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Elastic Search vs Weaviate Enterprise Cloud comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Feb 8, 2026

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 Vector Databases
2nd
Average Rating
8.2
Reviews Sentiment
6.5
Number of Reviews
90
Ranking in other categories
Indexing and Search (1st), Cloud Data Integration (6th), Search as a Service (1st)
Weaviate Enterprise Cloud
Ranking in Vector Databases
18th
Average Rating
8.0
Reviews Sentiment
3.9
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Vector Databases category, the mindshare of Elastic Search is 4.0%, down from 6.2% compared to the previous year. The mindshare of Weaviate Enterprise Cloud is 2.7%, up from 1.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Elastic Search4.0%
Weaviate Enterprise Cloud2.7%
Other93.3%
Vector Databases
 

Featured Reviews

Anurag Pal - PeerSpot reviewer
Technical Lead at a consultancy with 10,001+ employees
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.
reviewer2811174 - PeerSpot reviewer
AI Developer at a tech services company with 11-50 employees
Hybrid search has transformed search relevance and has enabled faster delivery of AI features
I am a strong advocate for Weaviate Enterprise Cloud, but there are areas where improvement would make a real difference. Monitoring and observability could be more robust out-of-the-box. Currently, I rely on external tools such as Grafana to track my cluster performance, and having a native dashboard with deeper query-level insights would be beneficial. I would appreciate SDK parity across languages. Some newer features are available on the Python SDK before they reach Go and TypeScript, which slows down teams working on other languages. The learning curve for advanced configuration, sharding strategies, replication, and tuning schema design can be steep for newer team members, so better-guided workflows or templates would help. Multi-region support is also a pending request for Weaviate to seamlessly join cross-region platforms. Auto-scaling granularity could be smarter. The current scaling responds to overall resource usage, but it would be better if it could scale independently based on query load versus ingestion load, as these spike at different times for me. Backup and disaster recovery flows need to be more flexible. While backups exist, setting up a cross-cloud failover or point-in-time recovery to a specific transaction can still be manual. Native re-ranking integration has been improved, and these are areas where Weaviate needs continued improvement.

Quotes from Members

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

Pros

"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."
"Elasticsearch includes a graphical user interface (GUI) called Kibana. The GUI features are extremely beneficial to us."
"The most valuable feature of the solution is its utility and usefulness."
"The best feature of Elastic Search is it does exactly what it says."
"A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text data."
"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 solution is quite scalable and this is one of its advantages."
"Big businesses cannot survive without Elastic Search because it gives us very good visibility and handles our use cases very well."
"Overall, Weaviate Enterprise Cloud shifted my engineering focus from managing infrastructure to building AI-first features that drive business value, which has been a crucial win for my entire organization and the time that every employee is spending per quarter."
 

Cons

"Elastic Search should provide better guides for developers."
"Both the graph feature and the reporting feature are a little bit lacking. The alerting also needs to be improved."
"We'd like to see more integration in the future, especially around service desks or other ITSM tools."
"There are potential improvements based on our client feedback, like unifying the licensing cost structure."
"I don't see improvements at the moment. The current setup is working well for me, and I'm satisfied with it. Integrating with different platforms is also fine, and I'm not recommending any changes or enhancements right now."
"To do what we want to do with Elastic Search, the queries can get complex and require a fuller understanding of the DSL."
"More AI would be beneficial. I would also appreciate more simplicity in dashboards."
"I have not explored Elastic Search at the most. Searching from vector DB is available in Elastic Search, and there is one more concept of graph searching or graph database searching. I have not explored it, but if it is not there, that would be an improvement area where Elastic Search can improve."
"The experience with pricing for Weaviate Enterprise Cloud was mixed."
 

Pricing and Cost Advice

"The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very 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 Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition."
"The tool is not expensive. Its licensing costs are yearly."
"This is a free, open source software (FOSS) tool, which means no cost on the front-end. There are no free lunches in this world though. Technical skill to implement and support are costly on the back-end with ELK, whether you train/hire internally or go for premium services from Elastic."
"The pricing model is questionable and needs to be addressed because when you would like to have the security they charge per machine."
"It can be expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
9%
Retailer
7%
Comms Service Provider
13%
Computer Software Company
11%
Media Company
10%
Educational Organization
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business38
Midsize Enterprise10
Large Enterprise45
No data available
 

Questions from the Community

What do you like most about ELK Elasticsearch?
Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time anal...
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.
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...
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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.
1. KLM Royal Dutch Airlines 2. Rabobank 3. Philips 4. ING Bank 5. ABN AMRO Bank 6. Booking.com 7. TomTom 8. Randstad 9. Heineken 10. Shell 11. Unilever 12. ASML 13. Ahold Delhaize 14. DSM 15. AkzoNobel 16. VodafoneZiggo 17. NXP Semiconductors 18. Signify 19. Wolters Kluwer 20. Adyen 21. Aegon 22. Arcadis 23. ASR Nederland 24. BAM Group 25. Boskalis 26. Corbion 27. Fugro 28. Galapagos 29. GrandVision 30. IMCD Group 31. Kendrion 32. OCI
Find out what your peers are saying about Microsoft, Elastic, Redis and others in Vector Databases. Updated: March 2026.
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