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Apache Kafka vs Upsolver comparison

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Comparison Buyer's Guide

Executive Summary

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

Apache Kafka
Ranking in Streaming Analytics
3rd
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
No ranking in other categories
Upsolver
Ranking in Streaming Analytics
22nd
Average Rating
8.6
Reviews Sentiment
7.6
Number of Reviews
4
Ranking in other categories
Data Integration (39th)
 

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.6%, up from 3.6% compared to the previous year. The mindshare of Upsolver is 1.3%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Apache Kafka3.6%
Upsolver1.3%
Other95.1%
Streaming Analytics
 

Featured Reviews

Amandeep Pawar - PeerSpot reviewer
Engineer Technology at Iris Software Technologies Private Limited
Event-driven architecture has improved asynchronous communication and supports high throughput
We can improve the high throughput because there are some limitations. We could add something to improve Apache Kafka. Based on my daily usage and analysis, there is a complex setup and management, which is one area requiring improvement. Apache Kafka does not have built-in message delay or scheduling capabilities. Apache Kafka cannot natively schedule messages. Limited message prioritization is also an area requiring improvement. Ordering is limited to a single partition. Large messages affect performance. Apache Kafka is optimized for many small to medium-sized messages, but large payloads increase network usage, increase disk usage, and slow down producers and consumers. I rate Apache Kafka eight out of ten instead of ten out of ten because operating an Apache Kafka cluster requires expertise in partitioning, application monitoring, and capacity planning. Self-managed deployment can become complex as the cluster grows. Pro-managed Apache Kafka services significantly reduce the operational overhead.
reviewer2784462 - PeerSpot reviewer
Software Engineer at a tech vendor with 10,001+ employees
Streaming pipelines have become simpler and onboarding new data sources is now much faster
One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

Quotes from Members

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

Pros

"We implemented the notification system between our components, and we found that Apache Kafka performs well in scalability, improving our organization because of the scalability and the comfort of a fail-safe or disaster recovery it provides."
"The processing power of Apache Kafka is good when you have requirements for high throughput and a large number of consumers."
"Apache Kafka is an open-source solution that can be used for messaging or event processing."
"The most valuable feature is that it can handle high volume."
"Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing."
"We appreciate the ability to persistently and quickly write data, as well as the flexibility to customize it for multiple customers. Additionally, we like the ability to retain data within Apache Kafka and use features, such as time travel to access past customer data. The connection with other systems, such as Apache Kafka and IBM DB2."
"It eases our current data flow and framework."
"It just works and it's super fast."
"I have saved 50 to 60% on maintaining pipelines since using Upsolver."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."
 

Cons

"Config management can be better. We are always trying to find the best configs, which is a challenge."
"I would like to see an improvement in authentication management."
"Kafka does not provide control over the message queue, so we do not know whether we are experiencing lost or duplicate messages."
"Kafka's interface could also use some work. Some of our products are in C, and we don't have any libraries to use with C. From an interface perspective, we had a library from the readies. And we are streaming some of the products we built to readies. That is one of the requirements. It would be good to have those libraries available in a future release for our C++ clients or public libraries, so we can include them in our product and build on that."
"We cannot apply all of our security requirements because it is hard to upload them."
"Kafka has a lot of monitors, but sometimes it's most important to just have a simple monitor."
"The manageability should be improved. There are lots of things we need to manage and it should have a function that enables us to manage them all cohesively."
"More Windows support, I believe, is one area where it can improve. We need to wrap it as a service, but there isn't one built into Windows."
"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."
"There is room for improvement in query tuning."
"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."
"I would say Upsolver's scalability is eight out of 10 because of pricing."
"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."
 

Pricing and Cost Advice

"Apache Kafka is an open-source solution."
"The solution is free, it is open-source."
"It's a premium product, so it is not price-effective for us."
"The cost can vary depending on the provider and the specific flavor or version you use. I'm not very knowledgeable about the pricing details."
"The solution is open source; it's free to use."
"I rate Apache Kafka's pricing a five on a scale of one to ten, where one is cheap and ten is expensive. There are no additional costs apart from the licensing fees for Apache Kafka."
"This is an open-source version."
"Apache Kafka is an open-source solution and there are no fees, but there are fees associated with confluence, which are based on subscription."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Outsourcing Company
13%
Manufacturing Company
10%
Computer Software Company
8%
Real Estate/Law Firm
14%
Manufacturing Company
14%
Retailer
13%
Construction Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business33
Midsize Enterprise20
Large Enterprise51
No data available
 

Questions from the Community

What are the differences between Apache Kafka and IBM MQ?
Apache Kafka is open source and can be used for free. It has very good log management and has a way to store the data used for analytics. Apache Kafka is very good if you have a high number of user...
What is your experience regarding pricing and costs for Apache Kafka?
Based on my understanding for the setup and cost to set up Apache Kafka, I was not responsible for the pricing or licensing decisions. Apache Kafka itself is open source and free to use. Infrastruc...
What needs improvement with Apache Kafka?
We can improve the high throughput because there are some limitations. We could add something to improve Apache Kafka. Based on my daily usage and analysis, there is a complex setup and management,...
What is your experience regarding pricing and costs for Upsolver?
My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.
What needs improvement with Upsolver?
I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.
What is your primary use case for Upsolver?
My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...
 

Comparisons

 

Overview

 

Sample Customers

Uber, Netflix, Activision, Spotify, Slack, Pinterest
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Find out what your peers are saying about Apache Kafka vs. Upsolver and other solutions. Updated: September 2026.
915,341 professionals have used our research since 2012.