ClickHouse vs Faiss comparison

ClickHouse and Meta are both solutions in the Open Source Databases category. ClickHouse is ranked #4 with an average rating of 8.6, while Meta is ranked #10 with an average rating of 8.0. ClickHouse holds a 6.5% mindshare in OSD, compared to Meta’s 3.6% mindshare. Additionally, 100% of ClickHouse users are willing to recommend the solution, compared to 100% of Meta users who would recommend it.

ClickHouse

Read 21 ClickHouse reviews

3,722 Views
1,712 Comparison Views

100% willing to recommend

Faiss

Read 3 Faiss reviews

3,588 Views
1,686 Comparison Views

100% willing to recommend

ClickHouse

Faiss

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Executive SummaryUpdated on Nov 23, 2025

ClickHouse and Faiss are both active in the big data analytics and large-scale data search categories. ClickHouse appears to have the edge for analytics with structured data, whereas Faiss is strong in high-dimensional vector search for AI applications.

Features: ClickHouse offers real-time data ingestion, columnar storage for efficient querying, and high-speed analytics supporting large datasets. Faiss provides fast similarity search, dense vector clustering, and strong integration with Python, useful for AI and machine learning initiatives.

Ease of Deployment and Customer Service: ClickHouse provides seamless on-premises and cloud deployments, complete with comprehensive documentation simplifying setup. Customer service is responsive and supportive. Faiss, although easily deployable with strong Python integration, may be complex initially due to its specialization in ML models. Customer service primarily relies on community support, fitting for tech-savvy teams.

Pricing and ROI: ClickHouse offers a flexible pricing model based on cloud consumption or on-premises licensing, promising a compelling ROI through efficient data analytics. Faiss, being open-source, is cost-effective but may require additional investment for optimal ML workflow integration and specialist deployments. This can lead to hidden costs, potentially affecting ROI compared to the streamlined analytics features of ClickHouse.

To learn more, read our detailed ClickHouse vs. Faiss Report (Updated: January 2026).

Buyer's Guide

ClickHouse vs. Faiss

January 2026

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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

4th

Ranking in Vector Databases

7th

Average Rating

8.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Faiss

Ranking in Open Source Databases

10th

Ranking in Vector Databases

5th

Average Rating

8.0

Reviews Sentiment

3.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Open Source Databases category, the mindshare of ClickHouse is 6.5%, up from 2.0% compared to the previous year. The mindshare of Faiss is 3.6%, down from 5.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Open Source Databases Market Share Distribution
Product	Market Share (%)
ClickHouse	6.5%
Faiss	3.6%
Other	89.9%

Open Source Databases

Featured Reviews

Yush Mittal

Level 2 Software Engineer at a computer software company with 201-500 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.

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Kalindu Sekarage

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.

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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

"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."

"ClickHouse has reduced our storage cost and improved our 99th percentile latency by 40%."

"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."

"ClickHouse Cloud saves a lot of time for our DevOps engineers since there isn't much deployment involved and everything is served directly, leading to increased efficiency and productivity."

"The best thing about the tool is that I can set it up on my computer and run queries without depending on the cloud. This is why I use it every day."

"The tool is column-based and infinitely scalable."

"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 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."

More ClickHouse pros

"I used Faiss as a basic database."

"The product has better performance and stability compared to one of its competitors."

Cons

"The open-source version of ClickHouse is not very scalable."

"ClickHouse could be improved further in several areas."

"Initially, I faced challenges integrating ClickHouse, particularly with inserting data from ActiveMQ, which caused duplicates. However, after adjusting the ClickHouse settings, the issue was resolved and there were no more duplicates."

"I would like ClickHouse to work more on integration with third-party tools."

"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."

"The aggregation capability is a valuable feature. It's highly efficient, allowing us to review entire transaction histories and user activities in the market. We've tried MongoDB, Postgres, MariaDB, and BigQuery, but ClickHouse is the most cost-efficient solution for collecting data at high speeds with minimal cost. We even used ClickHouse Cloud for a month, and it proved to be a great setup, especially for startups looking to handle big data. For example, if there is a need for 2-4 terabytes of data and around 40 billion rows with reasonable computing speed and latency, ClickHouse is ideal. Regarding the real-time query performance of ClickHouse, when using an API server to query it, I achieved query results in less than twenty milliseconds in some of my experiments with one billion rows. However, it depends on the scenario since ClickHouse has limitations in handling mutations. Additionally, one of ClickHouse's strengths is its compression capability. Our experimental server has only four terabytes, and ClickHouse effectively compresses data, allowing us to store large amounts of data at high speed. This compression efficiency is a significant advantage of using ClickHouse."

"ClickHouse could be improved with self-hosting capabilities and better documentation for how to host it at scale."

"We would like to have fuzzy search capabilities in ClickHouse like we had with Kibana because there are scenarios where we cannot search keywords fuzzily in ClickHouse, whereas Elasticsearch allows that, and in such cases, Elasticsearch outperforms ClickHouse."

More ClickHouse cons

"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."

"It could be more accessible for handling larger data sets."

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."

"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."

"The tool is open-source."

"ClickHouse has an open-source version, which is free to use and has almost all the features."

"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."

"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

18%

Computer Software Company

16%

Manufacturing Company

Comms Service Provider

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	13
Midsize Enterprise	4
Large Enterprise	8

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...

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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 ...

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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...

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What do you like most about Faiss?

I used Faiss as a basic database.

See all answers

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...

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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...

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Compared 10% of the time

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Compared 8% of the time

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Compared 8% of the time

MongoDB Enterprise Advanced vs Faiss

Compared 7% of the time

LanceDB vs Faiss

Compared 3% of the time

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Product Reports

Buyer's Guide

ClickHouse

January 2026

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Buyer's Guide

Open Source Databases

January 2026

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Overview

ClickHouse is renowned for its speed, scalability, and real-time query performance. Its compatibility with SQL standards enhances flexibility while enabling integration with popular tools.

ClickHouse leverages a column-based architecture for efficient data compression and real-time analytics. It seamlessly integrates with tools like Kafka and Tableau and is effective in handling large datasets due to its cost-efficient aggregation capabilities. With robust data deduplication and strong community backing, users can access comprehensive documentation and up-to-date functionality. However, improvements in third-party integration, cloud deployment, and handling of SQL syntax differences are noted, impacting ease-of-use and migration from other databases.

What features make ClickHouse outstanding?

Speed and Scalability: Enables rapid deployment and scalability, particularly on Kubernetes.
SQL Compatibility: Aligns with SQL standards for flexible database management.
Column-based Structure: Offers efficient data compression and quick query performance.
Integration Support: Works with Kafka, Tableau, and other popular platforms.
Data Deduplication: Manages complex data duplication efficiently.

What benefits should users consider?

Cost-Efficiency: Reduces expenses while managing large datasets effectively.
Real-Time Analytics: Provides fast data processing for real-time insights.
Flexibility: Offers multiple integration points and compatibility options.
Robust Documentation: Backed by an active development community, ensuring comprehensive support.

ClickHouse is deployed in sectors like telecommunications for passive monitoring and is beneficial for data analytics, logging Clickstream data, and as an ETL engine. Organizations harness it for machine learning applications when combined with GPT. With the ability to be installed independently, it's an attractive option for avoiding cloud service costs.

ClickHouse

Faiss is a powerful library for efficient similarity search and nearest neighbor retrieval in large-scale datasets. It is widely used in image and text processing, recommendation systems, and natural language processing.

Users appreciate its speed, scalability, and ability to handle high-dimensional data effectively. Faiss also offers easy integration and extensive support for different programming languages.

Its valuable features include efficient search capabilities, support for large-scale datasets, various similarity measures, easy integration, and comprehensive documentation and community support.

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

Buyer's Guide

ClickHouse vs. Faiss

January 2026

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