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Apache Kafka on Confluent Cloud vs Spring Cloud Data Flow comparison

 

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 on Confluent C...
Ranking in Streaming Analytics
10th
Average Rating
8.4
Reviews Sentiment
5.1
Number of Reviews
13
Ranking in other categories
No ranking in other categories
Spring Cloud Data Flow
Ranking in Streaming Analytics
9th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
9
Ranking in other categories
Data Integration (20th)
 

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.0%. The mindshare of Spring Cloud Data Flow is 4.7%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Ritik Varshney - PeerSpot reviewer
Enhanced data streaming with reliable features and good analytics
Apache Kafka on Confluent Cloud provides an enhanced level of reliability and resources compared to Apache Kafka alone. It offers more features which are beneficial for our clients, including cluster linking, schema registry, error handling, and dead-letter queues. It significantly improves customer and publisher satisfaction, especially with topic integration and data streaming.
NitinGoyal - PeerSpot reviewer
Has a plug-and-play model and provides good robustness and scalability
The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

Quotes from Members

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

Pros

"Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use."
"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."
"In case of huge transactions on the web or mobile apps, it helps you capture real-time data and analyze it."
"Confluent Cloud handles data volume pretty well."
"The product's installation phase is pretty straightforward for us since we know how to use 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."
"Some of the best features with Apache Kafka on Confluent Cloud are streaming and event capabilities, which are important due to scalability and resiliency."
"Apache Kafka on Confluent Cloud is more reliable and frequent to use compared to Apache Kafka."
"The dashboards in Spring Cloud Dataflow are quite valuable."
"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."
"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."
"The product is very user-friendly."
"The most valuable feature is real-time streaming."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
"The solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."
 

Cons

"Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges."
"There are some premium connectors, for example, available in Confluent, which you cannot access in the marketplace, so there are some limitations."
"There could be an in-built feature for data analysis."
"The solution is expensive."
"Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods."
"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."
"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."
"The solution's community support could be improved."
"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."
"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."
"I would improve the dashboard features as they are not very user-friendly."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
 

Pricing and Cost Advice

"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."
"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."
"If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
"The solution provides value for money, and we are currently using its community edition."
"This is an open-source product that can be used free of charge."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Manufacturing Company
7%
Computer Software Company
6%
Government
6%
Financial Services Firm
27%
Computer Software Company
16%
Retailer
7%
Insurance Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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...
What needs improvement with Apache Kafka on Confluent Cloud?
I think what I would improve about the solution is the cost, mostly. From my standpoint, it's the cost. From an engineering perspective, it works really well. There's always room for improvement. O...
What is your primary use case for Apache Kafka on Confluent Cloud?
We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publis...
What needs improvement with Spring Cloud Data Flow?
There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or r...
What is your primary use case for Spring Cloud Data Flow?
We had a project for content management, which involved multiple applications each handling content ingestion, transformation, enrichment, and storage for different customers independently. We want...
What advice do you have for others considering Spring Cloud Data Flow?
I would definitely recommend Spring Cloud Data Flow. It requires minimal additional effort or time to understand how it works, and even non-specialists can use it effectively with its friendly docu...
 

Overview

Find out what your peers are saying about Apache Kafka on Confluent Cloud vs. Spring Cloud Data Flow and other solutions. Updated: August 2025.
865,384 professionals have used our research since 2012.