Palantir Platform

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.

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.

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.

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

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

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

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

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

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

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

More onboarding processes or training processes would be great.

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

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

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

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

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

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

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

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

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

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

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

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