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 7.8, while Apache is ranked #5 with an average rating of 7.8. Amazon Web Services (AWS) holds a 6.7% mindshare in SA, compared to Apache’s 14.8% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 94% of Apache users who would recommend it.

Amazon Kinesis

Read 29 Amazon Kinesis reviews

2,858 Views
2,858 Comparison Views

90% willing to recommend

Apache Flink

Read 18 Apache Flink reviews

3,815 Views
3,815 Comparison Views

94% willing to recommend

Amazon Kinesis

Apache Flink

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Executive SummaryUpdated on Jul 27, 2025

Apache Flink and Amazon Kinesis are products in the data processing arena, with Amazon Kinesis gaining an edge through robust AWS integration and real-time analytics features.

Features: Apache Flink's strengths include stateful stream processing, low-latency, and high-throughput capabilities. It also supports inbuilt checkpointing and statefulness, facilitating easier state management. Meanwhile, Amazon Kinesis offers features like real-time data streaming, serverless architecture, and automatic scaling, making it a seamless choice for AWS-centric operations.

Room for Improvement: Apache Flink could benefit from simplified deployment processes and reduced technical expertise requirements. Easier integration with cloud services would enhance Flink's utility. Amazon Kinesis might improve by enhancing its cost structure and extending its functionality outside the AWS ecosystem. Better documentation for complex use cases could appeal to a broader user base.

Ease of Deployment and Customer Service: Apache Flink requires manual configuration, potentially increasing deployment complexity and resource management demands. However, Amazon Kinesis provides more straightforward deployment due to its managed AWS services, reducing operational overhead. While both offer comprehensive customer service, Amazon Kinesis benefits from 24/7 AWS-backed support.

Pricing and ROI: Apache Flink as an open-source solution can offer lower long-term costs if companies have the expertise to manage it. Amazon Kinesis employs a pay-as-you-go pricing model, offering rapid setup and scaling, which can lead to a quicker ROI within an AWS-oriented framework.

To learn more, read our detailed Amazon Kinesis vs. Apache Flink Report (Updated: September 2025).

Buyer's Guide

Amazon Kinesis vs. Apache Flink

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 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

5th

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 6.7%, down from 10.5% compared to the previous year. The mindshare of Apache Flink is 14.8%, up from 10.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Amazon Kinesis	6.7%
Apache Flink	14.8%
Other	78.5%

Streaming Analytics

Featured Reviews

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

Used for media streaming and live-streaming data

It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.

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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

"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."

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

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

"The solution works well in rather sizable environments."

"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."

"The integration capabilities of the product are good."

"Great auto-scaling, auto-sharing, and auto-correction features."

"The solution's technical support is flawless."

More Amazon Kinesis pros

"Easy to deploy and manage."

"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."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

"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 setup was not too difficult."

"It is user-friendly and the reporting is good."

"Apache Flink's best feature is its data streaming tool."

"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."

More Apache Flink pros

Cons

"We were charged high costs for the solution’s enhanced fan-out feature."

"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."

"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."

"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."

"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."

"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."

"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."

"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."

More Amazon Kinesis cons

"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."

"The machine learning library is not very flexible."

"The solution could be more user-friendly."

"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

"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."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

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

"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."

More Apache Flink cons

Pricing and Cost Advice

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

"The tool's pricing is cheap."

"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."

"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."

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

"The solution's pricing is fair."

"Amazon Kinesis is an expensive solution."

"Under $1,000 per month."

More Amazon Kinesis pricing and cost advice

"It's an open-source solution."

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

"It's an open source."

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

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

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Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

17%

Manufacturing Company

Comms Service Provider

Financial Services Firm

21%

Retailer

11%

Computer Software Company

11%

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	11

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?

Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...

See all answers

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. ...

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 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 ...

See all answers

Comparisons

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Compared 19% of the time

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Compared 9% of the time

Databricks vs Amazon Kinesis

Compared 8% of the time

Amazon MSK vs Amazon Kinesis

Compared 6% of the time

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Spring Cloud Data Flow vs Apache Flink

Compared 19% of the time

Databricks vs Apache Flink

Compared 13% of the time

Confluent vs Apache Flink

Compared 9% of the time

Azure Stream Analytics vs Apache Flink

Compared 9% of the time

Google Cloud Dataflow vs Apache Flink

Compared 8% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

October 2025

Download Amazon Kinesis product report

Buyer's Guide

Apache Flink

October 2025

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

September 2025

Free Report: Amazon Kinesis vs. Apache Flink

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