Apache Flink vs Apache Kafka comparison

Why PeerSpot?

Apache Flink and Apache Kafka are both solutions in the Streaming Analytics category. Apache Flink is ranked #4 with an average rating of 8.0, while Apache Kafka is ranked #3 with an average rating of 8.7. Apache Flink holds a 6.9% mindshare in SA, compared to Apache Kafka’s 3.6% mindshare. Additionally, 95% of Apache Flink users are willing to recommend the solution, compared to 96% of Apache Kafka users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

5,311 Views
5,311 Comparison Views

95% willing to recommend

Apache Kafka

Read 92 Apache Kafka reviews

7,270 Views
3,708 Comparison Views

96% willing to recommend

Apache Flink

Apache Kafka

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Executive SummaryUpdated on Dec 17, 2024

Apache Kafka and Apache Flink are both major players in the real-time data streaming domain. Kafka seems to have the upper hand due to its established market presence and extensive community support.

Features: Kafka offers replication, partitioning, and seamless integration with systems like Apache Spark, which ensures high availability and scalability. Real-time streaming and persistent messaging are also key features. Flink shines with stateful stream processing, low latency, and excellent event time processing. Its inbuilt checkpointing ensures robust state management.

Room for Improvement: Kafka could improve its GUI tools for monitoring and reduce dependency on ZooKeeper. User feedback suggests a need for better consumer offset management. Flink users look for enhanced Python support, better documentation regarding integration and a simplified setup process.

Ease of Deployment and Customer Service: Kafka is predominantly deployed on-premises but also extends to cloud setups and benefits from an active open-source community. Professional support from Confluent is available. Flink is more cloud-centric, with a similar reliance on community support. Both have limited official support options, often supplemented by third-party consulting services.

Pricing and ROI: Both Kafka and Flink are open-source and primarily free to use. Additional costs may arise from commercial support like Confluent. Kafka users often report significant ROI due to its architecture, while Flink's strong community backing provides cost-free advantages. However, some users see Flink's lack of commercial support options as a limitation for enterprise applications.

To learn more, read our detailed Apache Flink vs. Apache Kafka Report (Updated: September 2026).

Buyer's Guide

Apache Flink vs. Apache Kafka

September 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

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

Apache Kafka

Ranking in Streaming Analytics

3rd

Average Rating

8.2

Reviews Sentiment

6.9

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 Apache Flink is 6.9%, down from 14.6% compared to the previous year. The mindshare of Apache Kafka is 3.6%, up from 3.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Kafka	3.6%
Apache Flink	6.9%
Other	89.5%

Streaming Analytics

Featured Reviews

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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Amandeep Pawar

Engineer Technology at Iris Software Technologies Private Limited

Event-driven architecture has improved asynchronous communication and supports high throughput

We can improve the high throughput because there are some limitations. We could add something to improve Apache Kafka. Based on my daily usage and analysis, there is a complex setup and management, which is one area requiring improvement. Apache Kafka does not have built-in message delay or scheduling capabilities. Apache Kafka cannot natively schedule messages. Limited message prioritization is also an area requiring improvement. Ordering is limited to a single partition. Large messages affect performance. Apache Kafka is optimized for many small to medium-sized messages, but large payloads increase network usage, increase disk usage, and slow down producers and consumers. I rate Apache Kafka eight out of ten instead of ten out of ten because operating an Apache Kafka cluster requires expertise in partitioning, application monitoring, and capacity planning. Self-managed deployment can become complex as the cluster grows. Pro-managed Apache Kafka services significantly reduce the operational overhead.

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 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."

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

"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."

"This is truly a real-time solution."

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

"Easy to deploy and manage."

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

"The main advantage is the turnaround time, which has been reduced drastically because of Apache Flink, and now everything is in almost real time with no waiting or lag of data in the application while machine resources are utilized much more efficiently."

More Apache Flink pros

"The most valuable feature is the speed at which the solution can be deployed."

"The most valuable feature of Apache Kafka is its versatility. It can solve many use cases or can be a part of many use cases. Its fundamental value of it is in the real-time processing capability."

"It is the performance that is really meaningful."

"The solution is stable, and the most fantastic thing about it is its throughput."

"It is a stable solution...A lot of my experience indicates that Apache Kafka is scalable."

"Kafka was at the base of our system architecture."

"The solution scales horizontally and scales better than its competitors."

"Kafka allows you to handle huge amounts of data and classify it into different categories. If you have huge amounts of data, Kafka is a very good solution for data classification."

More Apache Kafka pros

Cons

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

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

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

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

"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."

"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."

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

"One way to improve Flink would be to enhance integration between different ecosystems."

More Apache Flink cons

"The solution should be easier to manage. It needs to improve its visualization feature in the next release."

"The manageability should be improved. There are lots of things we need to manage and it should have a function that enables us to manage them all cohesively."

"We used to have problems in Kafka every three weeks and our dev ops team fixed a few issues."

"Kafka does not provide control over the message queue, so we do not know whether we are experiencing lost or duplicate messages."

"I would like to see monitoring service tools."

"Observability could be improved."

"Config management can be better."

"Managing Apache Kafka can be a challenge, but there are solutions. I used the newest release, as it seems they have removed Zookeeper, which should make it easier. Confluent provides a fully managed Kafka platform, in which the cluster does not need to be managed."

More Apache Kafka cons

Pricing and Cost Advice

"It's an open-source solution."

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

"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."

"It's an open source."

"It is approximately $600,000 USD."

"Apache Kafka is open-source and can be used free of charge."

"When starting to look at a distributed message system, look for a cloud solution first. It is an easier entry point than an on-premises hardware solution."

"Apache Kafka has an open-source pricing."

"Running a Kafka cluster can be expensive, especially if you need to scale it up to handle large amounts of data."

"The solution is open source; it's free to use."

"It's a premium product, so it is not price-effective for us."

"Apache Kafka is an open-source solution and there are no fees, but there are fees associated with confluence, which are based on subscription."

More Apache Kafka pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Retailer

14%

Computer Software Company

Comms Service Provider

Financial Services Firm

15%

Outsourcing Company

12%

Manufacturing Company

10%

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	33
Midsize Enterprise	20
Large Enterprise	51

Questions from the Community

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...

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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...

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What advice do you have for others considering Apache Flink?

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What are the differences between Apache Kafka and IBM MQ?

Apache Kafka is open source and can be used for free. It has very good log management and has a way to store the data used for analytics. Apache Kafka is very good if you have a high number of user...

See all answers

What is your experience regarding pricing and costs for Apache Kafka?

Based on my understanding for the setup and cost to set up Apache Kafka, I was not responsible for the pricing or licensing decisions. Apache Kafka itself is open source and free to use. Infrastruc...

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What needs improvement with Apache Kafka?

See all answers

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Compared 8% of the time

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Product Reports

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Apache Flink

October 2026

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Buyer's Guide

Apache Kafka

October 2026

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Also Known As

Flink

No data available

Overview

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

Apache Kafka provides scalable, high-throughput, real-time data processing. Appreciated for its open-source nature and integration capabilities, Kafka supports distributed messaging and high-volume handling with essential features like message retention, replication, and partitioning.

Apache Kafka is a powerful tool for managing efficient data streams and high volumes of asynchronous messages. Its ease of setup and robust integration options make it popular among industries requiring real-time data streaming and processing. Key features such as message retention and consumer groups cater to demanding applications, while fault-tolerant design ensures reliability. Despite its advantages, Kafka can improve in areas like duplicate management, documentation, and intuitive interfaces. Challenges in configuration and monitoring tools suggest areas for enhancement, alongside reducing complexity and resource dependency.

What are the key features of Apache Kafka?

Scalability: Efficiently handles increasing data volumes without performance loss.
High Throughput: Processes large amounts of data quickly and efficiently.
Real-time Processing: Facilitates immediate data streaming and analytics.
Fault Tolerance: Maintains operations despite failures, ensuring continuous data flow.
Open-source Nature: Offers community-driven enhancements and reduced costs.

What benefits should users look for in Apache Kafka reviews?

Robust Integration: Easily connects with various applications and systems.
Cost-effectiveness: Leverages open-source advantages for financial savings.
Reliability: Provides consistent performance with fault-tolerant mechanisms.
Scalable Infrastructure: Supports growing business needs without compromising efficiency.

Industry applications for Apache Kafka include real-time data streaming for IoT, big data management, and analytics. In finance, it supports fraud detection and transaction monitoring. Healthcare uses Kafka for patient data handling and logistics leverage its data distribution capabilities to optimize operations. Its ability to manage large-scale asynchronous communication makes it vital across sectors demanding high data throughput and reliability.

Apache

Sample Customers

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

Uber, Netflix, Activision, Spotify, Slack, Pinterest

Buyer's Guide

Apache Flink vs. Apache Kafka

September 2026

Free Report: Apache Flink vs. Apache Kafka

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