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 #5 with an average rating of 8.4, while Apache is ranked #4 with an average rating of 8.0. Amazon Web Services (AWS) holds a 4.2% mindshare in SA, compared to Apache’s 8.2% 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

3,106 Views
3,106 Comparison Views

90% willing to recommend

Apache Flink

Read 19 Apache Flink reviews

4,639 Views
4,639 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: June 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Flink

June 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

5th

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 June 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.2%, down from 7.9% compared to the previous year. The mindshare of Apache Flink is 8.2%, down from 13.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	8.2%
Amazon Kinesis	4.2%
Other	87.6%

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.

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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.

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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

"The solution's technical support is flawless."

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

"There is no problem with the tool's stability."

"I find almost all features valuable, especially the timing and fast pace movement."

"The integration between Amazon Kinesis and Lambda helps us significantly."

"Amazon Kinesis has improved our ROI."

"Kinesis replaced a whole tier of servers, so we didn't need to have a server to catch the data and then send the data somewhere else, because Kinesis was the input port for very large amounts of data."

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

More Amazon Kinesis pros

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

"We are very happy with the product, and we have been able to achieve all of the use cases that we are expected to deliver for our customers."

"The end-to-end latency was drastically reduced, and our capability of handling high throughput has increased by using Flink."

"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."

"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."

"The setup was not too difficult."

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

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

More Apache Flink pros

Cons

"If there were better documentation on optimal sharding strategies then it would be helpful."

"When we had some of those slow downs, we used AWS support and I can't say that we had a great experience and they resolved the issues, but they looked into some of the flow downs and ultimately we just decided there was nothing we could do."

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

"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."

"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."

"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."

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

More Amazon Kinesis cons

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

"The solution could be more user-friendly."

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

"Failure is another area where it is a bit rigid or not that flexible."

"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."

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

"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."

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

More Apache Flink cons

Pricing and Cost Advice

"The tool's pricing is cheap."

"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."

"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."

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

"Under $1,000 per month."

"The solution's pricing is fair."

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

More Amazon Kinesis pricing and cost advice

"It's an open source."

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

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

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

"It's an open-source solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

12%

Manufacturing Company

Construction Company

Financial Services Firm

19%

Retailer

13%

Computer Software Company

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	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 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

Google Cloud Dataflow vs Apache Flink

Compared 6% of the time

Databricks vs Apache Flink

Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

June 2026

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Buyer's Guide

Apache Flink

June 2026

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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

June 2026

Free Report: Amazon Kinesis vs. Apache Flink

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