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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
8th
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
13th
Ranking in Vector Databases
13th
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 August 2026, in the Open Source Databases category, the mindshare of ClickHouse is 6.4%, up from 5.1% compared to the previous year. The mindshare of Faiss is 3.0%, down from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Open Source Databases Mindshare Distribution
ProductMindshare (%)
ClickHouse6.4%
Faiss3.0%
Other90.6%
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

"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."
"The tool's most valuable feature is a database. It supports portal APIs and offers good flexibility."
"The tool is column-based and infinitely scalable."
"ClickHouse is much faster than traditional databases like MySQL and MongoDB. Its column-row searching strategy makes it very efficient. With ClickHouse, we can manage multiple databases, automatically insert data from other databases and delete data as needed. It supports real-time query performance, allowing simultaneous data insertion and retrieval. ClickHouse has improved significantly over the past two years, adding more functions and queries, as well as top functionality."
"We moved away from Redshift to ClickHouse because of the integration and the flexibility that it provides, which best suited our use case."
"We faced a challenge with deploying ClickHouse onto Kubernetes. Recently, we've been using ClickHouse Cloud, and the main issue is the high cost of the cloud service. The pricing isn't very competitive, especially for startups. I would instead buy a server and self-host if I have enough disk space. Besides that, ClickHouse has done very well, with clear goals and effective execution."
"ClickHouse has positively impacted my organization, as there was an entire exercise done on which database we were supposed to use for solving our problems, and we found ClickHouse was the one performing the best, which is when we adopted it."
"It's easier to work with big data and calculations using the product."
"The product has better performance and stability compared to one of its competitors."
"I used Faiss as a basic database."
 

Cons

"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."
"ClickHouse has its own concept of database triggers and doesn't support traditional database triggers."
"There are some areas where ClickHouse could improve. Specifically, we encountered incompatibilities with its SQL syntax when migrating queries from MySQL or SQL to ClickHouse. This difference in details made it challenging to figure out the exact issues. Additionally, we faced difficulties due to the lack of a proper Django driver for ClickHouse, unlike MySQL, which Django supports out of the box."
"The open-source version of ClickHouse is not very scalable."
"There aren't too many improvements I'd suggest for ClickHouse as it covers all my needs. There are just a few technical issues. For example, sometimes, when you want to get unique values and use certain tables, they don't work as expected. But it's not a major problem."
"One issue is that you need persistent volumes. Otherwise, if one system goes down, you lose data in that cluster."
"Too many issues exist for beginners to set up ClickHouse. Many parameters must be configured, such as maximum scatter part settings that determine when writing to a table stops."
"ClickHouse could be improved further in several areas."
"It would be beneficial if I could set a parameter and see different query mechanisms being run."
"It could be more accessible for handling larger data sets."
"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 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."
"We used the free, community version of ClickHouse."
"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."
"The tool is open-source."
"The tool is free."
"ClickHouse Cloud is not expensive compared to other databases, costing a few dollars per month while providing fast performance."
"Faiss is an open-source solution."
"It is an open-source tool."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
12%
Outsourcing Company
9%
Comms Service Provider
9%
Financial Services Firm
15%
Manufacturing Company
10%
Comms Service Provider
10%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise5
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 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...
What is your primary use case for Faiss?
My main use case for FAISS is in a retrieval-augmented generation project using it with OpenAI, where we use FAISS to store our embeddings created by the Colbert model and for retrieval as well. In...
 

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: August 2026.
908,858 professionals have used our research since 2012.