Elastic Search vs Palantir Foundry comparison

Elastic and Palantir are both solutions in the Cloud Data Integration category. Elastic is ranked #5 with an average rating of 8.4, while Palantir is ranked #12 with an average rating of 8.0. Elastic holds a 1.7% mindshare in CDI, compared to Palantir’s 4.3% mindshare. Additionally, 97% of Elastic users are willing to recommend the solution, compared to 88% of Palantir users who would recommend it.

Elastic Search

Read 93 Elastic Search reviews

9,382 Views
1,032 Comparison Views

97% willing to recommend

Palantir Foundry

Read 18 Palantir Foundry reviews

10,443 Views
2,089 Comparison Views

88% willing to recommend

Elastic Search

Palantir Foundry

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Executive SummaryUpdated on Dec 3, 2024

Elastic Search and Palantir Foundry are contenders in the data analytics and integration space. Elastic Search is favored for its affordability and flexibility due to open-source availability, while Palantir Foundry's comprehensive platform offers enhanced data integration and security capabilities.

Features: Elastic Search offers scalability, ease of integration without specific data format adjustments, and real-time analytics capabilities enhanced with Kibana. This makes it ideal for log monitoring and quick incident response. On the other hand, Palantir Foundry provides end-to-end data integration and visualization, eliminating the need for multiple tools. Its platform facilitates seamless solution deployment with a strong focus on security and advanced data handling, suited for healthcare and engineering sectors.

Room for Improvement: Elastic Search could benefit from improved security in its open-source model and more intuitive alerting features. Complexity in machine learning functionalities and index management inefficiencies are noted drawbacks. Palantir Foundry's improvements could include cost reductions and enhanced documentation. Limitations in application development and high computational costs also indicate areas for development, especially to improve accessibility for non-technical users.

Ease of Deployment and Customer Service: Elastic Search allows deployment flexibility with various configurations supported by a robust community, though direct support may be costly and slow. Versatility in deployment environments emphasizes its adaptability. Palantir Foundry focuses on seamless cloud deployments and consistent technical support, yet users report the need for faster service responses. Its higher deployment costs highlight a trade-off for smoother integration.

Pricing and ROI: Elastic Search is generally cost-effective with open-source advantages, although premium features can add up. It offers favorable ROI through cost savings in deployment and operational efficiencies. Palantir Foundry presents a higher pricing model but aims to offset this with reduced labor costs due to platform unification. While Elastic Search attracts businesses prioritizing cost-effectiveness, Foundry appeals to those valuing sophisticated data management at greater expense.

To learn more, read our detailed Elastic Search vs. Palantir Foundry Report (Updated: April 2026).

Buyer's Guide

Elastic Search vs. Palantir Foundry

April 2026

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Helped 886,906 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

Elastic Search

Ranking in Cloud Data Integration

5th

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Indexing and Search (1st), Search as a Service (1st), Vector Databases (3rd)

Palantir Foundry

Ranking in Cloud Data Integration

12th

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Integration (10th), IT Operations Analytics (8th), Supply Chain Analytics (1st), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)

Mindshare comparison

As of April 2026, in the Cloud Data Integration category, the mindshare of Elastic Search is 1.7%, down from 1.8% compared to the previous year. The mindshare of Palantir Foundry is 4.3%, down from 4.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Integration Mindshare Distribution
Product	Mindshare (%)
Elastic Search	1.7%
Palantir Foundry	4.3%
Other	94.0%

Cloud Data Integration

Featured Reviews

Anurag Pal

Technical Lead at YatriPay Limited

Search and aggregations have transformed how I manage and visualize complex real estate data

Elastic Search consumes lots of memory. You have to provide the heap size a lot if you want the best out of it. The major problem is when a company wants to use Elastic Search but it is at a startup stage. At a startup stage, there is a lot of funds to consider. However, their use case is that they have to use a pretty significant amount of data. For that, it is very expensive. For example, if you take OLTP-based databases in the current scenario, such as ClickHouse or Iceberg, you can do it on 4GB RAM also. Elastic Search is for analytical records. You have to do the analytics on it. According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive. Another thing is that there is an open source available for that, such as ClickHouse. Around 2014 and 2012, there was only one competitor at that time, which was Solr. But now, not only is Solr there, but you can take ClickHouse and you have Iceberg also. How are we going to compete with them? There is also a fork of Elastic Search that is OpenSearch. As far as I have seen in lots of articles I am reading, users are using it as the ELK stack for logs and analyzing logs. That is not the exact use case. It can do more than that if used correctly. But as it involves lots of cost, people are shifting from Elastic Search to other sources. When I am talking about pricing, it is not only the server pricing. It is the amount of memory it is using. The pricing is basically the heap Java, which is taking memory. That is the major problem happening here. If we have to run an MVP, a client comes to me and says, "Anurag, we need to do a proof of concept. Can we do it if I can pay a 4GB or 16GB expense?" How can I suggest to them that a minimum of 16GB is needed for Elastic Search so that your proof of concept will be proved? In that case, what I have to suggest from the beginning is to go with Cassandra or at the initial stage, go with PostgreSQL. The problem is the memory it is taking. That is the only thing.

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Sai Arun Bandhakavi

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.

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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 security portion of Elasticsearch is particularly beneficial, allowing me to view and analyze security alerts."

"The most valuable features are the detection and correlation features."

"We chose Elasticsearch because we could build a model in a short amount of time, allowing us to build a whole setup in one month and get 93% accuracy, with complex AI-based features built in a shorter span and with high accuracy that wasn't possible with other search enterprise vendors we used."

"There's lots of processing power. You can actually just add machines to get more performance if you need to. It's pretty flexible and very easy to add another log. It's not like 'oh, no, it's going to be so much extra data'. That's not a problem for the machine. It can handle it."

"I like how it allows us to connect to Kafka and get this data in a document format very easily. Elasticsearch is very fast when you do text-based searches of documents. That area is very good, and the search is very good."

"The solution is very good with no issues or glitches."

"Elastic Search has impacted my organization positively as we use it for logging and APM."

"This is a very rich product, and it's got a very wide functionality, and a wide range of functionalities which I don't see in the other products, especially not in the cheaper ones."

More Elastic Search pros

"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."

"Foundry's data visualization is fantastic."

"The interface is really user-friendly."

"The virtualization tool is useful."

"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."

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

"The solution offers very good end-to-end capabilities."

More Palantir Foundry pros

Cons

"Scalability and ROI are the areas they have to improve."

"There are potential improvements based on our client feedback, like unifying the licensing cost structure."

"Elasticsearch should have simpler commands for window filtering."

"They could simplify the Filebeat and Logstash configuration piece. There are a lot of manual steps on the operating system."

"The different applications need to be individually deployed."

"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."

"Improving machine learning capabilities would be beneficial."

"Elastic Enterprise Search could improve the report templates."

More Elastic Search cons

"If you want to create new models on specific data sets, computing that is quite costly."

"There are some issues with scalability because when we are using a really large dataset, the system is rather slow."

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

"The solution’s data security could be improved."

"The one area where improvement could be made is the cost of the solution which is quite expensive."

"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."

"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 could use more online documentation for new users."

More Palantir Foundry cons

Pricing and Cost Advice

"I rate Elastic Search's pricing an eight out of ten."

"There is a free version, and there is also a hosted version for which you have to pay. We're currently using the free version. If things go well, we might go for the paid version."

"We are using the free open-sourced version of this solution."

"The pricing structure depends on the scalability steps."

"Elastic Search is open-source, but you need to pay for support, which is expensive."

"Although the ELK Elasticsearch software is open-source, we buy the hardware."

"The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."

"We are using the open-sourced version."

More Elastic Search 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."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

Manufacturing Company

Retailer

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	38
Midsize Enterprise	11
Large Enterprise	46

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	5
Large Enterprise	9

Questions from the Community

What is your experience regarding pricing and costs for ELK Elasticsearch?

When it comes to pricing, I think we had to pay AWS approximately 1,000 to 1,200 per month for the overall stack. I am not quite certain about how much Elastic Search costs specifically because I w...

See all answers

What needs improvement with ELK Elasticsearch?

Elastic Search has many features, including Kibana and Logstash, which we regularly use. However, one downside in our product is cost, as it can be expensive when maintaining multiple shards and in...

See all answers

What is your primary use case for ELK Elasticsearch?

As a developer, I use Elastic Search in developing one of my applications, basically integrating the back-end with Elastic Search. Our main use case for Elastic Search is for Logstash, which is a s...

See all answers

What needs improvement with Palantir Foundry?

See all answers

What is your primary use case for Palantir Foundry?

There are several use cases that we are working on with Palantir Foundry. The first thing is for data model creation for all our data engineering pipelines. That is one use case. Palantir Foundry a...

See all answers

What advice do you have for others considering Palantir Foundry?

The visualization part in Palantir Foundry works for me at least if I want to see how the data is structured and for an initial analysis, but I would say it is not as matured as Power BI or Tableau...

See all answers

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Compared 5% of the time

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Compared 3% of the time

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Compared 6% of the time

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Compared 5% of the time

Palantir Gotham vs Palantir Foundry

Compared 3% of the time

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Compared 3% of the time

SAP Data Services vs Palantir Foundry

Compared 2% of the time

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Product Reports

Buyer's Guide

Elastic Search

April 2026

Download Elastic Search product report

Buyer's Guide

Palantir Foundry

March 2026

Download Palantir Foundry product report

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud

No data available

Overview

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.

Elastic

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

Sample Customers

T-Mobile, Adobe, Booking.com, BMW, Telegraph Media Group, Cisco, Karbon, Deezer, NORBr, Labelbox, Fingerprint, Relativity, NHS Hospital, Met Office, Proximus, Go1, Mentat, Bluestone Analytics, Humanz, Hutch, Auchan, Sitecore, Linklaters, Socren, Infotrack, Pfizer, Engadget, Airbus, Grab, Vimeo, Ticketmaster, Asana, Twilio, Blizzard, Comcast, RWE and many others.

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

Buyer's Guide

Elastic Search vs. Palantir Foundry

April 2026

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