Palantir Foundry Reviews

My main use case for Palantir Foundry is to modernize the data infrastructure. One of the modernization projects I have worked on involved getting all the telemetry data collected from IoT devices that had been sitting in the field and then streaming it to Foundry while using the AIP capabilities to perform predictive maintenance and forecast performance degradation of the metrics. This allows the AIP agents to send out remote fixes to address the actual issues.

Palantir Foundry helps with predictive maintenance and forecasting performance degradation by providing a layer of abstractions so that I do not have to worry about piecing together all the different frameworks. Rather, everything is integrated beneath Foundry and the AIP. I can focus on the data part, integration, and data integrity, which means I worry less about modeling and optimization.

In my recent project work, I have been extending all the AIP agents to derivatively send remote fixes. Rather than keeping autonomous operations confined within the platform, the agents can now interact with the real world to fix issues or conduct extended analysis so that the issue can be briefed in the ontology.

Palantir Foundry's best features include AIP, specifically its AIP capabilities. What stands out to me about the AIP capability is how well the data is tightly integrated, allowing me to ingest the data and then hydrate my ontology with context-rich data. Beneath this layer, the ontology creates its own semantic layer so that I do not have to connect all the dots. Rather, the AIP agent itself can look at the complete ontology and has its very own access, so I do not have to be feeding anything specific. Instead, I can give complete connected dots to my AI agents.

Palantir Foundry has positively impacted my organization by enabling us to gain traction from different industries and different companies across various sectors. Since PwC operates as a service-based company, we can pull out massive deals from those companies across various industries, making this a positive service implementation I have noticed in my company.

It has definitely increased the project delivery timeline, so now it does not take weeks or months to deliver a project but rather just days for the development efforts. This allows us to look ahead and spend more time with the business on actually understanding the problem rather than spending most of the time developing the solution itself.

Palantir Foundry could noticeably improve in providing visibility over the different layers beneath Apollo or the platform itself. Whenever an issue arises with a pipeline or an AIP agent that runs away with all the tokens, I do not feel enough visibility beneath the layers to dive deep into tracking the issue and then mitigating it.

The problem with the AI capability is that whenever I spin up an agent that goes and drags documentation, I feel less control over its actions. Since everything is tied together in the ontology, I really have a less structured and integrated way that I can intervene.

Customer support should definitely be a concern, especially for the dev tier account I have been using, while for a corporate account, it is pretty good.

I have been using Palantir Foundry for three years.

Palantir Foundry is generally stable, though sometimes when the data gets finicky, the Palantir pipelines or the ETL abstraction that the pipeline has breaks, making it hard to decode all the metrics and trace back the error.

I have not faced any issues with scalability, especially during long-running compute. However, sometimes it depends on the region where the subscription is deployed, which might lead to some temporary degradation. The issues usually get fixed within an hour or so.

Customer support should definitely be a concern, especially for the dev tier account I have been using, while for a corporate account, it is pretty good.

I did not previously use a different solution and was fully utilizing open-source frameworks and languages.

The setup cost and licensing are all simple, and with the documentation, I can literally navigate through a series of steps and then set up my own organizations.

Palantir Foundry has dramatically helped us in terms of project costing because earlier we had our own React developers team from offshore. Now with the AIP capabilities launched on the platform, we have completely avoided the need for a dedicated team. This has been very helpful in terms of cost management and reducing team size.

The pricing is a bit on the higher side.

Before choosing Palantir Foundry, I evaluated Azure Foundry. Since it was under development and in its early stage at that time, Palantir Foundry was beating it in its own game and was way ahead of Azure.

The accuracy and reliability of Palantir Foundry's AI output is pretty great. All those aspects are good, especially the documentation, which is so good that I can literally debug myself without looking for a long video that requires extended viewing time.

My advice to others looking into using Palantir Foundry is to get hands on with the platform and explore all its applications and the products that are available, as it is going to save a lot of time and money. I would rate this platform a 7 out of 10.

Private Cloud

Amazon Web Services (AWS)

I have used Palantir Foundry in multiple cases, such as creating data pipelines and ingesting data into Palantir Foundry from various data sources, including structured, unstructured, and semi-structured data. After ingesting into Palantir Foundry, I have cleaned the data using PySpark and code repositories. I have written Python and PySpark scripts that clean the data using various transformations, schema changes such as converting boolean and string fields to boolean, data type changes, dropping unwanted data, and filtering the data. I am building end-to-end pipelines in which I have joined and integrated multiple data sets. I have also applied various health checks, scheduled jobs using Cron expressions, and created ontologies, including actions such as create, edit, and delete. On top of ontologies, I have created Workshop applications for the UI perspective and multiple kinds of UI applications. I have also worked on the Slate part. Regarding industries, I have worked in the aerospace industry, healthcare, and the gas and energy sector, with major clients in three industries.

I appreciate multiple aspects of Palantir Foundry. I start with the clean architecture and clean UI. Regarding the coding part, I use code repositories where I can create multiple Python and PySpark scripts and test them. Palantir Foundry has data lineage that shows my data pipeline in a graph, illustrating the data flow from data nodes. If I have a thousand datasets and a column called test, for instance, and I want to check which dataset this column is coming from, I can check it easily in the data lineage by typing the column name. I can debug my bugs very easily using it. Palantir Foundry has features to track daily pipelines, identify which dataset is failing, and track time since the last check, along with health-related aspects that I can monitor using that lineage.

On the Ontology UI part, using Workshop applications, I can create very interactive applications quickly and easily. These are very cool features in Palantir Foundry, and now with AIP, it is also very useful.

Palantir Foundry has a service called Resource Management, where I can track how much the architecture is costing and how much data I have in Ontology and datasets. This tracking is beneficial since the UI is very clean, and I really appreciate it.

Palantir Foundry could improve in several areas. I do not prefer Slate. I think Palantir has stopped developing Slate and is focusing more on Workshop applications, which they are developing rapidly. Slate does not have a branching concept where I can deploy changes. For example, if I have production and if I have QA or development, there is no feature to deploy changes from dev to QA or QA to prod. I need to do manual replication of my work in QA and prod. I think Palantir will drop that application in the future since they are not interested in Slate. Another improvement is needed in integration with multiple services. While Palantir is increasing integrations with other platforms, I believe more platforms should be added. Additionally, sometimes it is very hard to use Palantir APIs, and the documentation has very little information.

Slate is a UI perspective application in which I can write JavaScript functions. Palantir Foundry has a code sandbox where I can write HTML, CSS, and other elements. Slate is primarily used to create UI applications, but it lacks a branching concept for change deployment. Without this feature, I need to replicate my work manually in different environments. I believe that Palantir is not interested in Slate's development, and they will likely drop that application in the future.

I have been using Palantir Foundry for around five plus years.

I have not had much interaction with customer support. I have connected with the Palantir support team one or two times, but not often. Whenever I face an issue, I use Palantir AIP, which is very useful.

The support quality and speed depend on the contract. In one of my projects, I had a weekly call with the Palantir support team, which was very useful for addressing any questions or doubts regarding Palantir Foundry. However, in another project where there was no paid contract, the support was not good, and they were slow and not very helpful.

I have worked on Databricks, and similar to Palantir Foundry, I have written PySpark scripts in Databricks and created some UI applications, although that was a long time ago. I am not familiar with the current UI services in Databricks. I have worked on AWS Lambda and Glue, but not much. I have worked mostly on Palantir Foundry.

Whenever a client wants to initially come on Palantir Foundry, the process will be very easy. I need to consider multiple points in my mind when starting any project on Palantir Foundry. For instance, I worked with a major healthcare client who moved from Databricks to Palantir Foundry, and I helped them with the transition.

Regarding pricing, I heard about it, and until last year, the license was one million dollars. They have now increased this to four million dollars, which is high. From a pricing perspective, I have also worked on optimizing costs. I had existing pipelines that used multiple resources, which increased costs. I focused on optimizing our pipelines and code to use fewer resources, which means lower prices.

I have more than five years of experience overall, and from the start of my career, I have been working on Palantir Foundry. I am also a Palantir certified data engineer.

I use Palantir Foundry to ingest data and create visualizations for decisions.

My favorite thing about Palantir Foundry is the ability to utilize multiple different types of coding languages and the current updated documentation and help features that are in there have improved pretty significantly and make it rather friendly for use.

The scalability of Palantir Foundry is the part that I love the most. That is why I said that is where my primary focus is, trying to get data and then turn it into information and knowledge and understanding, and more importantly, getting decisions out of it. The scalability is phenomenal.

I think the things that I do not like about Palantir Foundry is not a Palantir issue so much as it is from my company side and what they have commissioned for and have not commissioned for.

With Palantir Foundry, it is a part of the user tools that they provide. Their AI Assist that they use is something I have found that sometimes I get better results for when I do need help and aid. I get better results going to outsourced AI assistance sites such as Gemini, because it seems like AI Assist, which my understanding is it is supposed to be searching and utilizing the documentation for Palantir Foundry, but sometimes it gets kind of confused in the capabilities and it will tell you, 'Oh, you can use this and do this.' And then when you try it, the system says, 'We do not support this.' Whereas I can go to Gemini and it will give me a workaround that will actually work.

I have been using Palantir Foundry for about four years now.

I find the stability of Palantir Foundry to be extremely stable. I have had only one time period where there was any downtime that I noticed, and it was for a very short period of time. When I say short period of time, I am talking within hours, not days.

I have contacted the technical support or customer support of Palantir a couple times.

Especially for what they are supporting and doing, I find the quality and the speed of the support to be extremely fast. I usually get within a day turnaround and the support staff are extremely knowledgeable and good at what they are doing.

Prior to using Palantir Foundry, I used various different SAPs and ERPs where I would actually have to export worksheets and then build my own. The closest thing I would be building would be databases that just resided on my computer, and I was not using anything like Palantir Foundry.

The initial deployment of Palantir Foundry was super simple.

It did not take me any time to fully set up Palantir Foundry because it was made available to us and it was basically already set up and rolled out by the time I got permissions to use it. All I had to do is basically create an account. Actually, I did not even do that. Someone else in my organization created the account for me with the initial setup of everything. By the time I logged in and went to use things, most of it was already initialized for me.

Palantir Foundry requires no maintenance on my end as it is taken care of by Palantir. The only thing I ever get, and Palantir already does it, is when there are certain upgrades to transformations that I have made, they will put in the upgrades, but it requires me to actually approve them and merge them in. It is minimal work on my part. Usually I can click it and just approve it right away.

I would rate this product a 9 out of 10.

My main use case for Palantir Foundry is pipelining and analyzing data there.

I replace the existing pipelines with Pipeline Builder in Palantir Foundry. I have various data flows and production of national reports, and I am replacing that using Palantir as part of an NHS Federated Data Platform. In terms of analytics, I use it to check data consistency and test it against what I have in other systems. People also use Quiver and Contour.

That is pretty much everything I have to add about my main use case or the way my team interacts with Palantir Foundry.

My background is in Databricks, and if I compare Palantir Foundry to Databricks, I see benefits of Palantir Foundry in that they make it simpler to configure clusters or at least to manage some infrastructure. If I think of Foundry as being an implementation of Apache Spark and compare that to Databricks, it is easier for an organization to use Foundry. I would also say that pipelining itself is more drag-and-drop style.

It is obviously easier to start with Palantir Foundry. I get more things managed by Palantir themselves. If I have a team with mostly SQL background and I want to move them to a Python, PySpark environment to use clusters, obviously using Palantir Foundry is an easier option than using Databricks.

There are pros and cons, obviously, regarding the features of Palantir Foundry. If I get stuck with the drag-and-drop nature of Pipeline Builder, it is going to be more difficult to migrate that to a different platform. From a Python coding perspective, even if I don't use much of that, I would say Databricks is probably better.

It is difficult to say how Palantir Foundry has impacted my organization positively. Palantir helped me migrate some data into the cloud. Whether they indeed impacted my organization positively is not clear because of Palantir's appalling reputation. So it is not that easy to say. If it was my choice, I wouldn't sign the contract with Palantir in the first place. I would probably stick to standard Databricks.

I wouldn't add more about the needed improvements, either on the technical side or regarding compatibility and integration.

Obviously the company's reputation needs to be improved regarding Palantir Foundry, or ideally, Palantir needs to get away from the appalling views on human rights and improve the reputation. Whether it can be improved, I don't know. This is not a technological problem; it is a problem of company image, so I wouldn't be surprised if the NHS actually triggers a break clause in the contract in February next year. That is not linked to the product itself. From a technical perspective, maybe to make Palantir Foundry more compatible with Databricks could be one option, or maybe more integrated with Azure. It is difficult to say because they might lose some of their competitive advantage in doing so.

I have been using Palantir Foundry for about nine months.

Palantir Foundry is okay in terms of stability. It gets sometimes occasional issues, but compared to Databricks, it is probably the same or may be better. However, as maybe one of early adopters, I get more technical support from Palantir, and I am maybe in a honeymoon phase. It is stable.

I don't know the answer to the question about Palantir Foundry's scalability, really, because I didn't test that. My assumption is that it is correct. I don't know the financial side of it, the cost. I can't judge that. To the best of my knowledge, it is not worse than Databricks.

Customer support for Palantir Foundry is okay. However, I am in this potentially better supported phase by Palantir Foundry. I am not admins on my tenant. What I have in the current environment is that Palantir Foundry themselves are running my tenant and configuring clusters and doing things that I need, simply because I don't have permissions. So when I get to that point of being admins on my own tenant, then I may be able to provide more information about that.

In the current organization, I used legacy on-premises and cloud databases, and I used and still use Microsoft Azure before I switched to Palantir Foundry. I haven't fully switched yet, but the decision to overall switch was based on the momentum to go to this FDP, a Federated Data Platform. I believe it was a financial incentive to do so because from my organizational perspective, I am not paying for cloud space that I use in Palantir. I only pay for computing, but even that is probably covered by a bulk contract.

I didn't purchase Palantir Foundry through the AWS Marketplace because in my case, it is a part of the contract between NHS England and Palantir. I don't know if they procured it via AWS Marketplace or not.

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

I haven't seen a return on investment with Palantir Foundry. I wouldn't even see the financial data. It is very difficult to judge for me. That is why if somebody would ask me whether Palantir Foundry in the NHS is value for money, it is difficult to answer that question.

I have no experience with pricing, setup cost, and licensing for Palantir Foundry because for me, essentially, it is free. NHS England pays for that as part of their procurement process. That is why I can't answer this question. But in terms of getting a contractor to work on that, I would probably say it is more expensive because there are fewer people with that skillset compared to, say, Databricks or Azure.

I didn't choose Palantir Foundry; I wouldn't choose them. If it was my choice, I would probably go for Databricks or even stay with Azure and try to see if I could use Spark there even without Databricks.

First of all, I would advise others looking into using Palantir Foundry to ask themselves if they want to use it given the reputation of the company. You don't need to use Palantir Foundry per se. The second consideration would be whether you want to use Databricks or any other implementation of Apache Spark. It would be interesting to see if you prefer the drag-and-drop nature of Pipeline Builder as opposed to, say, notebook structure of Databricks. So it might be a choice. I would probably say talk to your data engineers and ask for their opinion. Take that into consideration as well. Factors to consider include if you implement Palantir Foundry as what they consider a default option, you are likely to be very entangled into the product. It would be difficult to decouple in the future. That is why they are very sticky. That is probably one of the issues the NHS will get with the product in the future. My overall rating for Palantir Foundry is 6.

Palantir Foundry serves as our data platform for the company, which means we have numerous use cases and business cases that cross all the different business groups, subsidiaries of the company, and also different support functions and business functions of the corporate. We have more than 200 use cases in the corporate because Forvia is a very big company. The main use case is to enable the data value and data product for our corporate and for our business.

The main purpose of the data platform is to have a good return on investment based on IT digital dependencies. From a business point of view, I will give you a good example of purchasing. For the purchasing side, purchasing has two types: direct purchasing and indirect purchasing. Especially for the direct purchasing part, previously, we could not know that all the purchasing data management was quite siloed. With Palantir Foundry, we break the data silo to make all the different data which comes from the purchasing department globally, which have acceleration with the data sourcing assistant and AI sourcing assistant, to help our business accelerate their purchasing business transformation and to achieve excellence in terms of purchasing goods price. This helps us, at the same time, to speed up for the purpose of time saving, and it helps our business to accelerate all the price transformation strategy with our suppliers. That is a good benefit.

Not only for the purchasing part, it is also for the processing side in the operation and for the industrial operation, because Forvia is a manufacturing company. We have many data use cases in the plant. Globally, we have 500 plants and factories globally, which have many critical operations on the factory plant side. For example, the predictive maintenance with the data coming from the shop floor from the plant side helps us to have a good level of understanding of the different machine statuses of the plant.

This is the data-driven enterprise strategy. Since five years ago, we started our data program and launched the data-driven enterprise. This strategy has changed our HR organization, meaning we need to apply change management to accelerate because we are facing the change of data and AI. With Palantir Foundry, it helps us to accelerate this change management in our corporate, which is quite positive.

Time saving, budget saving, and cost reduction are benefits we have experienced, along with accelerating decision-making for the target, because Palantir Foundry with the data is quite useful. It helps management make the right decisions in the market, especially in the current situation where all the competition in the automotive market is quite complex. With Palantir Foundry, it helps us have better benefits and better return on investment, and also accelerates the right decision in the market.

There are three good features which we have applied until today in Palantir Foundry. The first one is, of course, all the data product features from Palantir Foundry, with all the different data pipelines, which helps us to have end-to-end data product experience with Palantir Foundry. The second one, relative to the previous benefits about data product, is that we have a good level of data ontology, which is a data catalog that helps the business people to understand better their data in a functional way. The third part is the AIP usage, because Palantir Foundry has the AIP feature, AI platform feature. With AIP features, we could accelerate our AI transformation and also develop our own AI agent with Palantir Foundry.

Palantir Foundry needs two points for improvement regarding the data product. First, Palantir Foundry needs to improve their clear resume about their product features roadmap. Second, Palantir Foundry needs to have a closer connection with the enterprise corporate application, which means the business application, because big companies have a very huge ecosystem of business applications. In my personal perspective, I think Palantir Foundry still has some space to improve in integrating with the IT landscape of the corporate.

I want to say that Palantir Foundry is quite expensive. It is not so easy for budget review and budget transformation of the company, which is quite expensive.

In terms of my experience with Palantir Foundry, I have been using the Foundry product from Palantir for more than five years already.

It is stable.

The scalability is good, but we need to pay for the compute and the resource.

The customer support is fine. We have the Forward Deployment Engineer, FDE, with us on site, but once again, it is quite expensive for the daily price of the FDE engineer. I think we need to rely on classical support by using a ticketing system of Palantir.

We previously had Cloudera in the company as the data lake solution.

At this stage, it is fine.

We don't have a migration plan.

I cannot give you the details of the money saved because it is quite confidential. What I can tell you is that return on investment is quite good, but Palantir Foundry is quite expensive and it is difficult to have a good budget for Palantir Foundry. That is the reality.

At this stage, it is fine.

At this stage, it is fine.

I have two suggestions for other companies looking to use Palantir Foundry. First, you need to understand how Palantir Foundry integrates with your IT system landscape before choosing to use Palantir Foundry. Second, you need to define and design good governance for Palantir Foundry usage for your data platform. I have rated this review with a score of 8.

Public Cloud

Microsoft Azure

My main use case for Palantir Foundry involves building data pipelines, creating workshop apps, and constructing Gaia maps.

Another example of my main use case with Palantir Foundry is obtaining different data sources and combining them so that they can be visualized either in a workshop app or a Gaia map.

The unified picture is important for improved collaboration and decision-making in my organization, as that is the ultimate goal of a tier one organization in the Department of Defense and it is crucial to communicate to lower echelons effectively.

The best features Palantir Foundry offers include the ability to bring in multiple data sources into one spot and also host models that I can either bring or models Palantir already has access to, then combine them into a global ontology.

Combining data sources and hosting models in Palantir Foundry has helped my work because it is convenient to work in one environment rather than moving from one application to another, as Palantir Foundry allows for that one-stop shop where I can accomplish much of the work.

Palantir Foundry can be improved with better documentation, more robust training, and enhancements for working through transformations that are not accepted by the ontology. Additionally, the connection between Foundry and Gotham is not clear, and managing objects in Gotham lacks good documentation and training, leading to frustration. Using a regular database with a third-party application might provide a solution without being tied to the ontology.

Another drawback of the ontology is that it creates an additional step along the provenance of the data, which can slow things down or change what that data actually is once it reaches the end user.

Always having to work with a Palantir representative creates severe bottlenecks and increases costs, making it desirable for me as the end user to perform tasks without constant requests for support.

I would like to see a reduction in the need for field service representatives from Palantir, and I hope for a more intuitive architecture that makes it easier to find things and perform tasks without a high learning curve.

I have been working as a data scientist for six years.

I find that Palantir Foundry is stable sometimes.

The scalability of Palantir Foundry seems to be fairly good, considering how many users we have. It still operates well without significant lag in performance, so the scalability seems to be acceptable.

The customer support can be frustrating, depending on where I am working from, especially if the demand signal needs resolution from a Palantir representative.

We did not use a unified solution before.

My general impression is that it has not paid for itself yet, as it is a very expensive platform to use and the government is still fairly early in utilizing Palantir products. I would say that we have not received a good return on investment yet.

I did not evaluate any other options before choosing Palantir Foundry, as the choice was not mine to make. I was not responsible for selecting Palantir.

My advice to others looking into using Palantir Foundry is to seriously consider the cost of using it and whether you are comfortable relying on a Palantir representative to complete your work or if you think you can manage without any Palantir representation. Additionally, consider if your solution can follow a different path and make a comparison. My overall rating for this product is seven out of ten.

Public Cloud

Amazon Web Services (AWS)

My main use case for Palantir Foundry is to provide an assisted, AI-assisted automation chatbot for the finance team; they reside in corporate finance and they have a lot of manual repetitive work that takes up to two hours to process manually. I have automated it with AI in about under 15 minutes, which was a huge win for them, using Palantir Foundry.

A quick specific example of one of those manual tasks I automated for the finance team is their manual analysis of a vendor before they onboard the company and do business with them. They do a financial capability analysis, which involves extracting data from unstructured PDFs that come in different formats; I used Palantir Foundry to employ the Visual LLM to extract the data in a structured manner. Once I have the data structured, I can create real-time dashboards for them to analyze the vendor in under five minutes.

I mainly use Palantir Foundry to process unstructured data into a structured format for chatbots, and that saves me a lot of time to build chatbots that are accurate, reliable, and answering questions for the users accurately.

Palantir Foundry has positively impacted my organization with over 200 projects completed efficiently and effectively, and they are very complex projects.

While I cannot speak for other projects, I can say that on my projects, I have saved over 100 hours processing multiple different vendors, which is huge for the corporate finance team.

In my experience, some of the best features Palantir Foundry offers include a great UI that is visually appealing, simple, and straightforward. The documentation is clear, and it is very user-friendly for me; I do not have to lose myself in a rabbit hole of documentation unlike Microsoft. I am starting to get tired of Microsoft, but Palantir Foundry has been the perfect solution so far.

What makes the UI stand out for me is the Artificially Intelligent FDE, which is the Forward Deployed Engineer; it is basically an AI within Palantir Foundry that helps me get things done. I think that is the most helpful part of Palantir Foundry, and I did not even need any training; I just had to work through it myself with the FDE, and with a bit of prompt engineering, I was able to get onboard Palantir Foundry very quickly.

I appreciate that Palantir Foundry can seamlessly integrate with SharePoint, which is very helpful for enterprise work, but other than that, nothing else comes to mind.

I really cannot think of any areas where Palantir Foundry can be improved; I think it is perfect.

I believe it is already perfect with FDE, and it is just a matter of continuing to improve AI FDE and making sure that it is a number one priority tool to utilize in Palantir Foundry, because users are naturally going to be inclined to use that.

I have been using Palantir Foundry for the past three months now, and it has been amazing.

Palantir Foundry is stable.

We have yet to find out about Palantir Foundry's scalability, but I am pretty sure it is fairly scalable and should not be an issue.

I have not dealt with customer support directly; I have interacted with representatives, and they have been very supportive.

Before using Palantir Foundry, I used Copilot Studio.

I switched from Copilot Studio to Palantir Foundry because Copilot Studio was very inaccurate, and the documentation was not clear; overall, Microsoft was not meeting my needs.

I have seen a return on investment with fewer developers needed.

I did not evaluate other options before choosing Palantir Foundry; I did not have any other options.

I find Palantir Foundry's accuracy and reliability of output to be fairly accurate; it takes a bit of trial and error. I discovered that Claude Sonnet 4.6 is the best model for unstructured data, and that is more a reflection of the model delivery companies such as Anthropic and OpenAI than of Palantir Foundry itself.

I have no idea what advice to give to others looking into using Palantir Foundry. I would rate this review a 10.

Public Cloud

Other

I have been using Palantir Foundry for about two to two and a half years. My main use case for Palantir Foundry is data analytics.

I get asked to do a particular project for data analytics. I research Palantir Foundry for the datasets that I am looking for. Sometimes I create datasets from other datasets, and then I either export the file or create a report on Palantir Foundry.

I think you have to exercise using Palantir Foundry to better understand how it works. However, there are tutorials and AI assistance with Palantir Foundry, which makes things easier.

The best features Palantir Foundry offers include the Workshop, which is excellent. It offers a widgetized method of developing dashboards and reports. The AI assistance is very good for trying to shape your report.

The Workshop feature is an app that allows me to build a dashboard or a report on Palantir Foundry.

Palantir Foundry impacts my organization positively by creating a central repository of data sources from all the other sources of data, making it a one-stop shop for any sort of data research.

It has made things easier in finding data. Though as a caution, had I tried to search for this data the normal way through the normal source, I probably would not have been allowed access to said data. However, because all the data is being pulled into Palantir Foundry, it makes it easier for me to access data that I have been restricted from.

In my use of Palantir Foundry, many people can go in there and create datasets, save datasets, and share datasets. However, if many people make datasets of low quality or if they are using the same name for datasets, it can get very confusing. So, it does not seem like there are any sort of business rules when it comes to naming your dataset or keeping your dataset active, making it quite messy depending on who is accessing it and what they are doing with it.

I am pretty sure Palantir will get around to coming up with better business rules and cleaning up bad datasets. It is only a matter of time.

I have been working in my current field for about four years.

As far as I know, Palantir Foundry is stable.

I believe there is a team working on gathering more data sources for Palantir Foundry. I can request additional data connectors, but it seems so far there has not been much restriction or rejection for additional data connectors.

Customer support for Palantir Foundry has been pretty good so far; I have only had one incident, and I got a response back rather quickly.

Previously, I would have to pull Excel spreadsheets from various sources and then make a report out of Power BI. I still use Power BI, just instead of going to multiple sources, I go to Palantir Foundry as a one-stop shop for my data sourcing.

Palantir Foundry makes data reporting easier and reduces time in data gathering and reporting. What would usually take about a day and a half of data gathering and reporting, I have ultimately reduced to about 20 minutes.

I do not have any experience with pricing, setup cost, or licensing since it all comes with the company that I work with.

There were no options before choosing Palantir Foundry; it is pretty much promoted by the company.

My advice to others looking into using Palantir Foundry is to go through the tutorials that are offered. Take the time to go through the lessons, and if you have any experience in SQL, probably improve your SQL knowledge. I would rate this product a seven out of ten.

Private Cloud

Other

My main use case for Palantir Foundry is primarily focused on time series related information, visualization, and a few applications where the background involves AI.

I receive different sensor data through time series and apply business logic on top of that. With the aggregated data, I perform visualization according to business requirements. The logics and everything are implemented both as core native logic within Palantir Foundry itself.

This is the most common use case I work through. Apart from that, there are a couple of additional projects involving workflows where resource management needs to be handled. This includes resourcing schedules as well as job allocation.

Palantir Foundry is deployed in my organization as a public cloud only.

In my opinion, the best features Palantir Foundry offers are that it is not rigid and provides low-code, no-code capabilities.

The low-code, no-code facility allows people with less technical knowledge who have domain knowledge in a particular field to directly use the application and readymade widgets to prepare their applications in a much faster way.

Another advantage is the Ontology layer, which serves as a business layer. Once the data is set on the Ontology layer, it can be accessed across multiple divisions.

These are the two main points. The overall architecture is definitely very robust and handles both the velocity and volume of data so that end users do not need to manage these concerns.

The sensor data and applications built on top of Palantir Foundry represent the main advantage my organization is currently taking.

The widgets are pretty limited. While they continue to improve, the widgets remain limited. If you want to create customized applications, it will be difficult. Using their standard widgets and features works very well, but any kind of additional customization needed will be challenging.

There are many widgets for specific needs or specific ways to build applications, but not all of those widgets or features are available in Palantir Foundry.

I have been using Palantir Foundry since 2023 across different projects within my current organization.

Palantir Foundry is stable based on my experience.

I am not certain about Palantir Foundry's scalability because I am particularly on the data engineering services side.

The customer support for Palantir Foundry was good, but it can be improved.

I came across multiple data engineering solutions before using Palantir Foundry.

I think it is both a time-saver and enables better decision-making because the sooner we get predictions or anomaly detection, the more helpful it is.

Compared to other SaaS tools, Palantir Foundry is definitely a time-saver, though I do not have specific metrics to share.

As a whole product, Palantir Foundry is well secured. Even though there is a capability of integrating with external applications, if all your data resides in Palantir Foundry, it is quite secured and includes most governance and security measures.

Personally, I am not involved much with AI capability related work.

I am not certain which cloud provider is used for Palantir Foundry. As a developer, I am not much aware of which cloud provider is used for Palantir Foundry.

My overall rating for this review is 7.

My main use case for Palantir Foundry is for decision intelligence. It functions as an operational operating system that connects fragmented data into a single ontology for our insurance provider, utilizing principal and component analysis.

An example of how I use Palantir Foundry for decision inclusions or connecting fragmented data is through the core mechanism of data connections to building Pipeline Builder, then using ontology chain. Data connection ingests data from different disparate sources such as Zabbix, health signals from Zabbix, and PostgreSQL external APIs. Now living in separate systems, Foundry pulls this into a unified raw dataset layer without needing custom ETL glue code. I transform those raw inputs into clean, joined datasets, and the ontology is where fragmentation is truly resolved. Instead of querying disparate tables, every entity, device, and incident becomes a pipeline run that functions as an object type with properties and links to related objects. A network object, for example, links to its Zabbix alert objects, its health metrics time series, and its owner, all the exact type of multi-source join that I can rebuild, rather than writing SQL joins every time. The ontology makes these relationships first-class.

In our infrastructure data spread across Zabbix, PostgreSQL, and Zyco, the classic problem is that each system has its own ID scheme, update cadence, and schema. Palantir Foundry's ontology solves this by creating a canonical object model on top. I do not migrate or replace the source systems; I overlay a unified semantic layer. Every downstream consumer reads from that layer and not from the raw source directly.

The best features Palantir Foundry offers include the ontology, which is not just a tool. Palantir Foundry can collect data from an existing system without modifying those systems, and once the ontology is ready, I can create a business application, analyze data, or build AI models on top of it. Team report projects' speed becomes faster than using multiple separate values. The ontology is not just a data model; it is a semantic layer that represents real-world entities, relationships, and decisions that act on them. Palantir Foundry supports ingestion, transformation, semantic modeling, analytics, and operational application development in one platform. This reduces the need to switch between separate tools for pipelines, governance, and downstream consumption. It is well-suited to use cases where analytics output must be embedded into operational processes rather than limited to dashboards.

Using this semantic layer in Palantir Foundry connects the fragmented data that meant writing JSON logic repeatedly across dbt models, Snowflake views, and Airflow DAGs. Every new consumer, such as a dashboard, a report, or an API, needed its own interpretation of what a device, incident, or transaction meant. Schema changes broke downstream consumers silently. Governance aimed to stitch together from dbt docs and Airflow audit logs and manual metadata. With Foundry's semantic layer, once the device object type is defined with properties from Zabbix, links to alert objects, and links to pipeline objects, every downstream tool, workshop, contour, API, or code is declared and reads from the same canonical definition. I can change the schema once in one place that lives on the object, not scattered across application code.

I am adding features such as Foundry Branching and data connection source agnosticism, particularly the Contour Aggregation Branch. Most people treat Contour as a BI tool, but it is actually more powerful than that. The Aggregation Branch lets me perform multi-step analytical transformations interactively and then publish those as a dataset back into Foundry. An analyst's exploratory work becomes a reusable data asset, not a one-time report. The boundary between BI and data engineering is resolving.

Using Palantir Foundry has positively impacted my organization because I studied Foundry deeply and built the manual equivalents of what Foundry formulizes across connections. I understand what I absolutely need to document, customer outcomes, and what I can totally solve because I know exactly what it costs to solve them.

Palantir Foundry has a steep learning curve and onboarding.

Going deeper, there are additional improvements needed in real engineering solving, such as code reuse and language lock-in. Palantir's lens compute modules were introduced specifically to solve the problem of integrating existing code into Foundry instead of rewriting the logic in Foundry's supported language. I can now containerize code and deploy it directly, with the platform handling scale, authentication, and connection automatically, but this is still maturing. If my team has production-grade Python modules, Kafka consumers, or consumer ML models, I had to prolong rewriting in Foundry's transformation paradigm and maintain a parallel codebase. Neither is acceptable at scale. Improvement is still needed for full parity between containerized workloads and native Foundry transforms in terms of lineage tracking, monitoring, and ontology write-back.

Debugging and observability in pipelines is one of the major drawbacks. However, the platform has many good features and is a good foundation to build upon in the future.

I have been using Palantir Foundry for three years.

Palantir Foundry is a highly scalable solution.

The customer support is great.

I believe the pricing, setup cost, and licensing could be lower.

I have seen money and time saved, but we need to yet see the return on investment.

Before choosing Palantir Foundry, I evaluated other options, including Snowflake and Tableau.

Palantir Foundry's AI capabilities, governance, and security are top tier as it is a fully locked environment and is best for government organizations where data security is a major consideration.

When I use Palantir Foundry's AI features, I find the outputs to be reliable and accurate and have not run into any issues; I trust the results. This is one of the most nuanced topics in the Foundry ecosystem. When the industry pivoted to LLM, the prevailing approach was pointing probabilistic engines at vector databases via standard RAGs, yielding results that were semantically plausible but structurally ungrounded with an unacceptably high hallucination rate and no distinction of facts. Palantir's approach diverges entirely; instead of retrofitting an LLM onto a flat data warehouse, AIP embeds LLM directly into a bidirectional knowledge graph. The resulting architecture dictates that AI interacts with the enterprise through a strictly governed semantic layer that natively understands relationship logic and operational constraints. In plain terms, standard RAG retrieves text chunks and lets the LLM guess connections, whereas OAG retrieves typical ontology objects with live relationships and lets the LLM reason over structured truth. The accuracy difference is architectural, not just prompt engineering.

I would advise others looking into using Palantir Foundry to get in early as soon as you can. If you are mature, do not. I give this review a rating of eight out of ten.

On-premises