Cassandra vs ClickHouse comparison

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Cassandra

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Executive SummaryUpdated on Mar 1, 2026

Apache Cassandra and ClickHouse are open-source database management systems. ClickHouse appears to have an advantage due to its superior analytical capabilities, although Cassandra offers excellent value and support.

Features: Cassandra provides peer-to-peer architecture, distributed scalability, and high availability. ClickHouse benefits from a columnar storage system, real-time queries, and efficient data compression.

Room for Improvement: Cassandra could improve in query performance and user interface friendliness, as well as providing more comprehensive query language support. ClickHouse could be enhanced in handling transactional workloads, simplifying integration with other systems, and better managing very high concurrency.

Ease of Deployment and Customer Service: Cassandra involves more complexity in setup and requires significant expertise, whereas ClickHouse benefits from simpler installation and a responsive community-driven support system, making deployment smoother.

Pricing and ROI: Cassandra is usually lower in initial costs and delivers high ROI with large datasets. ClickHouse may involve higher setup costs but offers significant performance benefits for analytical tasks, leading to a high return on analytical investments.

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

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Cassandra vs. ClickHouse

April 2026

Helped 892,776 peers since 2012

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 (3rd)

Mindshare comparison

As of May 2026, in the Vector Databases category, the mindshare of Cassandra is 3.4%, up from 1.7% compared to the previous year. The mindshare of ClickHouse is 5.6%, up from 3.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Vector Databases Mindshare Distribution
Product	Mindshare (%)
ClickHouse	5.6%
Cassandra	3.4%
Other	91.0%

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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reviewer2785038

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.

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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

"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."

"The most valuable features are the counter features and the NoSQL schema, and it also has good scalability because you can scale Cassandra to any infinite level."

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

"I am getting much better performance than relational databases."

"The most valuable feature of Cassandra is its fast retrieval. Additionally, the solution can handle large amounts of data. It is the quickest application we use."

"We have experienced zero downtime using Cassandra, it is highly stable."

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

"Since I haven't had years of experience with it, it's still new to me. One valuable feature is its distribution, so I can run it partly in the cloud and part on-prem. That's a feature I'd like to use but haven't yet because we're trying to move to Azure. I don't know if or when that will happen. Ideally, we'd have it distributed over the cloud and on-prem simultaneously, so if something happens to our on-prem, we can keep going in the cloud, like a pay-as-you-go model with Azure."

More Cassandra pros

"The main feature of ClickHouse is the optimizer because we had too many records to deduplicate, and the optimizer took this task by itself."

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

"The tool's most valuable feature is a database. It supports portal APIs and offers good flexibility."

"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."

"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."

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

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

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

More ClickHouse pros

Cons

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

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

"We have had stability issues including out of memory issues and crashes with earlier versions of the product."

"The clustering needs to be better; it is getting there."

"The disc space is lacking. You need to free it up as you are working."

"The solution is limited to a linear performance."

"Cassandra can improve by adding more built-in tools. For example, if you want to do some maintenance activities in the cluster, we have to depend on third-party tools."

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

More Cassandra cons

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

"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."

"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 can be improved, and the main challenge I see is its operational complexity."

"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 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."

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

"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."

More ClickHouse cons

Pricing and Cost Advice

"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."

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

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

"We pay for a license."

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

"The tool is free."

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

"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."

"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."

"The tool is open-source."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Comms Service Provider

Computer Software Company

Retailer

Financial Services Firm

16%

Computer Software Company

14%

Outsourcing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	14

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

Questions from the Community

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 primary use case for Cassandra?

My use case for Cassandra is for a document and other unstructured data management system as well as structured data for ultra-poor member community edition, community members' PII information, so ...

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

May 2026

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May 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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