Fabric Data vs Palantir Foundry comparison

Fabric Data and Palantir are both solutions in the Data and Analytics Service Providers category. Fabric Data is ranked #4 with an average rating of 8.0, while Palantir is ranked #1 with an average rating of 8.2. Fabric Data holds a 0.7% mindshare in DaAS, compared to Palantir’s 7.6% mindshare. Additionally, 95% of Fabric Data users are willing to recommend the solution, compared to 93% of Palantir users who would recommend it.

Fabric Data

Read 21 Fabric Data reviews

538 Views
527 Comparison Views

95% willing to recommend

Palantir Foundry

Read 60 Palantir Foundry reviews

14,283 Views
2,000 Comparison Views

93% willing to recommend

Fabric Data

Palantir Foundry

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Fabric Data and Palantir Foundry based on real PeerSpot user reviews.

Find out in this report how the two Data and Analytics Service Providers solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Fabric Data vs. Palantir Foundry Report (Updated: June 2026).

Buyer's Guide

Fabric Data vs. Palantir Foundry

June 2026

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Helped 903,147 peers since 2012

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

Fabric Data

Ranking in Data and Analytics Service Providers

4th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Palantir Foundry

Ranking in Data and Analytics Service Providers

1st

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Integration (3rd), IT Operations Analytics (4th), Supply Chain Analytics (1st), Cloud Data Integration (4th), Data Migration Appliances (2nd), Data Management Platforms (DMP) (1st)

Mindshare comparison

As of July 2026, in the Data and Analytics Service Providers category, the mindshare of Fabric Data is 0.7%, up from 0.6% compared to the previous year. The mindshare of Palantir Foundry is 7.6%, down from 12.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data and Analytics Service Providers Mindshare Distribution
Product	Mindshare (%)
Palantir Foundry	7.6%
Fabric Data	0.7%
Other	91.7%

Data and Analytics Service Providers

Featured Reviews

Pranav Vighe

Data Engineer at IRT Dogotal ANlytics

Automation of complex data workflows has reduced processing time and improves project delivery

The best features Fabric Data offers include Fabric Data Shortcut as the main feature, and also the integration of all the components like ingestion, transformation notebooks, and the deployment pipeline for CI/CD, which are game-changing. The visualization features are also great, and the features Fabric Data offers are different. The feature I find myself using the most is the time travel feature because I mainly work with data transformation. Whenever bad updates happen, I use the time travel feature the most. There is a high concurrency feature that can be applied in pipelines; we just need to add the high concurrency tag, and the pipeline will not start a new cluster each time the notebook runs. Fabric Data will use the same cluster for the notebook run, and this feature is a game-changer. Fabric Data positively impacts my organization by bringing us more projects and work to do and also reduces the time significantly. Nearly 20 to 30 hours per week were reduced by using Fabric Data, and it is also very cost-optimized.

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reviewer2846265

PALANTIR DATA ENGINEER at a healthcare company with 10,001+ employees

Unified healthcare pipelines have improved data trust and accelerated operational decisions

One challenge regarding how Palantir Foundry can be improved is the learning curve. Foundry has a very broad ecosystem with Ontology, Pipeline Builder, Code Repositories, and AI integrations. For new engineers or business users onboarding, it can take time, especially if they are coming from more traditional data platforms. Better documentation, simplified onboarding paths, and more beginner-friendly examples would help accelerate adoption. Another area is debugging complexity. While lineage and monitoring are strong features, troubleshooting deeply interconnected pipelines can still become difficult in a large enterprise environment. Sometimes error logs and pipeline failure messages could be more descriptive or developer-friendly, especially for distributed PySpark jobs. Another pain point is customization limitations in certain UI-driven components. While low-code tools are great for rapid development, highly customized workflows sometimes still require engineering workarounds or deeper technical implementation. The platform is extremely capable, but improvements around usability, debugging experience, DevOps flexibility, and ecosystem openness would make it even more effective for enterprise engineering teams.

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Quotes from Members

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

Pros

"I enjoy working with the pipelines since they provide a full end-to-end use case for me to take the data and report everything in one place instead of going back and forth with databases and engineers, allowing me to feel as a data scientist, data engineer, and business analyst in one location, giving me full authority to control everything."

"Fabric Data has positively impacted my organization by decreasing the storage-level cost, and we now have different teams, including a data analytics team and a data engineering team, all on one platform, allowing us to directly check the data analytics part."

"Fabric Data offers several standout features that are best in class."

"The unified workspace is the biggest advantage I experienced while building those data pipelines and working with OneLake storage."

"Fabric Data has positively impacted our organization as it has been our go-to tool for data integration with the help of Microsoft services."

"Fabric Data has impacted my organization positively because collaboration has been better and deployments have been faster."

"I reduced around 60% latency using Fabric Data and created one single source of truth on the Microsoft ecosystem when working with a pharmaceutical industry."

"Fabric Data has allowed us to change that and put the entire solution in one package and one environment, and that also makes things much more stable."

More Fabric Data pros

"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."

"Palantir Foundry is not just a data platform; it actually connects data engineering, analytics, operations, and decision-making all into one ecosystem."

"From my experience with Palantir Foundry, the most useful feature is its no-code, low-code environment, allowing me to develop applications without extensive coding, unlike my past as an Oracle developer where I had to write code for various functions."

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

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

"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."

"Palantir Foundry has positively impacted my organization by providing good support from Palantir teams, facilitating the development of many new solutions, building our UI and web applications, and significantly enhancing our productivity."

"This product has all the various components for getting data, transforming it and visually creating the dashboards without the need to integrate things and no need to check the compatibility."

More Palantir Foundry pros

Cons

"I felt some features, particularly around the Dataflow Gen2 error handling and pipeline monitoring, lacked clear documentation at the time of my study."

"I cannot say that the analytics and reporting capabilities of Fabric Data are good enough because it only provides compatibility with Microsoft Power BI."

"I have observed some limitations with Fabric Data, especially when it comes to bringing data from private networks."

"I feel that the Copilot in Power BI is very weak; for example, if you power it with the cloud, it is much more powerful than the Copilot in Power BI, which I believe could offer much better insights since it is a Microsoft tool."

"Customer support receives a rating of six out of ten because they themselves are trying to figure out what is new and what the issue is."

"Fabric Data can be improved because it tends to be run by Fabric Capacity, which is basically the compute cycles, and it is not very clear on how and what that is going to be used."

"Fabric Data could be improved in the future by increasing the size capability from terabyte to petabyte for deeper integration."

"I believe Excel sheets have some issues when creating a data frame; however, JSON data works fine for Fabric Data. When using an Excel sheet, we need some extra libraries, and that feature would be useful because most e-commerce sites store data in Excel."

More Fabric Data cons

"The widgets are pretty limited."

"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 requires a lot of manual work and is very time-consuming to get to a functional point."

"I believe that the AI or agent needs improvement because sometimes we face difficulties when looking for solutions, and when we ask the agent, AIP, it does not understand our queries and occasionally provides wrong solutions."

"Palantir Foundry's stability is sometimes good and sometimes not; there are blunders and issues."

"For example, exporting data from Palantir Foundry is very difficult and has many limitations."

"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."

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

More Palantir Foundry cons

Pricing and Cost Advice

Information not available

"Palantir Foundry is an expensive solution."

"Palantir Foundry has different pricing models that can be negotiated."

"The solution’s pricing is high."

"It's expensive."

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Top Industries

By visitors reading reviews

Manufacturing Company

17%

Financial Services Firm

16%

University

14%

Healthcare Company

Manufacturing Company

14%

Financial Services Firm

10%

Government

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	2
Large Enterprise	16

By reviewers
Company Size	Count
Small Business	11
Midsize Enterprise	7
Large Enterprise	50

Questions from the Community

What is your experience regarding pricing and costs for Fabric Data?

My experience with pricing, setup cost, and licensing is that pricing and those aspects are a different matter. It is not a concern for me because it matters to the client. Compared to other option...

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What needs improvement with Fabric Data?

One area Fabric Data can be improved is the semantic model refresh. Though it says it is a direct link, the refresh times of the semantic model sometimes need explicit refresh. This takes a bit of ...

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What is your primary use case for Fabric Data?

My main use case for Fabric Data is building data solutions for one of the retail firms in the US. I use Fabric to process source data, perform data processing, and provide analytical reports for e...

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What needs improvement with Palantir Foundry?

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

See all answers

What is your primary use case for Palantir Foundry?

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

See all answers

What advice do you have for others considering Palantir Foundry?

I would rate this product a 9 out of 10.

See all answers

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Product Reports

Buyer's Guide

Fabric Data

July 2026

Download Fabric Data product report

Buyer's Guide

Palantir Foundry

June 2026

Download Palantir Foundry product report

Overview

Fabric Data delivers powerful data management to streamline analytics, enhance data accessibility, and improve business decision-making processes within enterprises.

Fabric Data is designed to address complex data environments, offering a comprehensive approach to ensuring data integrity and consistency. Targeted towards data-driven organizations, it simplifies data management and integration, making data easy to access and utilize for advanced analytics and reporting. By facilitating seamless scalability, it supports growth and evolving data requirements efficiently.

What are the most important features of Fabric Data?

Data Integration: Simplifies the merging of multiple data sources into a single cohesive system.
Scalability: Adaptable framework allows for effortless expansion as needs grow.
Data Security: Implements robust measures to protect sensitive information.
Real-time Analytics: Offers immediate insights for faster decision-making.

What benefits or ROI should users look for in reviews?

Improved Efficiency: Streamlines data processes, reducing time and effort.
Cost Savings: Lowers operational costs by automating tasks and enhancing productivity.
Enhanced Decision-making: Provides reliable data insights to inform strategy.
Increased Agility: Respond quickly to changes with flexible data management.

Fabric Data can be implemented in sectors such as finance, healthcare, and retail, where it facilitates data-driven strategies, enhances customer experiences, and optimizes operational efficiencies. In finance, it supports risk management and regulatory compliance. In healthcare, it contributes to patient data management and care personalization.

Fabric Data

Palantir Foundry offers intuitive data management and application development, prioritizing accessibility through low-code/no-code tools, enabling users to integrate, analyze, and collaborate efficiently.

Palantir Foundry centers on user accessibility, data governance, and real-time capabilities, streamlining processes with low-code/no-code development. It supports comprehensive data analysis and integration, enhanced by digital twin features that align virtual and physical interactions. Despite high costs and performance challenges with large datasets, it remains a prime choice for sectors needing structured and unstructured data integration. Key areas include robust data security, lineage tracking, and predictive analytics, promoted through a unified management platform adaptable to diverse needs.

What are the key features of Palantir Foundry?

User-friendly interface enabling easy data governance and automation.
Real-time integration with strong data analysis and collaboration tools.
Low-code/no-code development to widen accessibility.
Digital twin capabilities for bi-directional process integration.
Strong data lineage, predictive analytics, and visualization tools.
Flexibility and customization options.

What benefits and ROI could reviews highlight?

Improved decision-making through enhanced data visibility.
Increased efficiency with automated data processes.
Broader accessibility to data insights via low-code options.
Enhanced collaboration and integration between departments.
Potential cost savings through streamlined operations.

In manufacturing, Palantir Foundry aids in engineering pipeline models and semantic frameworks, while utilities utilize its analytics to enhance service delivery. Insurance firms leverage its capability to assess and predict customer behavior. Throughout these industries, Foundry integrates across cloud environments, bridging structured and unstructured data from various sources.

Palantir

Sample Customers

Information Not Available

Merck KGaA, Airbus, Ferrari,United States Intelligence Community, United States Department of Defense

Buyer's Guide

Fabric Data vs. Palantir Foundry

June 2026

Free Report: Fabric Data vs. Palantir Foundry

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