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ClickHouse vs Faiss comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 1, 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

ClickHouse
Ranking in Open Source Databases
3rd
Ranking in Vector Databases
7th
Average Rating
8.6
Reviews Sentiment
6.8
Number of Reviews
21
Ranking in other categories
No ranking in other categories
Faiss
Ranking in Open Source Databases
12th
Ranking in Vector Databases
9th
Average Rating
8.0
Reviews Sentiment
3.3
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Open Source Databases category, the mindshare of ClickHouse is 6.6%, up from 2.8% compared to the previous year. The mindshare of Faiss is 3.8%, down from 5.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
ClickHouse6.6%
Faiss3.8%
Other89.6%
Open Source Databases
 

Featured Reviews

reviewer2785038 - PeerSpot reviewer
Senior Data Engineer at a transportation company with 501-1,000 employees
Data observability has enabled real‑time analytics and cost savings but needs smoother inserts and cleanup
ClickHouse could be improved concerning data insertion, especially given the high amount of data handled. Constant efforts are made to optimize the features on its own, but with merges and inserts, only a single insert query can be performed allowing for the input of only 100,000 rows per second. It would be beneficial to insert more data and have configurations that are less user-operated. Ideally, ClickHouse would optimize itself to handle these processes automatically, reducing the need to contact the ClickHouse support team for infrastructure optimization. Additionally, delays are experienced when trying to delete databases with corrupt data, taking too much time and causing major outages, which necessitate contacting multiple teams across continents for resolution. The community surrounding ClickHouse also seems limited, providing a reliance on documentation, and there is a scarcity of developers working with ClickHouse, which hinders growth. If ClickHouse were more user-friendly and technically feasible, it would likely see greater expansion in usage.
Kalindu Sekarage - PeerSpot reviewer
Senior Software Engineer
Integration improves accuracy and supports token-level embedding
The best features FAISS offers for my team include seamless integration with Colbert and the ability to use FAISS via the Ragatouille framework, which is tailor-made for using the Colbert model. Feature-wise, FAISS allows for more accurate result retrieval, and retrieval speed is also good when comparing the index size. Regarding features, I also emphasize that the usability of FAISS is very seamless, particularly its integration with Colbert and Ragatouille. FAISS has positively impacted my organization by helping us increase the accuracy of retrieval documents; when we store documents in token-level embedding, the accuracy will be high. Additionally, we do not need any external server to host FAISS, allowing us to integrate it with our backend framework, making it a very flexible framework.

Quotes from Members

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

Pros

"With ClickHouse, since data is stored in a columnar way, we get aggregation functions that are much faster than transactional databases, such as SQL Server, and the cost efficiency is also much reduced compared to Cosmos DB since we use it on-premises, the cost is nearly cut, which is very useful for us."
"If you have a real-time basis, you should take a look at ClickHouse because it works on a vector database, and the querying is super fast compared to traditional databases."
"We moved away from Redshift to ClickHouse because of the integration and the flexibility that it provides, which best suited our use case."
"ClickHouse provides responses within a few seconds, typically two to three seconds, which is impressive."
"ClickHouse is very easy to use; one of the good features is that it has joins, which were not present in Druid, and Druid was quite expensive, especially with our applications at Sam's Club utilizing ClickHouse very quickly."
"Regarding performance, we tried multiple solutions when Kibana was failing, including PostgreSQL, MySQL, and even MongoDB for log ingestion of huge volumes, but ClickHouse outperformed all databases we tested, leading us to choose it for further use cases."
"ClickHouse is open source with no vendor lock-in, providing excellent freedom to choose any vendor without restrictions."
"The main feature of ClickHouse is the optimizer because we had too many records to deduplicate, and the optimizer took this task by itself."
"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
 

Cons

"One issue is that you need persistent volumes. Otherwise, if one system goes down, you lose data in that cluster."
"ClickHouse could be improved further in several areas."
"ClickHouse could be improved with self-hosting capabilities and better documentation for how to host it at scale."
"I chose nine out of ten because, as I mentioned, the improvement side and the ten thousand partition limit created issues that we were hitting quite frequently, but with some schema manipulations we did manage to find a workaround, although that could have been avoided had things been better documented on how we could have solved this problem in a different approach, which took some bandwidth."
"In terms of improvements, it's not designed for very frequent small writes, making it less scalable in write-intensive workloads, and it's not flourishing in transactional use cases or when ingesting streaming data, such as batching or buffering, which is something ClickHouse will improve."
"I would like ClickHouse to work more on integration with third-party tools."
"The open-source version of ClickHouse is not very scalable."
"We had a lot of troubles while deploying a whole cluster."
"It could be more accessible for handling larger data sets."
"It would be beneficial if I could set a parameter and see different query mechanisms being run."
"One of the drawbacks of Faiss is that it works only in-memory. If it could provide separate persistent storage without relying on in-memory, it would reduce the overhead."
 

Pricing and Cost Advice

"ClickHouse Cloud is not expensive compared to other databases, costing a few dollars per month while providing fast performance."
"The tool is free."
"For pricing, if you use the self-hosted version, it would be free. Cloud services pricing would be an eight out of ten. I try to minimize costs but still have to monitor usage."
"We used the free, community version of ClickHouse."
"The tool is open-source."
"ClickHouse has an open-source version, which is free to use and has almost all the features."
"If you have an in-house deployment on Kubernetes or something, it's going to be very cheap since you'll be managing everything."
"It is an open-source tool."
"Faiss is an open-source solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
16%
Manufacturing Company
8%
Educational Organization
7%
Financial Services Firm
14%
Computer Software Company
13%
Manufacturing Company
10%
Comms Service Provider
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business13
Midsize Enterprise4
Large Enterprise8
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for ClickHouse?
My experience with pricing, setup cost, and licensing was such that the setup costs were just my own bandwidth, while licensing and pricing were done by other members of the team so it was abstract...
What needs improvement with ClickHouse?
ClickHouse can be improved on the documentation side, and there is one small constraint that is mentioned in ClickHouse documentation, which is a partition limit of ten thousand that we hit, so if ...
What is your primary use case for ClickHouse?
My main use case for ClickHouse is data ingestion and for its OLAP properties, as we had use cases where database locks were slowing us down and because ClickHouse does not have that, we chose to u...
What do you like most about Faiss?
I used Faiss as a basic database.
What is your experience regarding pricing and costs for Faiss?
I did not purchase FAISS through the AWS Marketplace because FAISS is an open-source product. My experience with pricing, setup cost, and licensing is straightforward, as there is no cost for acqui...
What needs improvement with Faiss?
I currently do not think there is anything to be improved based on our experience, as Faiss performs as we expected for our workflow. I would like to see improvement in the fact that FAISS currentl...
 

Comparisons

 

Overview

 

Sample Customers

Information Not Available
1. Facebook 2. Airbnb 3. Pinterest 4. Twitter 5. Microsoft 6. Uber 7. LinkedIn 8. Netflix 9. Spotify 10. Adobe 11. eBay 12. Dropbox 13. Yelp 14. Salesforce 15. IBM 16. Intel 17. Nvidia 18. Qualcomm 19. Samsung 20. Sony 21. Tencent 22. Alibaba 23. Baidu 24. JD.com 25. Rakuten 26. Zillow 27. Booking.com 28. Expedia 29. TripAdvisor 30. Rakuten 31. Rakuten Viber 32. Rakuten Ichiba
Find out what your peers are saying about ClickHouse vs. Faiss and other solutions. Updated: March 2026.
884,873 professionals have used our research since 2012.