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Axiom Team vs ClickHouse comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Nov 23, 2025

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

Axiom Team
Average Rating
8.6
Number of Reviews
2
Ranking in other categories
Log Management (42nd), Data Observability (9th)
ClickHouse
Average Rating
8.6
Reviews Sentiment
6.8
Number of Reviews
21
Ranking in other categories
Open Source Databases (3rd), Vector Databases (7th)
 

Mindshare comparison

Axiom Team and ClickHouse aren’t in the same category and serve different purposes. Axiom Team is designed for Log Management and holds a mindshare of 0.4%, up 0.0% compared to last year.
ClickHouse, on the other hand, focuses on Open Source Databases, holds 6.5% mindshare, up 3.9% since last year.
Log Management Mindshare Distribution
ProductMindshare (%)
Axiom Team0.4%
Splunk Enterprise Security6.8%
Wazuh5.4%
Other87.4%
Log Management
Open Source Databases Mindshare Distribution
ProductMindshare (%)
ClickHouse6.5%
PostgreSQL13.3%
MySQL10.5%
Other69.7%
Open Source Databases
 

Featured Reviews

reviewer2783832 - PeerSpot reviewer
Programmer 1 at a manufacturing company with 10,001+ employees
Logging has reduced costs and now provides fast queries and dashboards for lambda troubleshooting
Axiom Team excels at querying, with a query language that makes it very easy to assemble queries. The platform also makes it simple to create dashboards from logs. Axiom Team dashboards are used to monitor execution time, pricing, and errors. Axiom Team has positively impacted the organization by greatly reducing logging costs.
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.

Quotes from Members

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

Pros

"Axiom Team has positively impacted the organization by greatly reducing logging costs."
"Axiom Team has positively impacted my organization mainly in terms of cost, as we have reduced our logging cost by approximately half."
"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 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."
"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 positively impacted my organization by replacing PostgreSQL, which required complex foreign tables for queries, and with ClickHouse we now have Cube.js for easier data visualization."
"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."
"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 great query speeds because it is an OLAP database, so naturally, it provides higher speeds."
 

Cons

"Axiom Team can be improved by ingesting logs faster, if possible."
"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."
"My experience with pricing, setup cost, and licensing indicates that it is very expensive—ClickHouse is the most expensive option."
"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."
"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."
"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 main issue is the lack of documentation. Many features are available but are not fully documented, which can make finding information challenging."
"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."
"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."
 

Pricing and Cost Advice

Information not available
"The tool is free."
"The tool is open-source."
"ClickHouse Cloud is not expensive compared to other databases, costing a few dollars per month while providing fast performance."
"If you have an in-house deployment on Kubernetes or something, it's going to be very cheap since you'll be managing everything."
"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."
"We used the free, community version of ClickHouse."
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Top Industries

By visitors reading reviews
Construction Company
34%
Comms Service Provider
10%
Transportation Company
7%
Financial Services Firm
7%
Financial Services Firm
16%
Computer Software Company
14%
Outsourcing Company
8%
Comms Service Provider
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business13
Midsize Enterprise4
Large Enterprise8
 

Questions from the Community

What is your experience regarding pricing and costs for Axiom Team?
Pricing, setup cost, and licensing experience are not available due to lack of access to billing information.
What needs improvement with Axiom Team?
Axiom Team can be improved by ingesting logs faster, if possible.
What is your primary use case for Axiom Team?
Axiom Team is primarily used for logging Lambda functions and searching through those logs. When issues arise, the function logs are examined to determine what went wrong, and Axiom Team effectivel...
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...
 

Comparisons

 

Overview

Find out what your peers are saying about Splunk, Wazuh, Cribl and others in Log Management. Updated: May 2026.
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