Amazon Kinesis vs Apache Flink comparison

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #2 with an average rating of 7.9, while Apache is ranked #4 with an average rating of 8.0. Amazon Web Services (AWS) holds a 5.2% mindshare in SA, compared to Apache’s 10.9% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 95% of Apache users who would recommend it.

Amazon Kinesis

Read 29 Amazon Kinesis reviews

2,513 Views
2,513 Comparison Views

90% willing to recommend

Apache Flink

Read 19 Apache Flink reviews

3,771 Views
3,771 Comparison Views

95% willing to recommend

Amazon Kinesis

Apache Flink

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Executive SummaryUpdated on Feb 8, 2026

Apache Flink and Amazon Kinesis compete in real-time data processing. Apache Flink offers more advanced stream processing features, while Amazon Kinesis excels in service integration within the AWS ecosystem.

Features: Apache Flink is recognized for low-latency processing, sophisticated event-time handling, and complex data stream analysis. It supports high throughput and precise time processing. Amazon Kinesis provides seamless AWS integration for scalable data stream processing, handles real-time analytics efficiently, and offers ease of use with auto-scaling and managed infrastructure.

Room for Improvement: Apache Flink could benefit from simplified deployment processes and enhanced user documentation for non-experts. Its community support, while passionate, lacks the comprehensive resources of paid services. Amazon Kinesis might improve by offering more flexible pricing models and increasing third-party tool integration options beyond AWS services. Enhancing its real-time processing capabilities for extremely high-volume data scenarios could also be beneficial.

Ease of Deployment and Customer Service: Amazon Kinesis is simple to deploy within AWS, offering extensive support documentation and robust customer service. Apache Flink provides flexibility for various platform deployments but requires greater technical expertise to harness its full potential, relying heavily on community support for troubleshooting.

Pricing and ROI: Apache Flink, as an open-source solution, has lower initial setup costs but incurs expenses in complex custom deployments. It offers high ROI in projects needing intricate data transformations. Amazon Kinesis has pricing tied to data throughput, potentially leading to higher costs but provides excellent value through AWS integration efficiency, which can justify the investment for businesses heavily relying on AWS services.

To learn more, read our detailed Amazon Kinesis vs. Apache Flink Report (Updated: March 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Flink

March 2026

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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 Kinesis

Ranking in Streaming Analytics

2nd

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Flink

Ranking in Streaming Analytics

4th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 5.2%, down from 9.0% compared to the previous year. The mindshare of Apache Flink is 10.9%, down from 12.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Amazon Kinesis	5.2%
Apache Flink	10.9%
Other	83.9%

Streaming Analytics

Featured Reviews

Chandra Mohan Dhanasekaran

AWS Cloud Architect at a healthcare company with 10,001+ employees

Real-time streaming and seamless integration enhance workloads with room for competitive pricing improvements

Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic. The throughput is convenient for processing volumes out of the box and does not require complex configurations. It also provides auto-scaling with different partition keys into various shards. Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.

Read full review

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

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 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 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.

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

"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."

"One of the best features of Amazon Kinesis is the multi-partition."

"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."

"The scalability is pretty good."

"The most valuable feature is that it has a pretty robust way of capturing things."

"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."

"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."

"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."

More Amazon Kinesis pros

"The documentation is very good."

"Allows us to process batch data, stream to real-time and build pipelines."

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

"The ease of usage, even for complex tasks, stands out."

"What I appreciate best about Apache Flink is that it's open source and geared towards a distributed stream processing framework."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

"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."

"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."

More Apache Flink pros

Cons

"The price is not much cheaper. So, there is room for improvement in the pricing."

"The tool should focus on having an alert system rather than having to use a third-party solution."

"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."

"In order to do a successful setup, the person handling the implementation needs to know the solution very well. You can't just come into it blind and with little to no experience."

"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."

"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."

"We are contemplating moving away from Amazon Kinesis primarily because of the cost."

"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."

More Amazon Kinesis cons

"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."

"There is a learning curve. It takes time to learn."

"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."

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

"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."

"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."

"There are more libraries that are missing and also maybe more capabilities for machine learning."

"Apache Flink's documentation should be available in more languages."

More Apache Flink cons

Pricing and Cost Advice

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."

"The tool's pricing is cheap."

"Amazon Kinesis is an expensive solution."

"The fee is based on the number of hours the service is running."

"The tool's entry price is cheap. However, pricing increases with data volume."

"Under $1,000 per month."

"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."

More Amazon Kinesis pricing and cost advice

"It's an open source."

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

"The solution is open-source, which is free."

"Apache Flink is open source so we pay no licensing for the use of the software."

"It's an open-source solution."

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Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

14%

Manufacturing Company

Educational Organization

Financial Services Firm

19%

Retailer

13%

Computer Software Company

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	10
Large Enterprise	9

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	12

Questions from the Community

What do you like most about Amazon Kinesis?

Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.

See all answers

What is your experience regarding pricing and costs for Amazon Kinesis?

Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.

See all answers

What needs improvement with Amazon Kinesis?

We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for u...

See all answers

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.

See all answers

What needs improvement with Apache Flink?

Apache could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

See all answers

What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

See all answers

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Compared 9% of the time

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Compared 9% of the time

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Compared 7% of the time

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Compared 11% of the time

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Compared 9% of the time

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Compared 9% of the time

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Compared 8% of the time

Google Cloud Dataflow vs Apache Flink

Compared 6% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

March 2026

Download Amazon Kinesis product report

Buyer's Guide

Apache Flink

March 2026

Download Apache Flink product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Flink

Overview

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Amazon Web Services (AWS)

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “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.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

Apache

Sample Customers

Zillow, Netflix, Sonos

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

Buyer's Guide

Amazon Kinesis vs. Apache Flink

March 2026

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