Apache Kafka on Confluent Cloud vs Upsolver comparison

Confluent and Upsolver are both solutions in the Streaming Analytics category. Confluent is ranked #11 with an average rating of 8.5, while Upsolver is ranked #20 with an average rating of 8.5. Confluent holds a 0.1% mindshare in SA, compared to Upsolver’s 0.4% 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 14 Apache Kafka on Confluent Cloud reviews

1,042 Views
10 Comparison Views

100% willing to recommend

Upsolver

Read 2 Upsolver reviews

369 Views
100 Comparison Views

100% willing to recommend

Apache Kafka on Confluent C...

Upsolver

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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: September 2025).

Buyer's Guide

Apache Kafka on Confluent Cloud vs. Upsolver

September 2025

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Helped 869,202 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

11th

Average Rating

8.6

Reviews Sentiment

3.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Integration (40th)

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.1%, up from 0.0% compared to the previous year. The mindshare of Upsolver is 0.4%, 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	0.1%
Upsolver	0.4%
Other	99.5%

Streaming Analytics

Featured Reviews

FABIO LUIS VELLOSO DA SILVA

Partner at SouJava

Has enabled asynchronous communication and real-time data processing with strong performance

The valuable features with Apache Kafka on Confluent Cloud are the messaging and the asynchronous messages; it's the basic, not advanced usage. It's only to create clusters to receive and send messages. The point is the asynchronous messages and the scalability; it is important for us. To guarantee the compliance of the architecture and the patterns for the company, to provide scalability, and to guarantee the security to send the messages. The Kafka Streams API helps with real-time data transformations and aggregations. It's very fast and helps us to create the project, guarantee the message delivery, and the performance. It's a good experience with very impressive processing and a very impressive project and product.

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Snehasish Das

Technology Leader at eTCaaS

Allows for data to be moved across platforms and different data technologies

The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies. Upsolver does this in a quick time, unlike traditional processes which are time-consuming. Additionally, it offers scalability for large volumes of data, with performance and ease of cloud-native integration.

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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

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

"Overall, I think it's a good experience. Apache Kafka can be quite complex and difficult to maintain on your own, so using Apache Kafka on Confluent Cloud makes it much easier to use it without worrying about setup and maintenance."

"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."

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

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

"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."

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

"Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use."

More Apache Kafka on Confluent Cloud pros

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

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

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

Cons

"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 are some premium connectors, for example, available in Confluent, which you cannot access in the marketplace, so there are some limitations."

"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."

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

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

"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."

"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."

More Apache Kafka on Confluent Cloud cons

"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."

"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."

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

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Manufacturing Company

Educational Organization

Government

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	3
Large Enterprise	6

No data available

Questions from the Community

What do you like most about Apache Kafka on Confluent Cloud?

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 s...

See all answers

What needs improvement with Apache Kafka on Confluent Cloud?

If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it. The clustering is a little hard for juniors and clients....

See all answers

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

We need to send a lot of asynchronous messages in this project, and we use the middleware and Apache Kafka on Confluent Cloud to guarantee asynchronous messaging between the services. We use Apache...

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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.

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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....

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Buyer's Guide

Apache Kafka on Confluent Cloud

October 2025

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Data Integration

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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

September 2025

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