Palantir Foundry vs StreamSets comparison

Palantir and IBM are both solutions in the Data Integration category. Palantir is ranked #12 with an average rating of 8.0, while IBM is ranked #21 with an average rating of 9.0. Palantir holds a 2.1% mindshare in DI, compared to IBM’s 1.2% mindshare. Additionally, 88% of Palantir users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Palantir Foundry

Read 17 Palantir Foundry reviews

8,904 Views
3,829 Comparison Views

88% willing to recommend

StreamSets

Read 21 StreamSets reviews

2,863 Views
2,090 Comparison Views

100% willing to recommend

Palantir Foundry

StreamSets

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 19, 2024

StreamSets and Palantir Foundry compete in the data integration and pipeline management category. StreamSets has the upper hand due to its ease of use and configuration flexibility without extensive coding skill requirements.

Features: StreamSets offers ease of configuration with numerous connectors and a graphical interface, allowing the creation of data pipelines without coding. It includes features like data drift management and integration with Hadoop and Snowflake. Palantir Foundry provides integrated data visualization, comprehensive data lineage tracking, and robust real-time integration capabilities, making it a powerful end-to-end data solution.

Room for Improvement: StreamSets may improve its integration with non-Java platforms and enhance its user interface and logging capabilities. Addressing its complexity and refining the security model could benefit users. Palantir Foundry is often critiqued for its high expense and steep learning curve, with a need for better documentation and user experience improvements for non-technical users.

Ease of Deployment and Customer Service: StreamSets supports flexible deployments across various environments, with generally positive customer service but occasional variability in response times. Palantir Foundry, being largely cloud-based, offers a streamlined deployment process but receives mixed reviews on support, often relying on community resources.

Pricing and ROI: StreamSets offers diverse pricing models, including a free version, with significant ROI through reduced development time. Its extensive features can be expensive for smaller businesses. Palantir Foundry's high cost is offset by its integrated capabilities eliminating the need for multiple tools, although cost remains a concern for small enterprises seeking quick ROI.

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

Buyer's Guide

Palantir Foundry vs. StreamSets

January 2026

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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

12th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

IT Operations Analytics (10th), Supply Chain Analytics (1st), Cloud Data Integration (11th), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)

StreamSets

Ranking in Data Integration

21st

Average Rating

8.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Data Integration category, the mindshare of Palantir Foundry is 2.1%, down from 2.5% compared to the previous year. The mindshare of StreamSets is 1.2%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Market Share Distribution
Product	Market Share (%)
Palantir Foundry	2.1%
StreamSets	1.2%
Other	96.7%

Data Integration

Featured Reviews

SRINIVAS RATHOD

Architect at L&T Technology Services

Finds security and customization features impressive, although cost concerns persist

My experience with Palantir Foundry and Azure has been good. Palantir Foundry is costly, but Azure is open, which allows for easier experimentation. Being a closed product, Palantir Foundry is difficult to practice offline unless we have an enterprise edition. However, it is very secure compared to other platforms. Palantir Foundry's best features include security, built-in features, low-code, no-code platform, and ease of use. The collaborative workspaces within Palantir Foundry contribute to team efficiency and project outcomes through seamless operation. The ease of customization is particularly notable. I have worked with the data lineage feature in Palantir Foundry, which comes by default. We simply need to tick the checkbox and make necessary configuration changes within the system itself. We do not need to procure another lineage platform as Palantir Foundry has its own built-in features for data lineage, data governance, and data security. The lineage feature helps enhance our data management practices by allowing us to understand the origin of data, track all activities happening on the data, identify users and consumers, and monitor how it flows across the system. This makes it easier to generate reports based on the lineage database. 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. Using the AIP library within Palantir Foundry helps us develop quick resolutions for predictive models and analytics.

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SrinivasanSankar

Enterprise Solutions Architect at a energy/utilities company with 1,001-5,000 employees

Enables effective batch loading with visual interface and enterprise support

One issue I observed with StreamSets is that the memory runs out quickly when processing large volumes of data. Because of this memory issue, we have to upgrade our EC2 boxes in the Amazon AWS infrastructure. I had to switch to a new EC2 box, even though the processor was not fully utilized. It would be beneficial if StreamSets addressed any potential memory leak issues to prevent unnecessary upgrades. Additionally, it would be a great enhancement if StreamSets could produce a lineage graph to visualize how the data has passed through the system.

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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

"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."

"Great features available in one tool."

"It's scalable."

"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."

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

"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."

"Live video sessions enhance the available documentation and allow you to ask questions directly."

More Palantir Foundry pros

"Also, the intuitive canvas for designing all the streams in the pipeline, along with the simplicity of the entire product are very big pluses for me. The software is very simple and straightforward. That is something that is needed right now."

"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"

"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."

"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."

"What I love the most is that StreamSets is very light. It's a containerized application. It's easy to use with Docker. If you are a large organization, it's very easy to use Kubernetes."

"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."

"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."

"The entire user interface is very simple and the simplicity of creating pipelines is something that I like very much about it. The design experience is very smooth."

More StreamSets pros

Cons

"The frontend capabilities of Palantir Foundry could be improved."

"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 solution’s data security could be improved."

"The solution's visualization and analysis could be improved."

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

"The workflow could be improved."

"Cost of this solution is quite high."

"The major hindrance with Palantir Foundry is that being a very closed product, the cost optimization and costing are not exposed to the end users."

More Palantir Foundry cons

"The software is very good overall. Areas for improvement are the error logging and the version history. I would like to see better, more detailed error logging information."

"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."

"One area for improvement could be the cloud storage server speed, as we have faced some latency issues here and there."

"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."

"StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target."

"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."

"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."

"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."

More StreamSets cons

Pricing and Cost Advice

"It's expensive."

"Palantir Foundry is an expensive solution."

"The solution’s pricing is high."

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

"There are two editions, Professional and Enterprise, and there is a free trial. We're using the Professional edition and it is competitively priced."

"The licensing is expensive, and there are other costs involved too. I know from using the software that you have to buy new features whenever there are new updates, which I don't really like. But initially, it was very good."

"StreamSets is an expensive solution."

"We use the free version. It's great for a public, free release. Our stance is that the paid support model is too expensive to get into. They should honestly reevaluate that."

"I believe the pricing is not equitable."

"The pricing is affordable for any business."

"StreamSets Data Collector is open source. One can utilize the StreamSets Data Collector, but the Control Hub is the main repository where all the jobs are present. Everything happens in Control Hub."

"The overall cost is very flexible so it is not a burden for our organization... However, the cost should be improved. For small and mid-size organizations it might be a challenge."

More StreamSets pricing and cost advice

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Top Industries

By visitors reading reviews

Manufacturing Company

14%

Financial Services Firm

10%

Government

Computer Software Company

Insurance Company

Financial Services Firm

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	5
Large Enterprise	8

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	11

Questions from the Community

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...

See all answers

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...

See all answers

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.

See all answers

What do you like most about StreamSets?

The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customiz...

See all answers

What needs improvement with StreamSets?

See all answers

What is your primary use case for StreamSets?

We are using StreamSets for batch loading.

See all answers

Comparisons

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Compared 8% of the time

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Compared 6% of the time

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Compared 3% of the time

MuleSoft Anypoint Platform vs Palantir Foundry

Compared 3% of the time

Collibra Platform vs Palantir Foundry

Compared 3% of the time

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Confluent vs StreamSets

Compared 7% of the time

webMethods.io vs StreamSets

Compared 6% of the time

Matillion Data Productivity Cloud vs StreamSets

Compared 6% of the time

Informatica PowerCenter vs StreamSets

Compared 5% of the time

Qlik Talend Cloud vs StreamSets

Compared 3% of the time

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Product Reports

Buyer's Guide

Palantir Foundry

January 2026

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Buyer's Guide

StreamSets

January 2026

Download StreamSets product report

Overview

Palantir Foundry is an enterprise data management platform offering comprehensive tooling for working with big data. Because it is an operating system made for modern enterprises, it is highly available and a continuously updated platform.

Palantir Foundry is a fully managed SaaS platform that spans from cloud hosting and data integration to flexible analytics, visualization, model-building, operational decision-making, and decision capture. It equips technical and non-technical users to make data-driven operational decisions.

Palantir Foundry includes tools to integrate data of any scale, format, or structure, and also has granular, flexible access controls for individual datasets. In addition, it has an open, modular architecture with multiple RESTful APIs, it has native applications for developing machine learning and artificial intelligence, it provides sophisticated data science applications for users of all technical abilities, and much more.

Palantir Foundry Features

The most valuable Palantir Foundry features include:

Security, flexibility, interoperability, easy deployment, built-in role classification, purpose-based access controls, interoperable architecture, model integration, AI modeling tools, ontology, custom workflows, team-specific applications, self-serve analytics, lineage system, operational application building, 200+ data connectors, data versioning, change management framework, sand decision orchestration, and custom dashboard and report building tools.

With Palantir Foundry You Can:

Accelerate development & operations
Collaborate globally
Save time on security
Reduce costs

Palantir Foundry Benefits

Some of the many Palantir Foundry benefits include:

Modular: Because Palantir Foundry is modular, it can integrate with existing infrastructure.
Granular governance: Robust data governance controls help ensure seamless compliance, auditability, and security.
Secure data sharing: The security infrastructure allows you to share data securely, within organizations and with chosen partners.
Rapid integration: Palantir Foundry can rapidly integrate models and data from data platforms into an operational ontology, and can also fuel existing data platforms.
Stand-alone deployment: Palantir Foundry can be deployed standalone and can provide all necessary capabilities for data connection, data integration, and model building.
Prevent lock-in: The data and model integration suites are designed to prevent lock-in, and assume that tools will be interoperating with Palantir Foundry as an organization grows.
No SQL necessary: Users do not have to use SQL queries or employ engineers to write strings in order to search petabytes of data. With Palantir Foundry, natural language is used to query data and results are returned in real-time.
Excellent data integration: Palantir Foundry provides secure, scalable, and resilient integration of all data sources.
Availability: The Palantir platform uses DNS routing, gateways and elastic load balancing to ensure availability. Dedicated accounts and virtual resources are used on a per customer basis.
Multiple metrics types: The Palantir platform provides hundreds of metrics. Some include average session length, drop-off rate, number of logins per user, number of accounts, and search speed.

Reviews from Real Users

PeerSpot users like Palantir Foundry because it has many advantages:

“It is user-friendly, good automation, and allows you to do a better job of data governance.” - Associate, Inhouse Consulting at a pharma/biotech company

“Works seamlessly with good end-to-end capabilities and the capability to scale.” - Wallace H., Sr. Director at a tech services company

Palantir

StreamSets is a data integration platform that enables organizations to efficiently move and process data across various systems. It offers a user-friendly interface for designing, deploying, and managing data pipelines, allowing users to easily connect to various data sources and destinations. StreamSets also provides real-time monitoring and alerting capabilities, ensuring that data is flowing smoothly and any issues are quickly addressed.

IBM

Sample Customers

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

Availity, BT Group, Humana, Deluxe, GSK, RingCentral, IBM, Shell, SamTrans, State of Ohio, TalentFulfilled, TechBridge

Buyer's Guide

Palantir Foundry vs. StreamSets

January 2026

Free Report: Palantir Foundry vs. StreamSets

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