Apache Kafka on Confluent Cloud vs Spring Cloud Data Flow comparison

Apache Kafka on Confluent Cloud vs. Spring Cloud Data Flow

Download the complete report

Helped 885,311 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

5.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

10th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (23rd)

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.5%. The mindshare of Spring Cloud Data Flow is 3.5%, down from 4.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Spring Cloud Data Flow	3.5%
Apache Kafka on Confluent Cloud	0.5%
Other	96.0%

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.

Read full review

NitinGoyal

Engineering Lead at Naukri.com

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.

Read full review

Quotes from Members

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

Pros

"Confluent Cloud handles data volume pretty well."

"Apache Kafka on Confluent Cloud is critical infrastructure for us; without it, our infrastructure costs would increase significantly, potentially amounting to hundreds of thousands of dollars each year, and its real-time capabilities accelerate speed to value and enable new use cases, providing significant business value."

"The order guarantee of Apache Kafka on Confluent Cloud and the amount of throughput it can handle are valuable; the fact that the consumer pulls the data, not the broker, makes it more resilient and more reliable compared to other technologies."

"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 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 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."

"The return on investment has been significant, especially in terms of stability, scalability, and the fact that we almost never had any issues in production."

More Apache Kafka on Confluent Cloud pros

"This product will assist us in saving costs in many ways: No longer need to continue paying high fees for proprietary software, reduce the number of software engineers needed to support the product, and achieve faster time to market by using this product for our middleware."

"The dashboards in Spring Cloud Dataflow are quite valuable."

"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 product is very user-friendly."

"The most valuable feature is real-time streaming."

"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 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."

More Spring Cloud Data Flow pros

Cons

"Although, specifically with Apache Kafka on Confluent Cloud, it was a bit more challenging to increase adoption because it's very expensive."

"Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods."

"The ability to implement request-response communication on Apache Kafka needs improvement."

"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."

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

"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."

"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."

"Improvement can be made by making it easier to build applications on the real-time stream, focusing on real-time pre-processing and anomaly detection."

More Apache Kafka on Confluent Cloud cons

"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

"The documentation on offer is not that good."

"The solution's community support could be improved."

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."

"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

"The visual user interface could use some help; it needs improvement."

"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."

"Some of the features, like the monitoring tools, are not very mature and are still evolving."

More Spring Cloud Data Flow cons

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."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

885,311 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Construction Company

16%

Financial Services Firm

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

17%

Computer Software Company

12%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	1
Large Enterprise	5

Questions from the Community

What needs improvement with Apache Kafka on Confluent Cloud?

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...

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 ...

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...

n8n vs Apache Kafka on Confluent Cloud

Comparisons

Compared 25% of the time

WarpStream vs Apache Kafka on Confluent Cloud

Compared 19% of the time

Tecton Feature Store vs Apache Kafka on Confluent Cloud

Compared 10% of the time

Cloud Security Connector for Zscaler vs Apache Kafka on Confluent Cloud

Compared 10% of the time

Conductor Cloud Rendering vs Apache Kafka on Confluent Cloud

Compared 9% of the time

More Apache Kafka on Confluent Cloud Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 17% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 5% of the time

Apache Kafka vs Spring Cloud Data Flow

Compared 5% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 4% of the time

IBM InfoSphere DataStage vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Apache Kafka on Confluent Cloud

Download Apache Kafka on Confluent Cloud product report

Spring Cloud Data Flow

Download Spring Cloud Data Flow product report

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

Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

Broadcom

Apache Kafka on Confluent Cloud vs. Spring Cloud Data Flow