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Databricks vs Palantir Foundry comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Databricks
Ranking in Data Management Platforms (DMP)
5th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
93
Ranking in other categories
Cloud Data Warehouse (6th), Data Science Platforms (1st), Streaming Analytics (1st)
Palantir Foundry
Ranking in Data Management Platforms (DMP)
1st
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
18
Ranking in other categories
Data Integration (12th), IT Operations Analytics (10th), Supply Chain Analytics (1st), Cloud Data Integration (11th), Data Migration Appliances (3rd), Data and Analytics Service Providers (1st)
 

Mindshare comparison

As of March 2026, in the Data Management Platforms (DMP) category, the mindshare of Databricks is 5.8%. The mindshare of Palantir Foundry is 15.4%, down from 32.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Management Platforms (DMP) Mindshare Distribution
ProductMindshare (%)
Palantir Foundry15.4%
Databricks5.8%
Other78.8%
Data Management Platforms (DMP)
 

Featured Reviews

SimonRobinson - PeerSpot reviewer
Governance And Engagement Lead
Improved data governance has enabled sensitive data tracking but cost management still needs work
I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.
BA
Associate Vice President at a insurance company with 10,001+ employees
Unified data workflows have empowered collaborative analytics and streamlined AI development
Regarding points for improvement for Palantir Foundry, I see that they are improving day by day. In the last one to two years, I have seen many improvements compared to the two years that I have worked on Palantir Foundry. There are many things that come up, but a few things are not intuitive enough. Now that we are in this AI phase, Palantir Foundry has created some wrappers around the models, allowing us to create using a no-code application, chatbots, and LLM functions. The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has. There are ways to do that, but it is not that intuitive, which is what I feel.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It helps integrate data science and machine learning capabilities."
"Databricks is a truly essential platform for data engineering needs, and I recommend it to anyone looking to advance in the data engineering field."
"Automation with Databricks is very easy when using the API."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"It is a cost-effective solution."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"I rate Palantir Foundry a ten out of ten."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"Great features available in one tool."
"The virtualization tool is useful."
"I like the data onboarding to Palantir Foundry and ETL creation."
"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 ease of use is my favorite feature. We're able to build different models and projects or combine different projects to build one use case."
"Based on my huge experience with Palantir Foundry, I find that starting from the data connection to the end user application, there is a tool for everyone."
 

Cons

"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."
"A lot of people are required to manage this solution."
"Pricing is one of the things that could be improved."
"There is room for improvement in visualization."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"The solution's visualization and analysis could be improved."
"The frontend capabilities of Palantir Foundry could be improved."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"Cost of this solution is quite high."
"If you want to create new models on specific data sets, computing that is quite costly."
"They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure."
"The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
 

Pricing and Cost Advice

"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."
"There are different versions."
"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."
"We're charged on what the data throughput is and also what the compute time is."
"The solution is affordable."
"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."
"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."
"Palantir Foundry has different pricing models that can be negotiated."
"It's expensive."
"Palantir Foundry is an expensive solution."
"The solution’s pricing is high."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
9%
Computer Software Company
8%
Healthcare Company
6%
Manufacturing Company
14%
Financial Services Firm
9%
Government
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business27
Midsize Enterprise12
Large Enterprise56
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise5
Large Enterprise9
 

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
What needs improvement with Palantir Foundry?
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 out...
What is your primary use case for Palantir Foundry?
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 hig...
What advice do you have for others considering Palantir Foundry?
Palantir Foundry is an excellent product for data engineering. On a scale of one to 10, I would rate Palantir Foundry a 9.
 

Comparisons

 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
No data available
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Merck KGaA, Airbus, Ferrari,United States Intelligence Community, United States Department of Defense
Find out what your peers are saying about Databricks vs. Palantir Foundry and other solutions. Updated: March 2026.
884,797 professionals have used our research since 2012.