Amazon MSK vs Spring Cloud Data Flow comparison

Amazon Web Services (AWS) and Broadcom are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #6 with an average rating of 7.2, while Broadcom is ranked #10 with an average rating of 7.8. Amazon Web Services (AWS) holds a 5.6% mindshare in SA, compared to Broadcom’s 4.6% mindshare. Additionally, 93% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Amazon MSK

Read 14 Amazon MSK reviews

2,391 Views
2,343 Comparison Views

93% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,991 Views
1,346 Comparison Views

88% willing to recommend

Amazon MSK

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Spring Cloud Data Flow and Amazon MSK focus on different aspects of real-time data processing. While Spring Cloud Data Flow has competitive pricing and deployment flexibility, Amazon MSK offers a wider range of integrations, making it more appealing for large-scale deployments.

Features: Spring Cloud Data Flow stands out for its flexible data flow orchestration and support for various programming languages. It effectively uses Spring components for microservices management and offers a simple programming model with auto-configuration. Amazon MSK provides seamless Apache Kafka cluster management, robust AWS integration, and features like high throughput and low latency, making it suitable for extensive data ecosystems.

Room for Improvement: Spring Cloud Data Flow could enhance its graphical environment for data visualization and offer more off-the-shelf components for easier implementation. Improvements in custom component development and a more intuitive interface would benefit users. Amazon MSK might expand features for non-AWS services, improve cost efficiency for smaller workloads, and simplify the setup for complex server communications.

Ease of Deployment and Customer Service: Spring Cloud Data Flow offers rapid deployment across various environments and has strong community support with detailed documentation. Amazon MSK excels at reducing operational complexity with its managed Kafka service and benefits from AWS's reliable customer support, providing effective problem resolution.

Pricing and ROI: Spring Cloud Data Flow leverages open-source technologies for cost-effective scaling, appealing to budget-conscious projects with promising ROI. Amazon MSK may have higher costs, but its streamlined operations and AWS integration justify the investment for businesses prioritizing scalability and comprehensive features.

To learn more, read our detailed Amazon MSK vs. Spring Cloud Data Flow Report (Updated: September 2025).

Buyer's Guide

Amazon MSK vs. Spring Cloud Data Flow

September 2025

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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

Amazon MSK

Ranking in Streaming Analytics

6th

Average Rating

7.2

Reviews Sentiment

6.5

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 (22nd)

Mindshare comparison

As of November 2025, in the Streaming Analytics category, the mindshare of Amazon MSK is 5.6%, down from 8.7% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.6%, down from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Amazon MSK	5.6%
Spring Cloud Data Flow	4.6%
Other	89.8%

Streaming Analytics

Featured Reviews

SYED SHAAZ

Co-Founder & CTO at Photios AI

Improved data streaming and integration challenges prompt search for alternatives

The integration capabilities of Amazon MSK are not very flexible. If you have your own self-managed Kafka, that helps significantly because you can set up configurations. We are considering self-managed Kafka since our product is only one year old. The Kafka integrations are fine, but the configurations are an issue. The only issue with Amazon MSK that we are facing is the configurations. There are preset configurations and limited configurations that we can set for our unique use case. The product could improve by allowing us to set different configurations. I would also like to see Amazon MSK improve in the area of connectors. We are considering Confluent Cloud because they have many more connectors. They have KSQL DB and governance features. It is slightly costlier, but Confluent offers more flexibility with their connectors.

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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.

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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 is a stable product."

"Amazon MSK's separation of concerns and ease of creating and deploying new features are highly valuable. It just requires to assign them to the topic, and then anyone who needs to consume these messages can do so directly from Amazon MSK. This separation of concerns makes it very convenient, especially for new feature development, as developers can easily access the messages they need without having to deal with complex server communications or protocol setups."

"Amazon MSK has significantly improved our organization by building seamless integration between systems."

"Amazon MSK has contributed positively to our real-time analytics capabilities because Fortis's dashboards have dashboard health that needs to be maintained, user logs that need to be maintained, and usage tracking."

"It provides installations, scaling, and other functionalities straight out of the box."

"What I appreciate most about Amazon MSK is that it doesn't require extensive concern about the configurations; it starts checking how the brokers are functioning, and automatically, Amazon MSK tries to resolve all the problems."

"Amazon MSK has good integration because our team has been undergoing significant changes. Coupling it with MSK within AWS is helpful. We don't have to set up additionals or monitor external environments. This"

"Amazon MSK's scalability is very good."

More Amazon MSK pros

"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 best thing I like about Spring Cloud Data Flow is its plug-and-play model."

"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."

"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 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."

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

Cons

"The only issue with Amazon MSK that we are facing is the configurations. There are preset configurations and limited configurations that we can set for our unique use case."

"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."

"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."

"Horizontal scale-out is actually not easy, making it an area where improvements are required."

"We need to create connectors in Amazon MSK, but there are no default connectors in AWS for that purpose."

"The downside of Amazon MSK is that when I needed to make a small change in the MSK configuration, it wasn't possible. I had to remove and drop all the clusters and recreate them again, which is complicated in a production environment."

"In my opinion, there are areas in Amazon MSK that could be improved, particularly in terms of configuration. Initially setting it up and getting it connected was quite challenging. The naming conventions for policies were updated by AWS, and some were undocumented, leading to confusion with outdated materials. It took us weeks of trial and error before discovering new methods through hidden tutorials and official documentation."

More Amazon MSK cons

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

"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."

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

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

"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."

"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."

Pricing and Cost Advice

"The platform has better pricing than one of its competitors."

"The price of Amazon MSK is less than some competitor solutions, such as Confluence."

"When you create a complete enterprise-driven architecture that is deployable on an enterprise scale, I would say that the prices of Amazon MSK and Confluent Platform become comparable."

"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."

"This is an open-source product that can be used free of charge."

"The solution provides value for money, and we are currently using its community edition."

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Computer Software Company

14%

Manufacturing Company

Retailer

Financial Services Firm

24%

Computer Software Company

14%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	7
Large Enterprise	4

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

Questions from the Community

What do you like most about Amazon MSK?

Amazon MSK has significantly improved our organization by building seamless integration between systems.

See all answers

What needs improvement with Amazon MSK?

See all answers

What is your primary use case for Amazon MSK?

We are recently working with Amazon MSK at Fortis, where we have multiple dashboards in our revenue intelligence platform. We are streaming data from different apps into those dashboards. The data ...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

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Compared 14% of the time

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Compared 7% of the time

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Compared 6% of the time

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Compared 8% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 6% of the time

Apache Kafka vs Spring Cloud Data Flow

Compared 5% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon MSK

November 2025

Download Amazon MSK product report

Buyer's Guide

Spring Cloud Data Flow

October 2025

Download Spring Cloud Data Flow product report

Also Known As

Amazon Managed Streaming for Apache Kafka

No data available

Overview

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

Amazon Web Services (AWS)

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

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

Information Not Available

Buyer's Guide

Amazon MSK vs. Spring Cloud Data Flow

September 2025

Free Report: Amazon MSK vs. Spring Cloud Data Flow

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