ClickHouse vs Milvus comparison

ClickHouse and The Milvus Project are both solutions in the Open Source Databases category. ClickHouse is ranked #3 with an average rating of 8.6, while The Milvus Project is ranked #10 with an average rating of 7.5. ClickHouse holds a 6.6% mindshare in OSD, compared to The Milvus Project’s 4.9% mindshare. Additionally, 100% of ClickHouse users are willing to recommend the solution, compared to 100% of The Milvus Project users who would recommend it.

ClickHouse

Read 21 ClickHouse reviews

4,995 Views
2,398 Comparison Views

100% willing to recommend

Milvus

Read 5 Milvus reviews

3,622 Views
2,209 Comparison Views

100% willing to recommend

ClickHouse

Milvus

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Executive SummaryUpdated on Mar 1, 2026

Milvus and ClickHouse serve distinct roles in the tech landscape; Milvus excels in vector database management, whereas ClickHouse is known for its powerful analytical capabilities in a columnar database format. ClickHouse leads in data processing and analytical power, but Milvus offers a more attractive package in terms of pricing and open-source integration.

Features: Milvus efficiently handles vector similarity search and supports AI-driven applications. Another valuable feature is its ease of handling high-dimensional data, making it suitable for maintaining data security. Additionally, the Milvus Dashboard offers visualization and data management capabilities. ClickHouse offers exceptional query processing speed, storage optimization, and joins for efficient data handling. It is especially effective for aggregation and data group by functions, focusing on real-time analytics.

Room for Improvement: Milvus could enhance its user-friendliness for beginners unfamiliar with programming concepts, improve on vector stores management, and provide more guidance for integrating and loading data. ClickHouse could improve its handling of mutations, provide more published documentation, and address its complexity in setup and integration challenges.

Ease of Deployment and Customer Service: Milvus stands out for its straightforward deployment with intuitive tools, easing integration into existing systems and providing a simple onboarding experience. Its setup processes are user-friendly, catering to both beginners and experienced users. ClickHouse requires more technical expertise due to its comprehensive feature set. It provides user documentation and community support, but initial deployment can be complex, leading to more customer service interactions.

Pricing and ROI: Milvus typically offers a lower setup cost due to its open-source framework, providing strong ROI for niche applications. ClickHouse, although potentially higher in initial investment, justifies its cost by providing substantial ROI through superior data processing capabilities and scalability, making it ideal for data-heavy environments. Milvus's lower pricing is appealing for cost-conscious users, while ClickHouse's features make it attractive for those needing advanced data management.

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

Buyer's Guide

ClickHouse vs. Milvus

April 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

3rd

Ranking in Vector Databases

6th

Average Rating

8.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Milvus

Ranking in Open Source Databases

10th

Ranking in Vector Databases

13th

Average Rating

7.4

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the Open Source Databases category, the mindshare of ClickHouse is 6.6%, up from 3.3% compared to the previous year. The mindshare of Milvus is 4.9%, down from 5.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Open Source Databases Mindshare Distribution
Product	Mindshare (%)
ClickHouse	6.6%
Milvus	4.9%
Other	88.5%

Open Source Databases

Featured Reviews

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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Sameer Bhangale

Leader, Data Science Practice at a computer software company with 5,001-10,000 employees

Provides quick and easy containerization, but documentation is not very user-friendly

Milvus' documentation is not very user-friendly and doesn't help me get started quickly. Chroma DB provides super user-friendly documentation, enabling new users to get started quickly. Chroma DB's setup doesn't have many dependencies, whereas Milvus usually comes with some dependencies because of the way it needs to be deployed. Unlike Milvus, it's very easy to do POCs with Chroma DB.

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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

"It's easier to work with big data and calculations using the product."

"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 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."

"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 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."

"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."

"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."

More ClickHouse pros

"The solution is well containerized, and since containerization is quick and easy for me, I can scale it up quickly."

"Milvus offers multiple methods for calculating similarities or distances between vectors, such as L2 norm and cosine similarity. These methods help in comparing different vectors based on specific use cases. For instance, in our use case, we find that the L2 distance works best, but you can experiment with different methods to find the most suitable one for your needs."

"I like the accuracy and usability."

"Milvus has good accuracy and performance."

"The best feature of Milvus was finding the closest chunk from a huge amount of data."

Cons

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

"ClickHouse could be improved further in several areas."

"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."

"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."

"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."

"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."

"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."

"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."

More ClickHouse cons

"Milvus has higher resource consumption, which introduces complexity in implementation."

"Milvus could make it simpler. Simplifying the requirements and making it more accessible. It could be more user-friendly."

"I've heard that when we store too much data in Milvus, it becomes slow and does not work properly."

"Milvus' documentation is not very user-friendly and doesn't help me get started quickly."

Pricing and Cost Advice

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

"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."

"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."

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

"Milvus is an open-source solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

15%

Comms Service Provider

Outsourcing Company

Computer Software Company

14%

Financial Services Firm

10%

Manufacturing Company

University

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 Milvus?

I like the accuracy and usability.

See all answers

What needs improvement with Milvus?

Milvus could be improved how it could automatically generate insights from the data it holds. Milvus maintains embedding information and knows the relationships between data points. It would be use...

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What is your primary use case for Milvus?

Milvus is primarily used in RAG, which involves retrieving relevant documents or data to augment the generation of new content. Milvus helps convert text and other data into a vector space, and the...

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Compared 8% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

ClickHouse

April 2026

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Buyer's Guide

Open Source Databases

March 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

Milvus is a powerful tool for efficiently storing and retrieving large-scale vectors or embeddings. It is widely used in applications such as similarity search, recommendation systems, image and video retrieval, and natural language processing.

With its fast and accurate search capabilities, scalability, and support for multiple programming languages, Milvus is suitable for a wide range of industries and use cases.

Users appreciate its efficient search capabilities, ability to handle large-scale data, support for various data types, and user-friendly interface.

Milvus enables easy retrieval of information from vast datasets, regardless of the data format, and is praised for its high performance and scalability. The intuitive and easy-to-use interface is also highlighted as a valuable aspect of the platform.

The Milvus Project

Sample Customers

Information Not Available

1. Alibaba Group 2. Tencent 3. Baidu 4. JD.com 5. Meituan 6. Xiaomi 7. Didi Chuxing 8. ByteDance 9. Huawei 10. ZTE 11. Lenovo 12. Haier 13. China Mobile 14. China Telecom 15. China Unicom 16. Ping An Insurance 17. China Life Insurance 18. Industrial and Commercial Bank of China 19. Bank of China 20. Agricultural Bank of China 21. China Construction Bank 22. PetroChina 23. Sinopec 24. China National Offshore Oil Corporation 25. China Southern Airlines 26. Air China 27. China Eastern Airlines 28. China Railway Group 29. China Railway Construction Corporation 30. China Communications Construction Company 31. China Merchants Group 32. China Evergrande Group

Buyer's Guide

ClickHouse vs. Milvus

April 2026

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