Try our new research platform with insights from 80,000+ expert users

Amazon MSK vs Apache Flink comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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
7.4
Reviews Sentiment
7.1
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Apache Flink
Ranking in Streaming Analytics
5th
Average Rating
7.8
Reviews Sentiment
6.9
Number of Reviews
18
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Amazon MSK is 6.8%, down from 9.7% compared to the previous year. The mindshare of Apache Flink is 13.9%, up from 9.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

FNU AKSHANSH - PeerSpot reviewer
Streamlines our processes, and we don't need to configure any VPCs; it's automatic
We don't have many use cases involving ingesting large amounts of data and scaling up and down. We have a clear understanding of our data volume, which remains relatively constant throughout the week. While we're aware of other features Amazon MSK offers, we feel confident in our current setup. If our requirements change significantly in the future, we'll reassess our needs and consider adopting Amazon MSK.
Aswini Atibudhi - PeerSpot reviewer
Enables robust real-time data processing but documentation needs refinement
Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every ( /products/every-reviews ) software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging ( /categories/debugging ) and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

Quotes from Members

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

Pros

"Amazon MSK's scalability is very good."
"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."
"The solution's technical support was helpful."
"Overall, it is very cost-effective based on the workflow."
"The scalability and usability are quite remarkable."
"Amazon MSK has significantly improved our organization by building seamless integration between systems."
"It offers good stability."
"It is a stable product."
"The setup was not too difficult."
"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."
"Easy to deploy and manage."
"Allows us to process batch data, stream to real-time and build pipelines."
"Apache Flink's best feature is its data streaming tool."
"The ease of usage, even for complex tasks, stands out."
"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."
 

Cons

"It should be more flexible, integration-wise."
"Horizontal scale-out is actually not easy, making it an area where improvements are required."
"One of the reasons why we prefer Kafka is because the support is a little bit difficult to manage with Amazon MSK."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"The cost of using Amazon MSK is high, which is a significant disadvantage, as the increase in cloud costs by 50% to 60% does not justify the savings."
"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."
"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."
"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."
"Apache Flink should improve its data capability and data migration."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"The machine learning library is not very flexible."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Apache Flink's documentation should be available in more languages."
"In a future release, they could improve on making the error descriptions more clear."
"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."
 

Pricing and Cost Advice

"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."
"The platform has better pricing than one of its competitors."
"The price of Amazon MSK is less than some competitor solutions, such as Confluence."
"Apache Flink is open source so we pay no licensing for the use of the software."
"It's an open source."
"The solution is open-source, which is free."
"It's an open-source solution."
"This is an open-source platform that can be used free of charge."
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
859,579 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
22%
Computer Software Company
17%
Manufacturing Company
5%
Retailer
5%
Financial Services Firm
23%
Computer Software Company
14%
Manufacturing Company
7%
Retailer
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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.
What needs improvement with Amazon MSK?
The cost of using Amazon MSK is high, which is a significant disadvantage, as the increase in cloud costs by 50% to 60% does not justify the savings. There were no other notable issues.
What is your primary use case for Amazon MSK?
We used Amazon MSK to manage high-volume third-party data entering our system. It served as a buffer when our system was unable to consume data at high speeds in real-time. The data initially went ...
What do you like most about Apache Flink?
The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. ...
What is your experience regarding pricing and costs for Apache Flink?
The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.
What needs improvement with Apache Flink?
Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool. There is a need for increased awareness and education, especially around best ...
 

Comparisons

 

Also Known As

Amazon Managed Streaming for Apache Kafka
Flink
 

Overview

 

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
Find out what your peers are saying about Amazon MSK vs. Apache Flink and other solutions. Updated: June 2025.
859,579 professionals have used our research since 2012.