Palantir Foundry vs Upsolver comparison

Palantir and Upsolver are both solutions in the Data Integration category. Palantir is ranked #3 with an average rating of 8.2, while Upsolver is ranked #39 with an average rating of 8.5. Palantir holds a 2.1% mindshare in DI, compared to Upsolver’s 0.7% mindshare. Additionally, 93% of Palantir users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Palantir Foundry

Read 62 Palantir Foundry reviews

15,651 Views
6,573 Comparison Views

93% willing to recommend

Upsolver

Read 4 Upsolver reviews

2,013 Views
1,610 Comparison Views

100% willing to recommend

Palantir Foundry

Upsolver

Comparison Buyer's Guide

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Executive Summary

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

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

To learn more, read our detailed Palantir Foundry vs. Upsolver Report (Updated: August 2026).

Buyer's Guide

Palantir Foundry vs. Upsolver

August 2026

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Helped 909,725 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

Palantir Foundry

Ranking in Data Integration

3rd

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

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

Upsolver

Ranking in Data Integration

39th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Streaming Analytics (21st)

Mindshare comparison

As of August 2026, in the Data Integration category, the mindshare of Palantir Foundry is 2.1%, down from 3.3% compared to the previous year. The mindshare of Upsolver is 0.7%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Palantir Foundry	2.1%
Upsolver	0.7%
Other	97.2%

Data Integration

Featured Reviews

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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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

"In my experience, the best features Palantir Foundry offers include the ontology and the possibility to create a digital twin of your company."

"Palantir Foundry is the future of development because it embeds multiple AI models, and the AI enabling feature is very fast and easy."

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

"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."

"Palantir Foundry is one of the most leading and comfortable tools to develop end-to-end data solutions."

"In terms of improvements, it helped us improve our data migration timelines by approximately 60 percent and improved the data accuracy and addressed the issues upfront by approximately 85 percent."

"Palantir Foundry gives me a unified view of AI and my engineering space while I have been doing a lot of data engineering in a couple of technologies, bringing that data together and stitching them and putting together AI, enabling AI use cases, which makes me see a holistic view of data coming from various platforms."

"I appreciate multiple aspects of Palantir Foundry, starting with the clean architecture and clean UI, and I really value how easily I can create and test Python and PySpark scripts, trace data lineage to debug issues, monitor daily pipelines and health checks, and quickly build very interactive Workshop applications, all supported by a clean and informative Resource Management UI that helps track costs and data usage."

More Palantir Foundry pros

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

Cons

"Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive."

"The theme is very monotonous and should be improved."

"I cannot advise someone to use Palantir Foundry due to cost efficiency and the complexity it introduces in handling large amounts of data."

"Sometimes it takes time to refresh the UI, especially since it is cloud-based."

"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."

"I think much of the work within Palantir Foundry is still manual, so I might want to write certain automations or develop conversational interfaces rapidly."

"The workflow could be improved. Although it works rather seamlessly, the workflow is too complicated sometimes."

"Palantir Foundry is very accurate, but I would have doubts about its reliability because there have been instances in my current job where we experience untimely application downtime, which has impacted the business significantly, so I think reliability needs improvement."

More Palantir Foundry cons

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"There is room for improvement in query tuning."

Pricing and Cost Advice

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

"Palantir Foundry is an expensive solution."

"It's expensive."

"The solution’s pricing is high."

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Manufacturing Company

14%

Financial Services Firm

10%

Government

Healthcare Company

Real Estate/Law Firm

14%

Manufacturing Company

14%

Retailer

12%

Construction Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

Questions from the Community

What needs improvement with Palantir Foundry?

The Workshop application could be improved because it is not very customizable, but it is still very strong. We also have the React OSK apps, but it does not allow the inbuilt applications such as ...

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What is your primary use case for Palantir Foundry?

My main use case for Palantir Foundry is to solve business problems, such as in healthcare. I also worked on a project for a law firm where thousands of PDFs were coming in, and we needed to check ...

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What advice do you have for others considering Palantir Foundry?

I believe they should get started by completing all the free certificates, then they could apply to the paid certificates to get a master of Palantir Foundry, solve some real use cases, do examples...

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What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

See all answers

What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

See all answers

What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

See all answers

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Product Reports

Buyer's Guide

Palantir Foundry

August 2026

Download Palantir Foundry product report

Buyer's Guide

Data Integration

August 2026

Download Upsolver product report

Overview

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

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

Upsolver

Sample Customers

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

Information Not Available

Buyer's Guide

Palantir Foundry vs. Upsolver

August 2026

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