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Elastic Search vs Palantir Foundry comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 3, 2024

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
94
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
18
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
ProductMindshare (%)
Elastic Search1.7%
Palantir Foundry4.3%
Other94.0%
Cloud Data Integration
 

Featured Reviews

reviewer2817942 - PeerSpot reviewer
Senior Software Engineer at a consultancy with 11-50 employees
Logging and vector search have transformed observability and empowered reliable ai agents
Elastic Search is not specifically being used for certain purposes. I deploy Elastic Search database on the cloud and use cloud services so that nobody can attack. However, I do not use Elastic Search to resolve attack issues. The basic main purpose of Elastic Search, as of now, I feel it can do more in the AI area. Sometime I saw that when I am developing RAG and have to generate the embeddings, which I call metadata, sometimes it tries to fail. That durability or issue handling should be improved, but apart from that, I did not find anything as of now. As per my use case, whatever I am using seems pretty good. Apart from that, some definitely improvement will be there. One improvement is that it should be faster. Whenever I am searching any logs, it takes much time. For example, if I open my log in Notepad or a similar tool, I can search the text within a second. With Elastic Search, it takes a little bit of time, ten to fifteen seconds. That can be improved. Sometimes, engineers take time to assign when I create a ticket.
BA
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.

Quotes from Members

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

Pros

"I really like the visualization that you can do within it. That's really handy. Product-wise, it is a very good and stable product."
"The special text processing features in this solution are very important for me."
"The best feature of Elastic Search that I appreciate is its monitoring capability."
"Elastic Search has impacted my organization positively as we use it for logging and APM."
"The solution has improved our organization by allowing us to quickly search data from multiple systems saving valuable time."
"I have found the sort capability of Elastic very useful for allowing us to find the information we need very quickly."
"It is easy to scale with the cluster node model.​"
"The initial setup is very easy for small environments."
"The virtualization tool is useful."
"Foundry's data visualization is fantastic."
"The solution offers very good end-to-end capabilities."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"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."
"It's scalable."
"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."
 

Cons

"It would be useful to include an assistant into Kibana for recommendations, advice, tutorials, or things that can help improve my daily work with Elastic Search."
"Elastic Enterprise Search could improve its SSL integration easier. We should not need to go to the back-end servers to do configuration, we should be able to do it on the GUI."
"Maybe Elastic Search could improve the analytics part of the search so it can be more powerful to the user."
"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."
"There are some features lacking in ELK Elasticsearch."
"From the UI point of view, we are using most probably Kibana, and I think they can do much better than that."
"Elasticsearch is useful for different business processes, but there are some problems."
"While Elastic Search is a good product, I see areas for improvement, particularly regarding the misconception that any amount of data can simply be dumped into Elastic Search."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"The workflow could be improved."
"Cost of this solution is quite high."
"Difficult to receive data from external sources."
"The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has."
"I rate Palantir Foundry five out of 10. I'm ambivalent."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
 

Pricing and Cost Advice

"we are using a licensed version of the product."
"We use the free version for some logs, but not extensive use."
"The tool is not expensive. Its licensing costs are yearly."
"The tool is an open-source product."
"The price could be better."
"The pricing structure depends on the scalability steps."
"It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process."
"We are using the open-sourced version."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
9%
Retailer
6%
Manufacturing Company
13%
Financial Services Firm
9%
Government
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business39
Midsize Enterprise11
Large Enterprise46
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise5
Large Enterprise9
 

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...
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...
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...
What needs improvement with Palantir Foundry?
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 wo...
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...
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...
 

Also Known As

Elastic Enterprise Search, Swiftype, Elastic Cloud
No data available
 

Overview

 

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
Find out what your peers are saying about Elastic Search vs. Palantir Foundry and other solutions. Updated: April 2026.
889,955 professionals have used our research since 2012.