.

Palantir Foundry's scalability is really useful. You can scale in and out, and you can control your metrics and resources such as the amount of compute and storage you require.

My rating for customer service is 3.

I would advise that Palantir Foundry gives us the permission to get a trial and practice more. It is really useful and opens the door to learning more about other features. For example, in my country, Morocco, I do not have permission to get the trial of Palantir Foundry, which significantly slows down my learning and impedes development and creativity.

Overall, the platform of Palantir Foundry is really strong compared to other platforms, but it can be quite complicated. I sincerely hope to see more attractive learning resources or documentation that can assist users. My overall rating for this review is 7.

There are several use cases that we are working on with Palantir Foundry. The first thing is for data model creation for all our data engineering pipelines. That is one use case. Palantir Foundry also has an ontology, more of a semantic layer, so that we can directly hand over the data model to the end users. That is another use case that we have, creating the semantic layer ontology. Recently, we have started working on some AI use cases as well. Palantir Foundry has very good wrappers such as AIP Agent Studio and AIP Logic, where you can choose any model and build your own chatbot or any AI function or generative AI function. These are a few use cases we are working on.

I work with different types of data in Palantir Foundry, including structured and unstructured data. We process PDFs and Word documents, but I have not worked on any use case with video and audio, although there are a few teams in our company that actually process video and audio as well. When it comes to textual information, I have worked on several use cases, and Palantir Foundry has made it very simple. There are some built-in functions, and you can also use Python libraries if you want. Additionally, there are no-code tools to parse unstructured information.

Based on my huge experience with Palantir Foundry, I find that starting from the data connection to the end user application, there is a tool for everyone. For example, I do a little bit of coding, and even for me, if I wanted to do some enhancements, I can do it and build specialized applications on it. If there is a person who is from a non-coding background, such as a business analyst, they will have some application to do that as well. For example, I have to build some pipelines on Spark, which needs to use several rows of huge data. There are also no-code tools available which are as good as Spark, not in terms of data management, but at least in terms of learning and ease of use. A business analyst can use that and prepare the same type of pipeline. For everyone, there is a tool or application in the platform.

We use collaborative functionality in Palantir Foundry. Initially, when I started, the collaboration function was not as matured as working on a general open-source application or something else outside the platform. Now it has matured a lot. Starting from the initial dataset, you can create branches up to the application level as well. This is a very good enhancement that Palantir Foundry has made in the last year or so.

The main benefits that Palantir Foundry provides for me as an end user are that everything is in one place. You can use multiple tools, and initially, there may be a little learning curve, but after you get started with it, you will find many advantages. I feel the advantages are on an organizational level. The main advantage is you can decentralize the analytics, and you will have everything in one place, so that you do not need to rely on multiple departments working on different tools. If your organization adapts to Palantir Foundry, then it is a totally different thing, because I see that advantage. I work in an internal audit team as a data engineer and data analyst, and sometimes we need data from different departments to do analysis. It became much easier for our organization because all the data is in Palantir Foundry. That is the main advantage that I see.

Regarding points for improvement for Palantir Foundry, I see that they are improving day by day. In the last one to two years, I have seen many improvements compared to the two years that I have worked on Palantir Foundry. There are many things that come up, but a few things are not intuitive enough. Now that we are in this AI phase, Palantir Foundry has created some wrappers around the models, allowing us to create using a no-code application, chatbots, and LLM functions. The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has. There are ways to do that, but it is not that intuitive, which is what I feel.

I have been working with Palantir Foundry for four years now.

For stability, I would rate it nine out of ten.

For scalability, I would rate it ten out of ten because you have a lot of flexibility. You do not need to worry about spinning up machines as you would with Azure. Here, you can select what memory or configuration you want. It is very easy, a ten out of ten.

I am not sure in general about the technical support, but at least for our company, it is very fluid. Whenever Palantir Foundry introduces a new product, the Palantir people come and train us on new applications, so I would rate that at least a nine or ten.

Positive

The setup process for Palantir Foundry is not complex at all. It is very simple. For me, the organization has already adopted Palantir Foundry, so I just need to deal with security policies that are already set up. I can directly start with sourcing the data and get started with the transformation. It is very easy for me, but at the organization level, I am not sure how difficult it is. Since we are in an insurance company, we have a lot of regulations, and I think there are many security policies in place, so all that setup is managed centrally for everyone in the organization.

Regarding pricing for Palantir Foundry, I am not entirely sure about the exact pricing because it is centrally managed by the organization.

When comparing Palantir Foundry with its main competitors on the market, I would say Azure and AWS are competitors because they offer several functionalities. However, Palantir primarily supports data management. I feel they are at the same level as those other cloud providers. I have also seen Microsoft Fabric, which has good pipeline building capabilities, but I am not sure about its AI capabilities since it is connected to Azure.

The visualization part in Palantir Foundry works for me at least if I want to see how the data is structured and for an initial analysis, but I would say it is not as matured as Power BI or Tableau in the market. Compared to Power BI and Tableau, it is not that mature. You can do a lot of things, but UI-wise, I feel Power BI and Tableau take precedence over what Palantir Foundry has. Palantir Foundry has it, but it is not as mature as those applications.

I am using some data integration features, including the in-built data integration feature in Palantir Foundry. We have several external sources apart from Palantir Foundry, so our application data is stored there. We use the data connection application and bring in all the data there.

I am not entirely sure about my level of satisfaction with the functionality of Palantir Foundry, but I think it is good. My overall review rating for Palantir Foundry is eight out of ten.

I am currently a data analyst using Palantir Foundry as an end-user for developing apps, forums, dashboards, and manipulating the data internally.

My usual use cases for Palantir Foundry mostly involve developing specialized apps, particularly in Palantir Slate. I am deeply using Palantir Slate, developing forums, dashboards, and specific applications. I currently have four short videos on my PC showing what I have accomplished in Palantir, although they are blurred due to company policy. On the technical side, I do the coding in JavaScript and develop using HTML and CSS to create my own widgets. Additionally, my first use case for Palantir Foundry was using the Contour app and the report app, and I can share those videos if you write to me on my LinkedIn profile.

The most valuable features of Palantir Foundry for my use cases are the Slate application and Contour, as the latter allows me to manipulate data ninety-five percent without knowing coding. I can transform data however I want and use the data set along with the pipeline builder to manipulate data flow and updates. My favorite tool remains Slate for developing forums and dashboards exactly how I want them to look.

I have noticed that Palantir Foundry cuts down the time of usage by automating forms and dashboards. However, a disadvantage is the challenge of connecting data to and from Palantir, which requires coordination with data engineers. It takes time, and a data analyst should not be responsible for connecting data; that is the role of the data engineer. I handle data manipulations myself based on customer requirements, ensuring I know how to aggregate, manipulate, and summarize it.

While I do not have specific suggestions for improvements or enhancements, after using Slate, I realized it provides the freedom to create websites. However, I found the Object Explorer and related apps limiting, as they confine me within boxes, restricting proper manipulation. If the Object Explorer and its related apps offered the same flexibility as Slate, it would greatly improve the experience. Although one does not need coding knowledge for Object Explorer, an understanding of primary and secondary keys is still required. Those unfamiliar with coding may require specific training for Object Explorer. Slate, by contrast, requires coding knowledge, including JavaScript, HTML, CSS, and how to connect data within and outside of Palantir Foundry. With this knowledge, I gain full flexibility in developing websites and can create my own widgets using CodeSandbox, which works much faster than using the predefined Palantir Slate widgets. For example, Palantir Slate allows about one thousand two hundred widgets, and performance suffers when limits are exceeded. However, by creating an HTML table with multiple widgets in CodeSandbox, performance remains excellent, illustrated by my application with over one thousand five hundred widgets on one page.

I have been using Palantir Foundry for about seven years.

The reliability and stability of Palantir Foundry is great; it remains stable as long as I do not accidentally create an infinite loop, which can cause the application to freeze. With a stable internet connection, everything works perfectly.

Palantir Foundry has proven to be a great tool in terms of scalability for me, especially compared to Power BI, which felt inadequate. Its scalability depends on the Slate application; essentially, I am only limited by my imagination. I have not explored machine learning features yet, such as three-dimensional visualization in Palantir Foundry, but in two-dimensional space, when developing forms or dashboards, I can copy and paste data from Excel, which then builds visual representations and drawings, demonstrating that the only limit is my creativity.

I communicate with the technical support of Palantir Foundry less frequently these days. My last interaction occurred when I created a forum for BP's data request support, where some users did not receive email notifications. I discovered that the issue stemmed from a Microsoft connection limitation affecting users connected to more than one thousand distribution lists. I have advised those affected to reduce their connections to stay below the threshold, which should allow the system to properly update and ensure they receive notifications.

Regarding the technical support team at Palantir Foundry, I would rate it around seven. It varies depending on the level of support and the responsiveness of the team. Sometimes it may take days to hear back, while other times I receive answers in as little as fifteen to twenty minutes. Developing apps using Slate can be particularly challenging since not many people have experience with it compared to other applications. When I seek help regarding code in Slate, it can take considerable time for the team to find the right answer or documentation, especially since the responses depend on the level of support provided, and specific queries regarding coding usually require reaching out to more experienced developers.

Before Palantir Foundry, I had some experience with HTML and CSS, as well as Macromedia Flash long ago. However, I mainly used Microsoft Excel, which I enjoyed and used to develop a forum in Excel.

I decided to stop using Excel and start using Palantir Foundry because my ex-line manager suggested I try using Palantir to manipulate data and create reports. Gradually, with some support and visits from Palantir representatives in Azerbaijan every few months, I learned more about how to use it. I even took a Python course on Coursera from the University of Michigan and completed an eighteen-month data analytics course supported by BP, where I also learned about machine learning that I may use in Palantir Foundry someday.

I did not participate in the initial setup of Palantir Foundry or the installation process. I consider myself an experienced user, as colleagues come to me in Azerbaijan for questions about applications. While I can help with applications I have developed, if responsibility falls to someone else, I need to take time to review and consult with my line manager to understand it better.

Palantir Foundry was suggested to me by my manager, and as I started using it, the adoption increased, with more apps being integrated. I have heard that BP also uses AWS, but it seems somewhat limited. I directly asked if there is anything in AWS that is similar to the Slate application, and they mentioned there is one, but BP management did not want to pursue it for some reason. I am a developer and coder, so I cannot elaborate on that.

I do not know anything about the pricing of Palantir Foundry; it is managed by the company's finance management. I am a user, but after using Palantir Slate, I noticed a large advertisement within the company concerning Power BI, which I strongly dislike. It seems that anything free, such as Power BI, cannot be sufficiently effective. I would rate this review with a rating of nine overall.

My main use case for Palantir Foundry is creating data engineering pipelines including ingestion of data, transformation of data to the final dashboarding tools used in Palantir Foundry.

A specific example of a project I have handled using Palantir Foundry involves working for a pharma-based company that had data coming in from different vendors. The data was located across different data sources including GCS, BigQuery, Redshift, and Snowflake. We ingested all of them into Palantir Foundry and built three separate layers of transformation: bronze, silver, and gold for cleanup of data, aggregation of data, and post-processing. At the end, we used the final data sets which we thought were ontology-worthy to create ontology objects on top of them and then used them in the dashboard building steps. Those dashboards were used by high-stakeholders to make very critical decisions.

I would highlight some tools within Palantir Foundry that made life easier for people who are not from a data engineering background. For example, Code Workbook and Contour are very low-code tools available in Foundry. In those, you can load any large data and do quick analysis, including complex joins, sorting, and filtering conditions. This made life much easier for the analysts in the team, as they did not have to go through complex SQL joins or worry about Spark resources.

In my experience, the best features Palantir Foundry offers are its usefulness for all levels of people from analysts to product managers to software engineers. People who do not want to go into the details of how resources are managed in the case of Spark resources can quickly load up their data with quick POC tools and get their job done. It also provides flexibility to developers and engineers who are keen on fine-tuning Spark configurations themselves. It can help create very useful dashboards for end stakeholders, and the front-end dashboards are very quick to create. I would also highlight that in the last two to three years, Palantir Foundry has quickly onboarded many AI-based tools, such as their AIP Foundry platform that they onboarded about two years ago, which is a very helpful tool. It gives access to all sorts of LLM models to build applications on top of that.

Using AIP platforms helps us build tools that are beneficial in accelerating drug trials. Specifically, to figure out how a molecule is similar to other molecules, there was a lot of text written between them, including all sorts of criteria. This text comparison on a manual basis takes a lot of time, but by leveraging the tools available in AIP Foundry, we can use LLM models to do that comparison and assign a soft score, alongside other signals that help us determine similarity. Using Ontology is valuable because it gives us the rigidity to save a schema for the final output. If there are any changes in the background table, such as a schema change that is not defined in the Ontology contract, then that sync would fail, and alerts would be raised. This is useful because if there is a failure, we know before the changes reflect on the dashboard.

Palantir Foundry positively impacts our organization as we are able to conduct numerous POCs around AI and LLM tools. Before using it, we did not have to worry much about resource modification or configuration and could focus solely on the logic. We manage to have a very neat pipeline running for us, and the monitoring and scheduling options in Palantir Foundry are quite good, which helps us fast track the work.

I see that Palantir Foundry can be improved in terms of tools that are not very customizable. For instance, the Workshop application has some widgets inside that we wanted to modify, but when we reached out to Palantir, they informed us that it was a design decision and those modifications would not be added. Therefore, the customization features in some cases are not accessible to end users, and it is up to Palantir to decide whether such modifications would be introduced or not.

Palantir Foundry is quickly catching up on onboarding a lot of AI tools, which will be beneficial to the organization in the long run.

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

I find Palantir Foundry to be stable.

Regarding scalability, we have not experienced any issues during the time we have been working on this. The resource pods available on a particular billable client are always accessible. If they are exhausted, jobs might fail or be put on hold until the resources are freed up, but scalability largely depends on the product tier purchased with Palantir Foundry.

Customer support for Palantir Foundry is good. I am not certain if that service comes as part of being a Palantir customer directly or if we are billed separately for it, but they are usually very responsive, offering help within two to three hours. In urgent cases, they are also available on call, which is a positive aspect.

Although I do not have direct comparisons available, we started the project on Palantir Foundry tech stack itself. However, having worked in other environments like AWS and GCP, I find that not worrying about the low-level details of a system is a beneficial aspect of using Palantir Foundry.

My company does not have a business relationship with Palantir other than being a customer.

We definitely see a return on investment with Palantir Foundry, but I cannot share specific metrics. Many of us working with the platform have realized that there is considerable time saved in the iterations of experiments we conduct, although it is not feasible to quantify that objectively.

I did not evaluate other options before choosing Palantir Foundry.

For others considering using Palantir Foundry, I would advise getting very comfortable with the data pipeline, data lineage, or Monocle, using Contour for quick analysis, and utilizing Code Workbook for quick POCs before writing code in the code repository. It is beneficial to make use of all the latest products Palantir rolls out, as they are really useful. From an organizational perspective, it is advisable to note that initially, the cost may be low, but as time passes, cost can be a significant factor in deciding whether to continue using Palantir Foundry or not, so those considerations should be taken into account.

I think Palantir Foundry is a good tool in general. However, there are some problems that I previously highlighted, including the availability of software engineers trained on Palantir Foundry. I am uncertain of the current situation, but it was quite difficult to find training on Palantir Foundry two years ago if you did not have project access. Unlike GCP and Azure, which provide free credits to try their products, Palantir Foundry did not have such offerings two years back. This made it difficult to learn about Palantir Foundry until onboarded to a project. There still seems to be a shortage of Palantir Foundry developers in the market, causing potential pauses or halts in work when someone from the team leaves for other opportunities. Cost is another factor; in the long run, the cost of using Palantir Foundry can escalate quickly, so one should be mindful of that.

It is difficult for me to assess how secure Palantir Foundry is, but per the contract we have, the calls were routed directly to Azure instances of the LLM deployments, ensuring that calls are made over a secure network. Thus, I find it difficult to comment on security.

The accuracy of output from Palantir Foundry is dependent on the LLM models we use; hence, I do not think that Palantir Foundry influences accuracy in any manner.

I would rate this review as a 6.

My main use case for Palantir Foundry is from the data engineering perspective.

A specific example of how I use Palantir Foundry for data engineering involves raw data stored in Redshift AWS, which we are using those tables in the form of a dataset in Foundry. We are ingesting that data into Foundry and using it for cleaning purposes. After cleaning the data, we create Ontology objects and use them for operational applications in the Workshop.

One of the use cases that I found with Palantir Foundry is when I worked on the supplier scorecard, which is dedicated to understanding supplier reviews based on the goods supplied. The company assigns ratings to their products through a supplier scorecard, providing scores to their suppliers. We used multiple datasets and created objects, adding our own logic in the Code Repository to check supplier goods by percentage and count, generating aggregated values in the Workshop app. Based on these parameters, business management can make decisions and take actions to update the supplier's score.

Palantir Foundry offers great features, including data connection specifically for ingesting data from various third-party servers like AWS or Azure, with around 250 plus data connections available. Additionally, it includes Pipeline Builder, one of the best ETL tools for transforming data from raw to gold layer data, following a medallion architecture of bronze, silver, and gold. In more complex use cases, the Code Repository offers a fully code-based solution while Pipeline Builder serves as a no-code, low-code tool; my preference leans towards Pipeline Builder for data refinement.

For data analytics, it features Contour, allowing for data analysis, and ontology objects for creating links between multiple objects with actions for CRUD operations throughout the Workshop. It also has Quiver for exploring objects using AI tools, enabling business users to ask questions in their native language, which Quiver converts into queries for report generation. Another significant feature is AIP Logic, akin to agentic AI, processing existing data with multiple AI models. AIFTA is another cool feature that needs only a prompt to handle various tasks, such as creating ETL pipelines based on raw data stored, selecting to create the pipeline in either Pipeline Builder or Code Repository as needed, and also supporting object creation at the branch level.

In the Pipeline Builder, we can use Databricks as a compute profile, which is one of the coolest features. The OSDK is a new feature that allows creating custom UI pages using React or Angular, fetching data through API, should the business be unsatisfied with existing widgets in the Workshop. There is also MCP Hub, which uses Model Context Protocol to operate Palantir Foundry from a local machine using LLMs, generating and deploying code efficiently.

Palantir Foundry has positively impacted my organization through multiple use cases, such as warranty data refinement, where previously we struggled with identifying the number of claims related to specific products. Analyzing the data helped us craft a Workshop application that tracks claims by country and product, enabling report generation for management action on defective products.

I believe Palantir Foundry could improve by introducing a tool to restrict object-level creation to specific people, such as developers. A dedicated application could streamline requests for access to data across different organizational verticals, enabling better tracking of costs associated with specific use cases and improving identification of data access requests.

Regarding documentation, I find that when I face issues, the outdated documentation is not helpful; for example, while trying to create a webhook to fetch SharePoint metadata, I found available resources lacking relevance, needing significant updates to assist users properly.

I have been using Palantir Foundry for the last four years.

Palantir Foundry is stable.

Regarding scalability, if you have billions and trillions of records, Palantir Foundry accommodates ETL pipelines with a dedicated compute profile that lets you choose from several sizes, whether small, large, extra-large, or custom to fit your needs.

Customer support is really good; when we encounter issues, raising a ticket with a screenshot leads to responses typically within a week or twice a month, depending on their organization.

Previously, for data engineering, we used Databricks; however, it lacked the capabilities we found in Palantir Foundry, which allow for analysis, reporting, and automation without needing to implement additional functions such as AWS Lambda. I appreciate that Palantir Foundry offers dedicated automation tools significantly simplifying processes.

We have seen a return on investment, primarily saving money on developers. With traditional development requiring many specialized roles, Palantir Foundry allows us to operate efficiently with fewer personnel, often enabling just a couple of front-end developers to manage our processes, thus noticeably reducing time and costs when completed effectively.

My experience with pricing, setup cost, and licensing has not been too overwhelming; I worked closely with a management colleague who explained how they check for cost based on user activity and individual vertical usage.

I primarily evaluated Databricks, which I found lacking compared to Palantir Foundry's robust offerings.

My advice for others considering Palantir Foundry is that it delivers an ecosystem eliminating the need for third-party applications, greatly simplifying tasks without requiring extensive efforts in model training or other processes, making it a strong option for organizations. Security and data governance are also significant advantages. I have covered everything I know and have used regarding Palantir Foundry. I would rate this product a ten out of ten.