Palantir Foundry Reviews

My main use case for Palantir Foundry is building a targeting system for NATO and allied forces using Maven. A specific example of how I'm using Palantir Foundry in that targeting system project involves ingesting data from ISR drones and from satellite systems, streaming those data as part of data ingestion, building the pipelines, running the analytics, and building the dashboard.

The best features Palantir Foundry offers for my work include that building the ontology is very easy and it is easy to use.

The easy ontology feature helps my team because it is faster to build, and when anything goes wrong, I can see that it is easy to do the debugging.

Building the analytics dashboard helps and makes it very visual for the end user, and it is very easily customizable. Palantir Foundry has positively impacted my organization because we are mostly still building POCs for the end client, so when we show the working solution, it is easier to get feedback and iterate.

When I demo the solution, there is a faster feedback loop and more engagement from clients, which impacts my team's workflow and project timelines because we can get a solution built in a couple of weeks.

Palantir Foundry can be improved by providing more information online. For example, it would help if Palantir could put more updated tutorials and knowledge articles on YouTube since some of those articles are outdated, and up-to-date information will actually help.

I have been using Palantir Foundry for more than a year.

Palantir Foundry is stable in my experience. I would add that live data streaming is very hard and it keeps breaking, so it is not very stable and depends a lot on the satellite network.

The scalability of Palantir Foundry is very good.

The customer support for Palantir Foundry is good.

I have not used any previous solution; what I am working on is very unique to Palantir Foundry.

I have seen a return on investment; specifically, fewer employees are needed.

My experience with pricing, setup cost, and licensing is that it is flexible.

I did not evaluate other options before choosing Palantir Foundry.

My advice for others looking into using Palantir Foundry is to start building and get quick feedback. I would rate this product an 8 out of 10.

On-premises

The main use case for Palantir Foundry is to build dashboards and pie charts, with applications like Workshop where I use Pipeline and Workshop on a daily basis for data integration, making objects, and building dashboards.

In my daily work, I ingest the data from the data connection into the pipeline builder, clean in the pipeline builder, and make separate pipelines for the cleaning. After cleaning the pipelines, I integrate them. Following the data set creation, I integrate those and make the object by combining it with other objects as per the client's requirement, such as showing images of the dashboard's results. I use Ontology to show dashboards, pie charts, maps, and quick results from Workshop by bringing images from S3 to display in Workshop.

For the data sets, if I have a new website for mining, I have to make an identical data set for it. Thus, I have used Pipeline Builder mostly to make a couple of data sets with similar identical fields present in the website, which must also be in the data sets, focusing on two crucial joins and unions.

The best features Palantir Foundry offers include the semantic layer providing schema-level understanding about the data, low-code and no-code integration for ease without coding in the pipeline builder, AI Assist for assistance, Ontology for digital twin relationships, branching in pipeline level and Foundry branching for better management, zero-copy architecture for querying without massive data, data lineage for troubleshooting, and security changes that can be made in the pipeline builder and Ontology Workshop.

I find myself using the branching mostly in the pipeline branching because it is very helpful. In Workshop, there is versioning that saves every draft. If I make a mistake in the current production, I can easily take the previous version and correct the current version, preventing errors.

What I found interesting about Palantir Foundry is its media set capability to ingest unstructured data and apply changes, making it simpler for showing in Workshop, which I use frequently.

The low-code and AI assist help me get unstuck, make changes in configuration, and reduce complexity, aiding better time efficiency for the team.

Sometimes, when I am handling a huge amount of data, I notice areas for improvement.

The performance of Palantir Foundry could improve, particularly due to the high cost of building data pipelines, detailed security policies, and the heavy setup for small to medium-sized data sets.

Regarding complex data, improvements are needed for efficiency.

I have been working in this area for three months.

Palantir Foundry is reliable and stable, but handling huge data and complex data can make operations slower.

When dealing with huge data sets, it takes time to load and handle those larger workloads.

While using Palantir Foundry, I have not faced any issues requiring customer support.

I switched from Azure Data Factory, which focused on cleaning the data and pipelines, to Palantir Foundry due to its more efficient features like branching and versioning in the pipeline builder.

I switched to Palantir Foundry during a mining project after comparing it with other applications used for replicating data sets and creating dashboards.

I have not seen a return on investment with Palantir Foundry since that information was not shared, and currently, we have only two employees for the project, leading to needed savings.

Pricing for Palantir Foundry is high compared to other cloud solutions. Setting up typically utilizes existing Palantir setups from clients to integrate data for building dashboards and insights.

I cannot advise someone to use Palantir Foundry due to cost efficiency and the complexity it introduces in handling large amounts of data. I gave this review an overall rating of eight.

Private Cloud

Amazon Web Services (AWS)

My main use case for Palantir Foundry is for data transformation using the PySpark code through the Code Repo or Pipeline Builder, so the majority of the time, I am transforming data based on business needs.

A specific example of a project where I used Palantir Foundry for data transformation involves cleaning data from across the world in the first phase before processing any of the business logic, which depends on the quality of the data received. I adjust timestamps, understand data types, and filter out inactive records, so all these cleaning-related tasks are very important when building pipelines in Palantir Foundry. With respect to business logic, it varies case by case, where I might need to conduct time series analysis, patient records, or work with product-related datasets that include product information and sales countries, making the classification and organization of data a complex task I am involved with.

I believe the best features Palantir Foundry offers are Ontology Manager, Workshop, and the data connection, which combined provide powerful capabilities that should be utilized in all projects. The data connection is seamlessly helpful for connecting with various data sources, while the ontology is a valuable concept from Palantir, where data is created as a layer and utilized by many affiliates. This assists the front-end team or those who build dashboards using Workshop, simplifying the work. Workshop executes designs into dashboards with ease, which I find beneficial.

Palantir Foundry has positively impacted my organization by enabling us to deliver projects quicker, as it organizes data from across the world into a dashboard that can be managed in one single location, accelerating business decisions made by the leadership team.

When I refer to it accelerating business decisions, I am referring to our marketing campaigns, where users take real-time actions in the field, and it is crucial that our data remains accurate. If our data deviates from the standard, it must be addressed immediately as this directly impacts the marketing and sales teams, and I ensure everything is in place so the business runs smoothly.

I do not think Palantir Foundry needs improvement; I feel everything is good as of now and I have no specific comments.

I do not think there are needed improvements, but I feel that since tools are continuously upgraded, conducting training sessions provided by Palantir would enhance our tool usage, even if documentation is provided.

I have been using Palantir Foundry for five years, and I continue to use Palantir Foundry to date.

My advice for others looking into using Palantir Foundry is that they should first get trained with the basics, as it is a powerful tool, and understanding its capabilities provides a strong foundation for effective use. Decision-making trusted by leadership requires accuracy, so it is better to get trained and then practically apply the knowledge. I would rate this product a 10 out of 10.

Public Cloud

Amazon Web Services (AWS)

In my job, I use Palantir Foundry exclusively to create multiple dashboards. For example, I use Palantir Foundry to create a dashboard corresponding to the visualization of many charts by extracting the dataset, which is Skywise, putting this dataset in ontology, and using the different tools in Palantir Foundry. This is my typical use case in my job.

My last dashboard created with Palantir Foundry is regarding the Project Speed Project Dashboard, which helps analyze more programs because this dataset comes from Skywise, where my principal customer is Airbus. This project clarifies all the X-tracker, enabling tracking of multiple defects in programs such as the A320, and visualizing all action plans for non-quality across multiple programs. This is my first job for the dashboard speed, where I also plan to add, modify, and delete actions we want to track including all performance analysis for the high to left performance.

The best feature that Palantir Foundry offers in my experience is the Ontology Manager, which stands out to me because it allows us to see if we have the write-back dataset to understand what to add, delete, or modify in our dashboard and it displays our modifications in materialization, which is very good. Another aspect in ontology is that we have the possibility to update manually and see changes very quickly, which is a good feature that I apply and use in Palantir Foundry.

The Ontology Manager has helped me create an object or action, for example, using TypeScript, which is new for me, and it allows me to point to the Ontology Manager or the object type in the slate very quickly.

The Ontology Manager positively impacts my organization across all projects because it incorporates new technology and features that can be applied globally, making the impact on my work and organization very high.

I cannot provide specific outcomes or metrics on how Palantir Foundry has made a difference because in all my projects, I am the only developer and do not interact with other developers, only interacting with the customer, who is not a developer. Thus, I cannot see the difference at this time, as I am the sole developer on all my projects.

I want to pass the certification of Palantir Foundry because it is not easy to find the information regarding this certification, making it not accessible for many people, which I think is not good. If it is possible to plan for accessibility to this certification, it would greatly benefit many individuals.

I have nine years of experience in Palantir Foundry, which I used during my first internship and during my master's degree at the University in Nice Sophia Antipolis.

If I pass the certification, it would be the best thing for me as a Data Engineer, especially the Data Engineer Professional Palantir Foundry certification, which I consider important for my career.

I always take time to explore all aspects of Palantir Foundry, including the Ontology Manager, object set, object viewer, and object explorer, which I find valuable. Palantir Foundry has improved with the generation of AI, and I think the governance and security are both good things to have in Palantir Foundry.

I find that the AI capabilities of Palantir Foundry provide great accuracy and reliability, comparable to tools such as ChatGPT and Google Gemini, indicating strong output in terms of accuracy and reliability.

My advice for others looking into Palantir Foundry is to pass the certification, which I believe is very important to demonstrate that one's experience is applicable and valuable in exploring everything that Palantir Foundry has to offer. I rate Palantir Foundry a nine out of ten because I have a good experience and find working in Palantir Foundry very easy, as it offers many possibilities for growth and cooperation with people from all over the world.

Private Cloud

Amazon Web Services (AWS)

My main use case for Palantir Foundry is defining datasets and working with data for a client in the insurance sector.

In the context of that use case, we have to show information about the customers in a dashboard, and for that we have the user datasets, some other information that comes to us in raw form, we have to clean it, and we show all of that using Palantir Workshop in an application that the other users access.

We helped the client implement it in their organizational core and were helping them during the process of organizational and paradigm change, since they didn't have Palantir Foundry before and now the whole company has started working with Palantir Foundry. We believe that the client has improved their efficiency compared to their previous working model; they have more clarity and, above all, more control over the data.

Without a doubt, the best features that Palantir Foundry offers are the abstraction and the creation of an ontology and being able to work directly with the ontology from the other applications, whether it's for doing analysis or for custom applications.

The ontology functionality has made our work and our team's work easier since we define ontologies based on the types of data we want to display, allowing us to cross several data points from several customers from several locations. We can abstract and create an ontology that is only for customer, some for receipts, policies and such, as it is related to the insurance sector. We can also create ontologies for the locations, different types of applications and such, which has made it easier for us to have that abstraction capability and not have to be looking at the raw datasets.

I don't see any feature, process or aspect that I would like to see optimized or changed in Palantir Foundry at the moment.

I have been using Palantir Foundry for two years.

I consider Palantir Foundry to be a stable platform.

I find the scalability of Palantir Foundry suitable to grow according to the client's needs.

My experience with customer service or technical support from Palantir Foundry has been very good since we had a person from Palantir on our team twenty-four hours a day internally. They provided support and managed all incidents, and we gave them our feedback so they could communicate directly with Palantir Foundry's development team.

I don't know if they previously used any other solution before Palantir Foundry, as I joined the client when they were already implementing Palantir Foundry and I don't know what tools they used before.

I'm a consultant and the integration of Palantir Foundry was not specifically in my organization, it was at the client.

We helped the client implement it in their organizational core and were helping them during the process of organizational and paradigm change.

I don't have information about observed return on investment after implementing Palantir Foundry, as that corresponds to another department.

My experience with the price, implementation cost, and licensing model of Palantir Foundry is limited because that data is managed by another department and I, as an engineer, don't have access to that information.

I don't know if they evaluated other options before choosing Palantir Foundry, nor am I aware of which ones they might have been.

My advice to other companies considering using Palantir Foundry is to take the leap if they have doubts, since it's a very good platform and, although at the beginning the learning curve can be a bit steep, once it stabilizes, growth is exponential. I give it a rating of nine out of ten because there's always room for improvement. No application is perfect and we all have the possibility to improve.

Private Cloud

Other

One of the leading European manufacturing plants uses Palantir Foundry for manufacturing interior parts of various car brands such as Honda, Hyundai, Ford, Mercedes-Benz, and BMW. This involves highly secured information that is not supposed to be shared with any competitors.

My experience with Palantir Foundry and Azure has been good. Palantir Foundry is costly, but Azure is open, which allows for easier experimentation. Being a closed product, Palantir Foundry is difficult to practice offline unless we have an enterprise edition. However, it is very secure compared to other platforms.

Palantir Foundry's best features include security, built-in features, low-code, no-code platform, and ease of use.

The collaborative workspaces within Palantir Foundry contribute to team efficiency and project outcomes through seamless operation. The ease of customization is particularly notable.

I have worked with the data lineage feature in Palantir Foundry, which comes by default. We simply need to tick the checkbox and make necessary configuration changes within the system itself. We do not need to procure another lineage platform as Palantir Foundry has its own built-in features for data lineage, data governance, and data security.

The lineage feature helps enhance our data management practices by allowing us to understand the origin of data, track all activities happening on the data, identify users and consumers, and monitor how it flows across the system. This makes it easier to generate reports based on the lineage database.

The predictive analytics capability within Palantir Foundry impacts financial forecasting strategies through its AIP functionality, which includes numerous pre-built models, LLMs, and data science application libraries. Using the AIP library within Palantir Foundry helps us develop quick resolutions for predictive models and analytics.

Apart from the pricing and offline availability issues, improvements are needed in Palantir Foundry's costing factor. Cost-wise, it is not open for everybody, and they are not exposing anything outside of the box.

The major hindrance with Palantir Foundry is that being a very closed product, the cost optimization and costing are not exposed to the end users. Apart from that, it is a very good tool and product.

In terms of stability and scalability, I have not faced any challenges. The scalability and scheduling capabilities are very good. Regarding performance, I have not experienced any stability, performance, or security issues.

I haven't had the opportunity to discuss with Palantir Foundry technical support, but we were able to manage on our own. The documentation and technical support are very good.

Positive

Palantir Foundry is an excellent product for data engineering. On a scale of one to 10, I would rate Palantir Foundry a 9.

Microsoft Azure

I am getting into the ontology space using Palantir Foundry. The primary use case is for developing a common business model that includes data, people, and processes, essentially describing how businesses operate. We are applying this model in the utilities sector.

Palantir Foundry offers the core capabilities of a digital twin enterprise. The digital twins concept allows bi-directional data and process integration between virtual and physical worlds. This integration enables what-if analysis, such as simulating tariff changes to plan responses. The low-code app development platform empowers any user to build applications using preferred visualization tools, while the AI platform allows for diverse model integration. Furthermore, Palantir Foundry is not limited to one LLM model, offering flexibility to build personalized models.

Palantir Foundry is missing marketing, which could help it grow. Additionally, the startup pricing is high, causing concern despite being cost-effective in terms of total cost of ownership. Palantir Foundry also needs to change the traditional data management approach from one-directional to bi-directional, near real-time data flow everywhere, which they address through data virtualization.

Palantir Foundry's customer service is excellent. I would rate it ten out of ten. They are knowledgeable, and their boot camps demonstrate solutions in just three days, which typically takes months or years. This speed of delivery was a key driver for choosing Palantir Foundry.

Positive

Palantir Foundry's initial setup is affected by high startup pricing, which might scare some people off. However, it is cost-effective in the long run, as it reduces the need for developers.

Palantir Foundry is expensive to start, but not costly in terms of total cost of ownership. Its high initial pricing can be intimidating, but it becomes cost-effective as it reduces the need for a development team. Traditional data management is being transformed to bi-directional, near real-time data flow using data virtualization.

I have heard about a partnership between Databricks and Palantir Foundry. Databricks has similar concepts focused on data.

I rate Palantir Foundry a ten out of ten. Overall, it's an excellent solution, and I recommend it. While I am building a business case to adopt Palantir Foundry, it's worth noting its partnership with Databricks. Our organization already has Databricks, and I hope we will get Palantir Foundry as well.

Other

Our use cases are mostly related to data analytics. We are building some dashboards and ETL pipelines on the Palantir side. Palantir Foundry is a low-code/no-code platform with a user-friendly UI. It is better than Databricks, where you need to code. Palantir Foundry has better data lineage. However, Databricks also provides many features with Databricks Unity Catalog.

I like the data onboarding to Palantir Foundry and ETL creation.

The solution’s data security could be improved. We cannot use many Python packages with the solution. We were able to use only a few compatible Python packages.

I have been using Palantir Foundry for six months.

I rate the solution’s stability a nine out of ten.

We couldn't implement or use some of the latest functionalities, like Spark. Palantir Foundry is scalable, but it is costly compared to other cloud providers. The solution is more suitable for small and medium businesses. It might be difficult for large enterprises.

I rate the solution’s scalability a seven out of ten.

The solution’s initial setup is simple compared to that of other tools.

Palantir Foundry is an expensive solution. However, it works because we need to develop a little less compared to Databricks or any other environment.

I prefer Palantir Foundry for simple ETL pipelines because it is a low-code/no-code platform. I will choose Databricks for handling complex big data because it supports all the Python modules.

We tried some machine learning algorithms with Palantir Foundry. Since some packages are unavailable, we have to do that specific work on the Azure environment. I would recommend the solution to other users, but they must evaluate data security concerns.

Overall, I rate the solution an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

We mainly used the solution to ingest our internal data. 

We have a client which uses it for insurance. The insurance company needed to calculate the premiums. In order to do that, we basically get our data from many regions, maybe New Zealand or Australia, Asia, and India. We focus on those regions. 

From those regions, get data in the form of Excel sheets or CSV files. We ingest those formats into the Palantir Foundry platform. And after ingesting, our data scientists upload the stream. Basically, they are trying to calculate the premiums on the basis of the data funneled over to them.

The best thing about the solution would be the data lineage. We use it to see the data sources in one place, including where the data is coming from and where it is going. The visibility is very easy. Right now, I'm working with another cloud platform and in that, and I don't see that option. I'm not able to easily know the sources. In Palantir, it was quite easy.

The solution is stable. 

It's scalable.

Sometimes, when we used to ingest the data, it was not able to know the schema exactly. For example, if we had some semi-structured pile. If we uploaded that into Palantir, maybe sometimes it would not be able to find the correct schema which we were expecting at first.

The solution could use more online documentation for new users. 

Technical support isn't very quick to help. 

It's an expensive product. 

I used the solution for about a year, eight months ago. I used it in my previous project.

I worked with the solution for about a year and found it very stable and reliable. It never broke down. There were no bugs or glitches. The performance was good. 

I'd rate the stability at about an eight or a nine in terms of reliability.

The solution was scalable. That was never a problem for us. 

Between ourselves and the client, we had 50 to 100 people on the solution. We had a few data engineers and data analysts on it. Data scientists often used it too. 

At the time, support was not very good. I would have preferred it if they were more helpful and responsive. 

Neutral

We are currently using AWS. It is not as visible and doesn't show details about the data. Palantir was chosen as it was very user-friendly. 

The initial setup had a medium level of difficulty. If we go through the documentation, we can learn about what to do. In Palantir, they had a section called Academy, and that Academy was quite useful. If you go through that as a new user, it makes the process easier as you learn what to do. Initially, we didn't have many sources that would help us learn things, so we struggled a bit. In contrast, with Azure and Amazon Cloud, you have many sources from where you would be easily able to learn. You could just Google what you needed with them, as there's so much available documentation online. 

What was easy was the fact that everything was in one place. With AWS Cloud, there are many applications to support. You can use Glue or Athena, and you have all these other applications. However, with Palantir, everything is easy due to the fact that it is centralized. It's drag and drop and everything is very transparent. 

When I joined the company, it was already in place, and therefore I can't speak to who was involved in the initial setup. 

While I do not know the exact cost, I have heard that it is quite expensive. 

I was an end-user. 

I'm not sure which version of the solution I'm using at this time. 

Whether or not I would recommend the solution depends on the use case. A startup, for example, may find it to be expensive. A larger company would have the budget for it.

I'd rate the solution eight out of ten based on the general ease of use of the product. 

I didn't use Foundry, but I went through some training, and my team became certified in it. When I left the company, there were probably 100 research projects that had been added to it. I did it project by project. Around 30 were completed. About 40 or 50 were in progress, while there were 20 more in the queue. You could reuse data and leverage data that had been imported. We imported lots of Epic data.

You needed permission to see the Epic data.  Someone with a research project approved by the institution could ask permission to join it with other data. In a relational world, you could say, "I'll give you database permissions, but I'll need to mask these columns that are based on those." It's similar to an SQL database.

People submitted their project requests to a project review committee. The capacity was limited because people needed to understand the platform, but I'm sure they have trained more people on it since then.

Foundry's data visualization is fantastic. If you had a solid data person sitting next to a researcher, you could do remarkable things with data visualization and transformation. 

The security is also excellent. It's highly granular, so the admins have a high degree of control, and there are many levels of security. That worked well. You won't have an EDC unless you put everything onto the platform because it is its own isolated thing.

The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points.

It was hard to generalize that and put it into a catalog for people who you didn't know to reuse data. Maybe it's different now. That's the nice thing about Informatica: The catalog is reusable. Palantir is successful, and the institution loves them. I don't want to disparage it. I'm just speaking technically from an interoperability perspective.

Foundry was highly stable. It didn't go down. They were hosting their implementation on a stable platform, but they have been improving the breadth and depth of their offering based on feedback from us.

It's a cloud platform, so it's very scalable.

We had account reps from Palantir, and they were our interface to tech support. They were pretty responsive. The tech support may have been in the UK, but we had a US representative. You give the rep a feature request, and they add it to the queue. It's not like, "Oh my God, our logs are filling up" or something. It wasn't that the kind of thing you would get with an on-prem solution. 

They were attentive. We had team calls with them several times a week. They were part of our whole implementation support. They're part and parcel of what the institution is doing. I give them very high ratings for attentiveness.

You don't need to set Foundry up. It's on the cloud.

I rate Palantir Foundry five out of 10. I'm ambivalent. Palantir's support is excellent, and you can do a lot with it. On the other hand, it's not open. 

People who enjoy working on it will give it a 10. However, I'm focused on interoperability when I think of an EDC. There's no dynamic metadata export or visibility. If I want data lineage, I have to go project by project. If I run a command, it goes to GitHub and then executes the command to pull out a current state lineage. I have to export that and then bring that into my EDC.