Apache Kafka on Confluent Cloud vs Upsolver comparison

Confluent and Upsolver are both solutions in the Streaming Analytics category. Confluent is ranked #12 with an average rating of 8.5, while Upsolver is ranked #20 with an average rating of 8.5. Confluent holds a 0.5% mindshare in SA, compared to Upsolver’s 0.7% mindshare. Additionally, 100% of Confluent users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Apache Kafka on Confluent C...

Read 15 Apache Kafka on Confluent Cloud reviews

1,403 Views
84 Comparison Views

100% willing to recommend

Upsolver

Read 3 Upsolver reviews

612 Views
141 Comparison Views

100% willing to recommend

Apache Kafka on Confluent C...

Upsolver

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Kafka on Confluent Cloud and Upsolver based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Kafka on Confluent Cloud vs. Upsolver Report (Updated: December 2025).

Buyer's Guide

Apache Kafka on Confluent Cloud vs. Upsolver

December 2025

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Helped 881,082 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

Apache Kafka on Confluent C...

Ranking in Streaming Analytics

12th

Average Rating

8.6

Reviews Sentiment

5.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Data Integration (37th)

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.5%. The mindshare of Upsolver is 0.7%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Kafka on Confluent Cloud	0.5%
Upsolver	0.7%
Other	98.8%

Streaming Analytics

Featured Reviews

Ashier Fernandes

Lead Software Engineer at a tech vendor with 10,001+ employees

Has unified log streams from multiple systems and accelerated issue tracking through streamlined setup

I think Apache Kafka on Confluent Cloud can be improved by probably working more around Confluent or the tool. In my opinion, it should utilize the response structures in a better way or be able to detect if there is any variable or if there is any data structure that is mismatched, as it would be easier than us manually having to put in the exact name in order for it to match the response. Regarding additional improvements, I would say probably around error handling, where when we encounter errors specific to our response structures and everything, or the tables or anything of that nature, it would be better if we were prompted with better error handling mechanisms. I do not think there are any other improvements Apache Kafka on Confluent Cloud needs, aside from error handling and response structures.

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reviewer2784462

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.

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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 very fast and helps us to create the project, guarantee the message delivery, and the performance."

"Confluent helped me to streamline all those logs into one place, and then I was consuming those logs that were produced, which made it very much easier because I know Kafka and using Confluent made it much simpler."

"The state-saving feature is very much appreciated. It allows me to rewind a certain process if I see an error and then reprocess it."

"Confluent Cloud handles data volume pretty well."

"Kafka and Confluent Cloud have proven to be cost-effective, especially when compared to other tools. In a recent BI integration program over the past year, we assessed multiple use cases spanning ship-to-shore and various Azure integrations. Our findings revealed that Confluent Kafka performed exceptionally well, standing out alongside Genesys and Azure Event Hubs. While these three are top contenders, the choice among other tools depends on the specific use case and project requirements. The customer initially used tools like SMQs, FITRA, and Stream for real-time data processing. However, after our recommendation, Confluent Cloud proved to be a superior choice, capable of replacing these three tools and simplifying their data infrastructure. This shift to a single tool, Confluent Cloud, streamlined their operations, making maintenance and management more efficient for their internal projects."

"In case of huge transactions on the web or mobile apps, it helps you capture real-time data and analyze it."

"Some of the best features with Apache Kafka on Confluent Cloud are streaming and event capabilities, which are important due to scalability and resiliency."

"The product's installation phase is pretty straightforward for us since we know how to use it."

More Apache Kafka on Confluent Cloud pros

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"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."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

Cons

"I thought Confluent would stop me when I crossed the credits, but it did not, and then I got charged."

"There could be an in-built feature for data analysis."

"The clustering is a little hard for juniors and clients. It's suitable for senior engineers, but the configuration and clustering are very hard for juniors."

"The administration port could be more extensive."

"Regarding real-time data usage, there were challenges with CDC (Change Data Capture) integrations. Specifically, with PyTRAN, we encountered difficulties. We recommended using our on-premises Kaspersky as an alternative to PyTRAN for that specific use case due to issues with CDC store configuration and log reading challenges with the iton components."

"There's one thing that's a common use case, but I don't know why it's not covered in Kafka. When a message comes in, and another message with the same key arrives, the first version should be deleted automatically."

"In terms of improvements, observability and monitoring are areas that could be enhanced. They are lacking in terms of observability and monitoring compared to other products."

"Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges."

More Apache Kafka on Confluent Cloud cons

"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."

"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."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

Pricing and Cost Advice

"Regarding pricing, Apache Kafka on Confluent Cloud is not a cheap tool. The right use case would justify the cost. It might make sense if you have a high volume of data that you can leverage to generate value for the business. But if you don't have those requirements, there are likely cheaper solutions you could use instead."

"I think the pricing is fair, but Confluent requires a little bit more thinking because the price can go up really quickly when it comes to premium connectors."

"I consider that the product's price falls under the middle range category."

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

Comms Service Provider

Insurance Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	3
Large Enterprise	8

No data available

Questions from the Community

What needs improvement with Apache Kafka on Confluent Cloud?

See all answers

What is your primary use case for Apache Kafka on Confluent Cloud?

I have used Apache Kafka on Confluent Cloud for one of my projects with regard to log monitoring. My main use case for Apache Kafka on Confluent Cloud in that project was mainly streaming of the lo...

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What advice do you have for others considering Apache Kafka on Confluent Cloud?

My advice to others looking into using Apache Kafka on Confluent Cloud is that it is easier and has a low learning curve. If there is any use case regarding streaming, I would suggest starting off ...

See all answers

What is your experience regarding pricing and costs for Upsolver?

Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.

See all answers

What needs improvement with Upsolver?

There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. Furthermore, allocating C...

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What is your primary use case for Upsolver?

I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs....

See all answers

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Product Reports

Buyer's Guide

Apache Kafka on Confluent Cloud

January 2026

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Buyer's Guide

Data Integration

January 2026

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Overview

Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.

Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.

What are the key features of Apache Kafka on Confluent Cloud?

Seamless Integration: Connects with many data sources seamlessly.
Enhanced Scalability: Easily scales to manage increasing data loads.
Schema Registry: Maintains compatibility and evolution of schemas.
Offset Management: Tracks consumer progress efficiently.
Error Handling: Offers mechanisms to manage inaccuracies effectively.
Connector Support: Extensive connectors for diverse third-party tools.

What benefits should users seek in reviews?

Reliability: Consistent performance in multi-cloud environments.
Ease of Use: Simplified operations and maintenance.
Developer Efficiency: Streamlined features that save developers time.
Data Volume Handling: Capable of managing large-scale data streaming.

In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.

Confluent

Upsolver's mission is to turn engineering-intensive data lakes into easy-to-use repositories that serve 50X more data professionals compared to the status-quo today. Upsolver cuts 95% from the tedious process of preparing event data for analytics and machine learning, using a visual SQL-based interface, a cloud-native data platform and deep technology for stream-processing, indexing and eventually-consistent file systems.

Upsolver

Buyer's Guide

Apache Kafka on Confluent Cloud vs. Upsolver

December 2025

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