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Palantir Platform
Palantir Platform empowers organizations to effectively integrate their data, decisions, and operations.
Reviews (50)
Habeeb Mustafa
Unified data access has transformed our decision making and collaboration across countries
Reviewed on Jun 03, 2026
Review from a verified AWS customer
What is our primary use case?
I work in an organization which uses Palantir Foundry, not as a customer, integrator, reseller, or consultant.
What is most valuable?
Our main problem statement with Palantir Foundry was that we have our data in corporate systems, primarily SAP-based, and many other systems managing HR, logistics, finances, and program activities, all working in silos without communication. Whenever we need to integrate something, it has to be done outside those systems, which could involve a lengthy, customized solution process and maintenance hassles. Updating the data becomes problematic as it turns static once extracted, requiring repetition for new updates, which compounds errors and effort across 120 countries and multiple locations. There was no central truth to the data, leading to discrepancies as everyone applies their own algorithms. This ongoing issue is rampant across organizations; I recognized it since joining in 2001, noting that other solutions like Tableau or Power BI merely mask the problem rather than solve it. Palantir Foundry was clearly the solution we needed; it has upgraded our thinking, decision-making processes, data handling, and overall data literacy. Now, the democratization of data occurs as everyone learns to access and analyze it uniformly. This shift has fostered new habits in data sharing and reporting while making previously theoretical efficiencies actionable.
What I appreciate most about Palantir Foundry is its focus away from mere reporting beautification; organizations historically prioritize chart aesthetics and transitions. Tableau exemplifies this with flashy features that, while entertaining, lack substance. The real value lies in key numbers and easy complex calculations, traditionally requiring extensive configuration. With Palantir Foundry we have eliminated the complications of such custom solutions, and it synchronizes data across systems effortlessly. Whether I am handling 10 rows or 10 billion, it processes the data efficiently, employing Spark for computations based on data volume. Moreover, the integration of foundational technologies into Palantir Foundry allows users to work with Python or SQL, maintaining a universally recognizable skillset. Even if you are new to these languages, learning them within Palantir Foundry equips you for future career shifts. While Palantir Foundry is becoming somewhat restrictive, it fundamentally educates users on widely applicable skills.
What needs improvement?
I believe one significant enhancement for Palantir Foundry could be making it more accessible for managers and decision-makers who often lack time for in-depth analytics. Currently, analysts handle the in-depth analytics and present information to managers. Bridging this gap means making it effortless for users to quickly open the platform and find the information they seek, which could improve with the introduction of AI assistants. Instead of needing to navigate complex queries, users can simply ask questions and receive the necessary data, making managers happier with Palantir Foundry's capabilities.
For how long have I used the solution?
I have been working with Palantir Foundry for about seven to eight years.
What do I think about the stability of the solution?
My impression of the stability and reliability of Palantir Foundry is positive; it is well managed and robust, and I have not observed breakdowns. Palantir appears to invest significant resources in support at various levels, allowing problems to be escalated effectively, despite occasional bureaucratic delays. The platform itself is well-crafted, and improvements are continually rolled out.
What do I think about the scalability of the solution?
The scalability of Palantir Foundry is immense, suitable for any industry and can expand to encompass various departments and fields that process data or workflows. It has applications across banking, insurance, logistics, supply chain, and engineering. However, I observe a potential drawback concerning market trends; as software becomes more accessible with individuals developing applications independently, there is a risk posed to Palantir Foundry, which combines multiple core functionalities. Although its significance remains vital, emerging competition can impact its customer base. Microsoft and Anthropic are both exploring ways to capture part of Palantir Foundry's market share by offering solutions that simplify tasks that would otherwise require substantial engineering resources.
How are customer service and support?
I perceive the technical support for Palantir Foundry as standard and adequate; I interact primarily with the deployed engineers, though I cannot distinguish which ones are from Palantir or from us.
Which solution did I use previously and why did I switch?
I have not evaluated other options before choosing Palantir Foundry because it had no competition seven to eight years ago. Other solutions such as building your own pipeline through IT were difficult to support and upgrade, often failing to connect to APIs. Once organizations grow larger, specialized units emerge, isolating business users from technical expertise, but Palantir Foundry integrates both, empowering technical data analytics for business users. It is increasingly relevant today with the rise of AI, as those sticking with Excel will struggle compared to specialists who can drive efficiency through data. Palantir Foundry enables a mutual understanding between technical and business perspectives. Competitors such as Tableau, while popular, provided only dashboards without addressing core issues. The same goes for BO and Power BI. Recently, Microsoft Fabric might be a real competitor, but I have no experience with it.
How was the initial setup?
I participated in the initial setup of Palantir Foundry from a business perspective, particularly in setting up the ontology. Connecting to corporate systems was handled by Palantir engineers along with our specialized technical staff, ensuring governance regarding security, safety, and the ingestion process. We created the first ontology and applications to demonstrate Palantir Foundry's capabilities. Additionally, we developed APIs that interfaced with legacy applications for data crunching in Palantir Foundry. We implemented code repositories to ensure robust production-level pipelines. As early adopters, our involvement predates Palantir's public launch, making us a key part of its development journey.
What was our ROI?
I think there are quantifiable efficiency gains from Palantir Foundry, but I cannot specify exact numbers; we have verified that it helps in saving costs.
What other advice do I have?
Navigating Palantir Foundry is straightforward for someone with a technical background, and the provided documentation and learning platform, learn.palantir.com, support various use cases with hands-on experience of new features. The AI assistant, although not perfect, offers assistance for troubleshooting. One of Palantir Foundry's strengths lies in its adherence to industry standards, allowing familiar practices from other applications to be applicable. Its version control, Git usage, and standardized charts provide multiple support avenues for users. I would rate this review a 9 out of 10.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
reviewer2848425
Data teams have delivered faster fraud detection and streamlined healthcare analytics
Reviewed on Jun 03, 2026
Review from a verified AWS customer
What is our primary use case?
I used Palantir Foundry in a different role prior to my current role; however, I do not use it in my current position.
In my previous role, my main use case for Palantir Foundry was data extraction, ingestion, and cleansing for use with machine learning models. A specific use case involved extracting data from multiple public health sources of patients and providing healthcare providers with legal data and logistical data about healthcare providers to analyze which healthcare providers were guilty of fraudulent billing.
Other use cases included ingesting veteran data for different kinds of machine learning using clinical data for veterans; however, the primary use case most recently was fraud detection for prevention of fraud, waste, and abuse in federal health agencies using data from NPI, National Provider Identification, for various providers along with state licensure data for fraud detection.
What is most valuable?
Based on my experience, Palantir Foundry is extremely easy to learn and adjust to since it is primarily a closed system, meaning that all the functions for data ingestion, data cleaning, machine learning, and related tasks can be performed from within the same system. This closed nature made it easier for my junior engineers to use the system for working on the actual work of ingesting the data.
The Ontology was one of the more helpful features alongside ease of use. Palantir Foundry has impacted my organization positively; the general output is comparable to other systems, but the value came more from the speed of querying and building tools to extract and transform the data.
The main benefit was having something that was faster to get to the result set, resulting in less manual work and fairly automated processes. The Ontology system was helpful for our teams.
What needs improvement?
This system needs more powerful tools for the power user; I feel the system is very well designed for the introductory level but could have finer-grained controls for data engineering experts and machine learning experts at the power user level.
For how long have I used the solution?
I have been working in my current field for eight months.
What do I think about the stability of the solution?
There were no issues with stability in our experience; we had no problems with that.
What do I think about the scalability of the solution?
We use Palantir Foundry for fairly large data sets; however, we did not scale to full size since we were building POCs and supporting the federal government in their use cases, and we definitely did not have any issues with scalability on our side.
How are customer service and support?
I cannot comment on customer support since I did not have to reach out to customer support for anything during my use of this system; however, I can comment on training and staff technology partnerships, as the Palantir trainers at the federal government were extremely friendly and very helpful.
Which solution did I use previously and why did I switch?
We did not have a different solution for this particular use, as the federal client was using a different use case and using Palantir Foundry from the outset; ours was a different use case for which Palantir Foundry was the first technology selected.
What about the implementation team?
Overall, I supervised a team that used Palantir Foundry, and the primary benefit here was that there was faster adoption of the technology and faster time to results, primarily with the technology enabling us to have quicker POCs, proof of concepts, using the data system.
What was our ROI?
I would say that there was significant time savings with Palantir Foundry; these may have translated into money savings, but typically we were more focused on the time savings.
What's my experience with pricing, setup cost, and licensing?
I was not typically responsible for pricing, setup cost, or licensing, as I was primarily the leader of the project that implemented the data ingestion, cleaning, and machine learning tasks and led the team; pricing and infrastructure setup would have been handled by other technical leaders in other domains.
Which other solutions did I evaluate?
We did evaluate other options, but Palantir Foundry was the natural fit for this particular use case, so we did not evaluate other options such as Databricks particularly seriously.
What other advice do I have?
My advice to others looking into using Palantir Foundry is to ensure that new users are taught fundamentals of data engineering, data ingestion, and data science before they go into the tool, so they do not have to learn it inside the tool but already come in knowing what to expect; learning from within the tool would create a more restricted learning system, and while the tool is powerful, it should not be used for learning systems but for developing systems. I provided this review a rating of eight.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Kruthik Paduru
Unified ontology has transformed fragmented data and now powers reliable AI-driven decisions
Reviewed on Jun 02, 2026
Review provided by PeerSpot
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Palantir Foundry for three years.
What do I think about the scalability of the solution?
Palantir Foundry is a highly scalable solution.
How are customer service and support?
The customer support is great.
How was the initial setup?
I believe the pricing, setup cost, and licensing could be lower.
What was our ROI?
I have seen money and time saved, but we need to yet see the return on investment.
Which other solutions did I evaluate?
Before choosing Palantir Foundry, I evaluated other options, including Snowflake and Tableau.
What other advice do I have?
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.
Sarwar Mastan
Data workflows have boosted accuracy and automation but access, training, and pricing still need work
Reviewed on Jun 02, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Palantir Foundry is working for a client, Airbus, where I work on the datasets. Currently, we are working on multiple services such as Code Repo, Slate, and Workshop.
We have Contour, where we analyze the data and find the anomalies. We look into the graphs, plot graphs, and investigate how the data is behaving. If the data is not up to date, we investigate that on Contour. In the data lineage part, we backtrack to the point of failure where the failure point is occurring.
I have worked on a project called CMA, wherein we have a Slate application. The dataset is a writeback dataset wherein we get the data as a writeback phonograph sync, and we get the output from the users, process the data, transform the data, and export the data to FTS+.
What is most valuable?
The best features Palantir Foundry offers include clickable outputs that we can easily get, such as the summary of a column straight away. By the click of a button, we can get the column names, and even if there are N number of columns, we can have the column count, row count, and multiple other data specifications in the tool itself. That is a very good thing I have seen, and on the Slate part, we have seen functions, queries, and objects that are very good.
These features help me in my daily work and improve my workflow by automating things and automating our day-to-day job. We use queries, functions, and all. Code Repo also works in this case, and data lineage along with multiple other capabilities are very useful to us.
Palantir Foundry has positively impacted my organization, especially Airbus, as we are dependent on Palantir Foundry for processing the data. We are very much more dependent on the tool.
It has especially improved data accuracy for us, wherein we use humongous data to build applications based on that particular big data, which is quite good since we have a tool Palantir Foundry.
What needs improvement?
Palantir Foundry can be improved by providing training to the people who are working. I feel there is a lot of training that needs to be provided to the developers of Palantir.
The way you handle the products is where improvements are needed, especially the training part. To add features, we need to read the whole documentation, which is time-consuming and wasteful. Providing more training and more videos on YouTube would help.
Regarding Palantir Foundry's AI capabilities, I think its governance and security could be improved. Palantir Assist could have more interactive ways, and I feel the interface where Palantir Assist is provided is not that good.
For how long have I used the solution?
I have been using Palantir Foundry since one and a half years ago.
What do I think about the stability of the solution?
Palantir Foundry's stability is sometimes good and sometimes not; there are blunders and issues.
What do I think about the scalability of the solution?
The scalability of Palantir Foundry is awesome. We can easily process and build applications on humongous data.
How are customer service and support?
Customer support is nice. I would rate customer support an eight, maybe.
Which solution did I use previously and why did I switch?
I have not used any other solutions previously.
What was our ROI?
I have seen a return on investment. Time is saved once we try to build applications on big data where the capacity of handling the data is awesome in Palantir, so I appreciate that.
What's my experience with pricing, setup cost, and licensing?
The pricing is quite high, and there are obstacles in licensing that could be made more flexible.
Which other solutions did I evaluate?
I did evaluate Azure Databricks before choosing Palantir Foundry.
What other advice do I have?
The accuracy and reliability of output from Palantir Foundry depend on the model. The advice I would give others looking into using Palantir Foundry centers around access and pricing, as these are the two things that need improvement. I hope we go with AWS for our cloud provider, as we have not worked with other clouds. I would rate this review a seven overall.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Nicholas Stauffer
Centralized data reporting has transformed analytics efficiency but needs better dataset governance
Reviewed on Jun 02, 2026
Review provided by PeerSpot
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working in my current field for about four years.
What do I think about the stability of the solution?
As far as I know, Palantir Foundry is stable.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
I do not have any experience with pricing, setup cost, or licensing since it all comes with the company that I work with.
Which other solutions did I evaluate?
There were no options before choosing Palantir Foundry; it is pretty much promoted by the company.
What other advice do I have?
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.
Ayush Agrawal
Data pipelines have supported complex analytics and interactive applications across industries
Reviewed on Jun 01, 2026
Review provided by PeerSpot
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Palantir Foundry for around five plus years.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
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.
CarlosPena
Ontology for ticket assignment has transformed how I match developers to client requests
Reviewed on Jun 01, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Palantir Foundry is that I am learning it through all the resources at learn.palantir.com, and I currently have one certificate while working towards two more certificates. Additionally, I am trying to create the ontology of the ticket management for the vertical of my company.
I am using Palantir Foundry to assign the best or the perfect developer for every ticket. When a client creates a ticket, there are many developers who could work on it, but some developers perform better with certain types of tickets, know the client, or have more available time. Currently, this decision is a human one, but I am creating an ontology in Foundry to enable an AI agent to propose the best choice.
I am very excited to create a more comprehensive ontology for my company.
What is most valuable?
In my experience, the best features Palantir Foundry offers include the ontology and the possibility to create a digital twin of your company. It operates in a way that allows you to integrate a vast amount of data from diverse sources and create an ontology that reflects the real aspects of your company, ultimately leading to the creation of applications, which become most impressive when combined with the ontology and data management.
Palantir Foundry has positively impacted my organization by significantly speeding up processes. I am starting with Foundry, and I believe that in the future, I will have KPIs. At this moment, the process of deciding which developer to assign to a ticket is very fast, which makes decision-making clearer, quicker, and more accurate.
What needs improvement?
I have faced some challenges while building the ontology and setting up the AI agent in Foundry. It is somewhat difficult when you start to work with Foundry, but I believe that now I am more comfortable with both the ontology and Foundry.
I think Palantir Foundry is a perfect system; however, it does require more time for learning. It is a very complicated platform, and you need to understand a lot about how to use it and the underlying thinking behind Foundry. I need time and extensive learning processes.
More onboarding processes or training processes would be great.
The issue is not whether you use LLMs like GPT or cloud services like Gemini. All LLMs and AI agents work as commodities within the ontology and applications.
For how long have I used the solution?
I have been using Palantir Foundry for one year, as I started last April of 2024.
What do I think about the stability of the solution?
Palantir Foundry is stable in my experience, with no issues regarding downtime or reliability.
What do I think about the scalability of the solution?
I believe that Palantir Foundry is scalable and can handle growing amounts of data and users without issues. I am confident there is no limit as long as you have the processing, computing, and data capabilities, although I do not know where that limit might be.
How are customer service and support?
I only created a ticket once, and the customer support was incredible. They responded within a few minutes and resolved the ticket immediately with a call.
Which solution did I use previously and why did I switch?
There are other software solutions for automation or data manipulation, such as Make, but none match the depth of concepts or the extensive functionalities of Palantir Foundry.
How was the initial setup?
Creating an ontology requires you to consider the sources of your company's health.
What was our ROI?
It is too early to determine if I have seen a return on investment. I think that in a few months, I will have more insights.
What's my experience with pricing, setup cost, and licensing?
It is very difficult to know the cost of the license. I do not really know the costs that I will incur in the future, as that area remains very unclear. I think it is important to clarify and provide information regarding pricing.
What other advice do I have?
I would advise anyone looking into using Palantir Foundry to learn thoroughly, start with learn.palantir.com, and complete all courses while reading all documentation, as it is very important to grasp the system before starting to work with Foundry.
I rate Palantir Foundry a ten out of ten because I believe it represents the system operation of the internet. It is something truly new, a new technology offering a novel methodology for understanding and utilizing technology within companies. I consider it the best innovation I have seen in the last ten years.
The accuracy and reliability of Palantir Foundry's AI output is great.
reviewer2847549
AI agents have transformed shipyard operations and connect live asset data for higher throughput
Reviewed on May 31, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Palantir Foundry is using AI to connect the shipyard and build AI agents that improve the operational throughput.
A specific example of how I'm using Palantir Foundry to connect the shipyard and improve operations is that the entire asset ecosystem in Drydocks has been revamped by using Palantir Foundry. I have loaded all the IoT data from those assets into Palantir Foundry and am using it to track all the live updates from all the assets to ensure there are no outages and no shortage of materials to maintain these assets.
Regarding my main use case, I am using engineering drawings, which usually require a lot of human intervention and human study to build items when requirements come from clients. I am using state-of-the-art AI models in Palantir Foundry to read these engineering drawings and provide much quicker analysis and insights to the engineers before they review the drawing themselves.
What is most valuable?
The best feature Palantir Foundry offers is the Ontology. Ontology stands out for me because I have a connected shipyard vision. Ontology allows me to truly connect the shipyard, starting from tendering to engineering to production to quality control, with each of those elements properly connected using Ontology.
Palantir Foundry has positively impacted my organization by increasing the operational throughput of the yard, which focuses on building new ships or repairing existing ones. This entire process has been streamlined, though as far as the pilot is concerned, I have not fully gone into production yet.
What needs improvement?
Palantir Foundry can be improved by providing more third-party application support and more support for the Ontology software development kit to develop more native applications rather than just web applications.
A good feature Palantir Foundry can have is allowing different third-party application support. For example, if a company uses an internal ERP system, in addition to having API points connect to it, Palantir Foundry could read standard ERP systems as built-in functionality rather than requiring data to be sent back and forth.
For how long have I used the solution?
I have been in the IT field for about five years and have been working in the Palantir Foundry ecosystem for two years.
What was our ROI?
I have measured the increase in operational throughput during the pilot phase by noting that I increased throughput by approximately 18 percent. This was measured by calculating how much I was able to complete during the pilot and how much time it saved when using AI models to perform the analysis. Extrapolating that to the entire yard, I reached a number around 18 percent.
What other advice do I have?
Regarding Palantir Foundry's AI capabilities, I find that the governance and security are very solid, with all governance and security built in.
Regarding Palantir Foundry's AI capabilities, I find that the accuracy is as good as the data and the prompts, so the accuracy has less to do with Palantir Foundry itself and more to do with how I utilize these AI models.
My advice to others looking into using Palantir Foundry is to pilot it. I would rate this product 9 out of 10.
Muniteja Muniteja
Unified data workflows have simplified front-end development and automated analytics tasks
Reviewed on May 31, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Palantir Foundry involves performing data engineering while also working on the front-end part, utilizing most of the applications in Foundry.
A specific example of how I use Palantir Foundry in my work involves a front-end application where we have several ontology objects that link to it so that users can edit or modify the objects. These functions include several action types, and I primarily handle these functions. When changes are needed in the UI or when logic changes occur, we have particular environments in Marketplace, specifically UAT, dev, and prod. By using the developer console, we make these available functions possible for React to fetch and use them. It is essentially a front-end position that we support using ontology and action types.
I also worked on another use case where I dealt with Code Repos and SAP data, which involved migration from another data platform. I migrated all the data, and we wrote the use cases and completed modifications of the data in the Code Repository using PySpark. After modifying the data, we developed ontology based on that.
What is most valuable?
The best features Palantir Foundry offers include ontology.
What I appreciate most about the ontology feature is that it simplifies the process. We do not need to configure linking objects or linking two different objects. Compared to backend databases or other external platforms where we need to link based on values, Palantir Foundry automatically checks whenever we upload new data, ensuring we do not have any duplicate data or mismatches. This feature helps significantly as we do not need to manually validate it.
Additionally, I find that AI helps us significantly. Palantir Foundry has integrated AI on top of every application we use. Whenever issues arise, we can directly use AI to understand the main issue, eliminating the need to go through every log. AI provides a brief summary of what the issue is, allowing us to directly solve the problem without deciphering logs. AIFD allows us to write our code directly, creating and merging pull requests, which significantly reduces our workload.
Palantir Foundry has positively impacted my organization mainly by consolidating our data management. We do not need multiple accounts for different applications. All our data can reside within Palantir Foundry, and we can access all applications from there without changing the data from one location to another. On top of that, all these applications have integrated AI, which understands the data we possess in other applications and allows us to utilize that information seamlessly.
What needs improvement?
Palantir Foundry could be improved in several areas. Sometimes it takes time to refresh the UI, especially since it is cloud-based. At times, it provides non-specific issues instead of the exact problems we are facing. I believe that is the primary issue to be fixed, along with tracking previous deployments in the developer console. Marketplace DevOps also presents challenges in tracing updates made.
Moreover, there is a limitation in supporting only up to three-dimensional data. My organization needs around four or five-dimensional data, which is why there was consideration to move out of Foundry for that capability. However, my higher-ups prefer not to move data outside of Foundry due to security concerns, which is the limitation we face.
The reason I rate it an eight out of ten is mainly due to the public UI limitations. I did not find options to make the UI public. Anyone using the UI must have a Palantir Foundry account, and there is no way to make the UI or link accessible publicly.
For how long have I used the solution?
I have been using Palantir Foundry for the past four years.
What was our ROI?
In terms of metrics, in my previous project using PySpark, we saved around thirty percent of effort and also thirty percent of the time it took compared to the entire run and build. I later built an automation that reduced manual testing every time a new logic or parameters were included. Automation testing reduced our time by about fifty percent, requiring us only to provide the parameters for validation through a build.
What other advice do I have?
My advice for others looking into using Palantir Foundry is that it is beneficial because we do not have to learn multiple tools. Everything can be handled within a single application, allowing us to use various tools without shifting focus to learn additional applications.
Overall, I view Palantir Foundry as a great tool that combines security with all the necessary features within a single platform. I rate Palantir Foundry an eight out of ten.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Debiprasanna Mishra
Low-code time series insights have accelerated decisions but custom app options remain limited
Reviewed on May 30, 2026
Review provided by PeerSpot
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Palantir Foundry since 2023 across different projects within my current organization.
What do I think about the stability of the solution?
Palantir Foundry is stable based on my experience.
What do I think about the scalability of the solution?
I am not certain about Palantir Foundry's scalability because I am particularly on the data engineering services side.
How are customer service and support?
The customer support for Palantir Foundry was good, but it can be improved.
Which solution did I use previously and why did I switch?
I came across multiple data engineering solutions before using Palantir Foundry.
What was our ROI?
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.
What other advice do I have?
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.