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 886,664 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 solution has good security features. I have been happy with the dashboards and interface."

"The UI is very nice, and performance wise it's quite good too."

"Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10."

"From a technical point of view, there are no significant issues recalled as Elastic Search has been absolutely awesome for this use case and covers 100% of the needs."

"The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server."

"The machine learning features of Elastic Search are very interesting, including the possibility to include models such as ELSER and different multilingual models that let us fine-tune our searches and use them in our search projects."

"One thing I appreciate about Elastic Search is the ability to aggregate everything into one dashboard, so I can have monitoring, logs, and traces in one portal instead of having multiple different tools to do the same."

"In the last 18 months Elastic has really caught up and also gone way beyond AWS by putting together all the missing components that make ELK Elasticsearch the most comprehensive stack in the entire Big Data ecosystem."

More Elastic Search pros

"It has been the platform for end to end data processing, manipulations, and reporting, greatly improved org's data reporting effort."

"Encapsulates all the components without the requirement to integrate or check compatibility."

"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."

"The interface is really user-friendly."

"I rate Palantir Foundry a ten out of ten."

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

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

"It's scalable."

More Palantir Foundry pros

Cons

"The one area that can use improvement is the automapping of fields."

"While integrating with tools like agents for ingesting data from sources like firewalls is valuable, I believe prioritizing improvements to the core product would be more beneficial."

"New Relic could be more flexible, similar to Elasticsearch."

"Maybe Elastic Search could improve the analytics part of the search so it can be more powerful to the user."

"We'd like more user-friendly integrations."

"There are challenges with performance management and scalability."

"It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement."

"There is an index issue in which the data starts to crash as it increases."

More Elastic Search cons

"I rate Palantir Foundry five out of 10. I'm ambivalent."

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

"The startup pricing is high, causing concern despite being cost-effective in terms of total cost of ownership."

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

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

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

"Cost of this solution is quite high."

"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."

More Palantir Foundry cons

Pricing and Cost Advice

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

"The pricing and license model are clear: node-based model."

"To access all the features available you require both the open source license and the production license."

"The price of Elastic Enterprise is very, very competitive."

"The solution is less expensive than Stackdriver and Grafana."

"We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more."

"The pricing model is questionable and needs to be addressed because when you would like to have the security they charge per machine."

"The cost varies based on factors like usage volume, network load, data storage size, and service utilization. If your usage isn't too extensive, the cost will be lower."

More Elastic Search pricing and cost advice

"Palantir Foundry is an expensive solution."

"It's expensive."

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

"The solution’s pricing is high."

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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?

On the subject of pricing, Elastic Search is very cost-efficient. You can host it on-premises, which would incur zero cost, or take it as a SaaS-based service, where the expenses remain minimal.

See all answers

What needs improvement with ELK Elasticsearch?

From the UI point of view, we are using most probably Kibana, and I think they can do much better than that. That is something they can fine-tune a little bit, and then it will definitely be a good...

See all answers

What is your primary use case for ELK Elasticsearch?

Elastic Search use cases for us involve maintaining a huge amount of data per day, around millions of transactions for each record. We are maintaining all this data with Elastic, and Elastic is doi...

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

Comparisons

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Compared 12% of the time

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Compared 7% of the time

Azure AI Search vs Elastic Search

Compared 7% of the time

Milvus vs Elastic Search

Compared 5% of the time

PostgreSQL vs Elastic Search

Compared 3% of the time

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Cognizant AI & Analytics vs Palantir Foundry

Compared 6% of the time

Confluent vs Palantir Foundry

Compared 5% of the time

Palantir Gotham vs Palantir Foundry

Compared 3% of the time

MuleSoft Anypoint Platform vs Palantir Foundry

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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