Cassandra vs ClickHouse comparison

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Cassandra

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Executive SummaryUpdated on Nov 23, 2025

Cassandra and ClickHouse compete in the database management system category. Based on performance and features, ClickHouse often holds the upper hand due to its exceptional speed and analytical capabilities.

Features: Cassandra offers scalability, fault tolerance, and a distributed architecture that excels at managing large datasets. ClickHouse provides high-speed query performance, advanced analytics, and real-time data processing, making it ideal for analytics-heavy tasks.

Room for Improvement: Cassandra may face challenges with read performance and requires improvements in data consistency tuning. It could enhance its documentation for better user adoption. ClickHouse can improve in handling data mutations and easing initial setup complexity. Its integration with other systems could be further optimized.

Ease of Deployment and Customer Service: Cassandra's straightforward deployment process is supported by a robust technical community, ensuring efficient problem-solving. ClickHouse requires more technical expertise upon deployment but offers extensive documentation and effective support channels, rewarding with superior performance.

Pricing and ROI: Cassandra is cost-effective with a modest initial setup and offers great ROI for startups due to its open-source nature. ClickHouse has a higher initial cost due to advanced hardware needs but delivers significant ROI through superior query performance and efficiency for complex analytics.

To learn more, read our detailed Cassandra vs. ClickHouse Report (Updated: December 2025).

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Cassandra vs. ClickHouse

December 2025

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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

Cassandra

Ranking in Vector Databases

14th

Average Rating

8.0

Reviews Sentiment

6.0

Number of Reviews

Ranking in other categories

NoSQL Databases (7th)

ClickHouse

Ranking in Vector Databases

7th

Average Rating

8.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Open Source Databases (4th)

Mindshare comparison

As of January 2026, in the Vector Databases category, the mindshare of Cassandra is 2.3%, up from 1.6% compared to the previous year. The mindshare of ClickHouse is 4.7%, up from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Market Share Distribution
Product	Market Share (%)
ClickHouse	4.7%
Cassandra	2.3%
Other	93.0%

Vector Databases

Featured Reviews

Monirul Islam Khan

Head, Data Integration & Management at a non-profit with 10,001+ employees

Has maintained secure document storage and efficient data distribution with peer-to-peer architecture

The functions or features in Cassandra that I have found most valuable are that it is a distributed system similar to Mongo. It's good enough for comparison with another SQL database, so it's smooth and organized for distributed database system. The peer-to-peer architecture in Cassandra is helpful for network decentralization, and I have already introduced that feature. Cassandra features in peer-to-peer as well as another monitoring, so basically, it's good enough for our service. The tunable consistency level in Cassandra is good, and we are using that feature already. In terms of built-in caching and lightweight transactions in Cassandra, the transaction level is good, and it's optimized, so there are no more issues in that database. Based on my experience, Cassandra is good for document management system, as well as distributed database system, and the automatic recovery process is there. Additionally, the database monitoring system or auditing system is well-comparable with other database systems, so we are actually happy to be using this Cassandra database.

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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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Quotes from Members

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

Pros

"The use of Cassandra in real-time data analytics has been pivotal for our e-commerce platform. As our platform operates 24/7, providing services to sellers and customers alike, the need for real-time updates is paramount."

"Some of the valued features of this solution are it has good performance and failover."

"The time series data was one of the best features along with auto publishing."

"Cassandra has some features that are more useful for specific use cases where you have time series where you have huge amounts of writes. That should be quick, but not specifically the reads. We needed to have quicker reads and writes and this is why we are using Cassandra right now."

"Based on my experience, Cassandra is good for document management system, as well as distributed database system, and the automatic recovery process is there."

"Overall, I would rate Cassandra as nine because of its fast writes, which really suit our use cases mostly."

"The most valuable features of Cassandra are its scaling capabilities and its non-SQL nature capabilities."

"Its retrieval is similar to an RDBMS, so our team finds it easy to adapt."

More Cassandra pros

"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 provides responses within a few seconds, typically two to three seconds, which is impressive."

"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."

"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."

"There is no better option than ClickHouse in all OLAP-based databases, so I think it is best to use ClickHouse in that regard."

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

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

"ClickHouse provides great query speeds because it is an OLAP database, so naturally, it provides higher speeds."

More ClickHouse pros

Cons

"Interface is not user friendly."

"The secondary index in Cassandra was a bit problematic and could be improved."

"We experience configuration issues when accommodating the volumes we require, which often necessitates consultation with the Cassandra development team."

"Cassandra is very complex to manage. Sometimes, I need to involve a senior DevOps engineer if we encounter a problem."

"The solution is not easy to use because it is a big database and you have to learn the interface. This is the case though in most of these solutions."

"The solution is limited to a linear performance."

"While Cassandra can handle NoSQL, I think there should be more flexibility for whole schema design when data is stored in wide columns. Additionally, I believe that eventual consistency should be enhanced."

"Maybe they can improve their performance in data fetching from a high volume of data sets."

More Cassandra cons

"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."

"In terms of needed improvements, some enhancements in documentation are necessary."

"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."

"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."

"The main issue is the lack of documentation. Many features are available but are not fully documented, which can make finding information challenging."

"If you join our team, it should be easy for you to use ClickHouse, especially if you are a developer. However, you need to read the documentation and understand the problems you are trying to solve."

"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."

More ClickHouse cons

Pricing and Cost Advice

"I don't have the specific numbers on pricing, but it was fairly priced."

"I use the tool's open-source version."

"We pay for a license."

"Cassandra is a free open source solution, but there is a commercial version available called DataStax Enterprise."

"There are licensing fees that must be paid, but I'm not sure if they are paid monthly or yearly."

"We are using the open-source version of Cassandra, the solution is free."

"ClickHouse Cloud is not expensive compared to other databases, costing a few dollars per month while providing fast performance."

"The tool is free."

"The tool is open-source."

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

"We used the free, community version of ClickHouse."

"If you have an in-house deployment on Kubernetes or something, it's going to be very cheap since you'll be managing everything."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

Comms Service Provider

Retailer

Financial Services Firm

17%

Computer Software Company

16%

Outsourcing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	1
Large Enterprise	14

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

Questions from the Community

What do you like most about Cassandra?

The use of Cassandra in real-time data analytics has been pivotal for our e-commerce platform. As our platform operates 24/7, providing services to sellers and customers alike, the need for real-ti...

What is your experience regarding pricing and costs for Cassandra?

The pricing for Cassandra is a little bit high, so it would be better for our community services if they consider community pricing for any non-profit organization like an NGO or other things. It w...

What needs improvement with Cassandra?

Regarding areas of improvement for Cassandra, currently, we are not facing significant issues. Some issues arise from our vendors like Apache slowness and distribution or load balancing from HAProx...

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...

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Product Reports

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Cassandra

January 2026

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January 2026

Overview

Cassandra is a distributed and scalable database management system used for real-time data processing.

It is highly valued for its ability to handle large amounts of data, scalability, high availability, fault tolerance, and flexible data model.

It is commonly used in finance, e-commerce, and social media industries.

Apache

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.

Sample Customers

1. Apple 2. Netflix 3. Facebook 4. Instagram 5. Twitter 6. eBay 7. Spotify 8. Uber 9. Airbnb 10. Adobe 11. Cisco 12. IBM 13. Microsoft 14. Yahoo 15. Reddit 16. Pinterest 17. Salesforce 18. LinkedIn 19. Hulu 20. Airbnb 21. Walmart 22. Target 23. Sony 24. Intel 25. Cisco 26. HP 27. Oracle 28. SAP 29. GE 30. Siemens 31. Volkswagen 32. Toyota

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