Amazon Kinesis vs Apache Flink comparison

Why PeerSpot?

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #11 with an average rating of 8.0, while Apache is ranked #4 with an average rating of 8.0. Amazon Web Services (AWS) holds a 3.8% mindshare in SA, compared to Apache’s 6.9% 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

3,589 Views
3,589 Comparison Views

90% willing to recommend

Apache Flink

Read 19 Apache Flink reviews

5,311 Views
5,311 Comparison Views

95% willing to recommend

Amazon Kinesis

Apache Flink

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Flink

September 2026

Download the complete report

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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

11th

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 October 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 3.8%, down from 6.6% compared to the previous year. The mindshare of Apache Flink is 6.9%, down from 14.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	6.9%
Amazon Kinesis	3.8%
Other	89.3%

Streaming Analytics

Featured Reviews

reviewer1480695

Director of Software Development at a tech vendor with 10,001+ employees

Has enabled real-time processing of critical event streams with seamless cloud integration

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 us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there. There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.

Read full review

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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

"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."

"I have worked in companies that build tools in-house. They face scaling challenges."

"The most valuable feature of Amazon Kinesis is real-time data streaming."

"We have been able to drop our costs for ingesting data by about 60 to 70%."

"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."

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

"The product's initial setup phase is not difficult because we are using the tool on the cloud."

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

"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."

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

"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. We use Apache Flink to control our clients' installations."

"Flink moved on to becoming a standard technology for location platform."

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

"Apache Flink provides faster and low-cost investment for me; I find it to have low hardware requirements, and it's faster with low code, meaning it's easy to understand for moving the streaming data."

"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

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

"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."

"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."

"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."

"The technical support could be improved. They tend to send you back to the documentation."

"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"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."

More Amazon Kinesis cons

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

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

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

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

"In a future release, they could improve on making the error descriptions more clear."

"Flink has become a lot more stable but the machine learning library is still not very flexible."

"There is room for improvement in the initial setup process."

"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."

More Apache Flink cons

Pricing and Cost Advice

"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."

"Under $1,000 per month."

"The product falls on a bit of an expensive side."

"Amazon Kinesis is an expensive solution."

"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."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

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

"The solution's pricing is fair."

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."

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

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

"It's an open-source solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

Manufacturing Company

Outsourcing Company

Financial Services Firm

16%

Retailer

14%

Computer Software Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

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

Questions from the Community

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.

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What needs improvement with Amazon Kinesis?

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What is your primary use case for Amazon Kinesis?

We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To p...

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

What advice do you have for others considering Apache Flink?

See all answers

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Compared 6% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

October 2026

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Buyer's Guide

Apache Flink

October 2026

Download Apache Flink product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Flink

Overview

Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.

Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.

What are the key features of Amazon Kinesis?

Real-time Data Streaming: Enables live data processing and analytics.
Seamless AWS Integration: Ensures smooth operation with AWS services.
High Throughput: Consistent 2Mbps rate supports large volumes of data.
Auto-scaling: Automatically adjusts to data load.
Fault Tolerance: Maintains system reliability and data integrity.
Lambda Function Processing: Processes data efficiently using Lambda.

What benefits or ROI should users expect?

Scalable Data Management: Handles data influx effortlessly.
Efficiency: Streamlines data flows without impacting performance.
Cost-effectiveness: Delivers value for budget-conscious users.
Low Latency: Provides real-time insights and reactions.
Multi-language Support: Accommodates various development needs.

In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.

Amazon Web Services (AWS)

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

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 2026

Free Report: Amazon Kinesis vs. Apache Flink

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