Apache Flink vs Confluent comparison

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Apache Flink

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Executive SummaryUpdated on Dec 17, 2024

Confluent and Apache Flink compete in the data streaming and processing domain. Confluent generally takes the upper hand in interconnectivity and integrated solutions, whereas Apache Flink excels in robust processing features, scalability, and advanced stream analytics.

Features: Confluent leverages its comprehensive Kafka integration, offering seamless data streams, scalability, and a variety of connectors. Its user-friendly interface and integrated platform make it a leading choice for enterprises seeking efficient stream processing. Apache Flink stands out with its advanced real-time data processing features, low-latency operations, and stateful event processing capabilities, ideal for sophisticated data computations. The checkpointing and savepointing features in Flink enable effective state management, allowing recovery and continuity in case of failures.

Room for Improvement: Confluent could benefit from enhanced documentation and reducing initial setup costs to lower entry barriers. While its integrations are comprehensive, further expansion of connector support would be advantageous. For Apache Flink, improving user support and providing more tailored deployment assistance would address the needs of less technically proficient users. Simplifying its setup process and enhancing community engagement for broader technical support could also be beneficial.

Ease of Deployment and Customer Service: Confluent offers straightforward deployment, particularly within Kafka environments, supported by a solid customer service model that enhances operational efficiency. Apache Flink provides flexible deployment options, accommodating bespoke solutions with the requirement of technical proficiency for optimal setup. Its adaptability allows for advanced customization but demands in-depth technical knowledge for seamless implementation.

Pricing and ROI: Confluent's initial setup costs are higher due to its integrated platform, but it often justifies this with strong ROI through enhanced efficiency in Kafka-based implementations. Apache Flink offers a cost-effective open-source model, appealing to organizations capable of leveraging its advanced functionalities for complex real-time analytics, providing financial flexibility and substantial ROI.

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

Apache Flink vs. Confluent

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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

Confluent

Ranking in Streaming Analytics

9th

Average Rating

8.2

Reviews Sentiment

6.3

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 Apache Flink is 8.2%, down from 13.7% compared to the previous year. The mindshare of Confluent is 6.6%, down from 8.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	8.2%
Confluent	6.6%
Other	85.2%

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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PavanManepalli

AVP - Sr Middleware Messaging Integration Engineer at Wells Fargo

Has supported streaming use cases across data centers and simplifies fraud analytics with SQL-based processing

I recommend that Confluent should improve its solution to keep up with competitors in the market, such as Solace and other upcoming tools such as NATS. Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools. They need to improve in that direction by not only reducing costs but also providing better solutions for the problems customers face to avoid frustrations, whether through future enhancement requests or ensuring product stability. The cost should be worked on, and they should provide better solutions for customers. Solutions should focus on hierarchical topics; if a customer has different types of data and sources, they should be able to send them to the same place for analytics. Currently, Confluent requires everything to send to the same topic, which becomes very large and makes running analytics difficult. The hierarchy of topics should be improved. This part is available in MQ and other products such as Solace, but it is missing in Confluent, leading many in capital markets and trading to switch to Solace. In terms of stability, it is not the stability itself that needs improvement but rather the delivery semantics. Other products offer exactly-once delivery out of the box, whereas Confluent states it will offer this but lacks the knobs or levers for tuning configurations effectively. Confluent has hundreds of configurations that application teams must understand, which creates a gap. Users are often unaware of what values to set for better performance or to achieve exactly-once semantics, making it difficult to navigate through them. Delivery semantics also need to be worked on.

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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 setup was not too difficult."

"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."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

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

"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."

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

"Among all of this, if I would talk about streaming, Apache Flink wins hands down, but there are other products like Apache Pulsar which I have no idea."

"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."

More Apache Flink pros

"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."

"Their tech support is amazing; they are very good, both on and off-site."

"I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and tools."

"We primarily use Confluent for service desk and task management, and it is also good for knowledge base management."

"As an enterprise organization, data availability is critical and Confluent provides that SLA support."

"To date, we have seen improvements in performance and scalability, so we recommend this solution."

"Confluent is an amazing tool that is highly configurable, integrates very well with Jira, and lets you create nice documentation for various products while also supporting reporting and online content hosting."

"The most valuable feature that we are using is the data replication between the data centers allowing us to configure a disaster recovery or software. However, is it's not mandatory to use and because most of the features that we use are from Apache Kafka, such as end-to-end encryption. Internally, we can develop our own kind of product or service from Apache Kafka."

More Confluent pros

Cons

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

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

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

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

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

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

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

"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

"One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it challenging to manage access at scale if we have many different data sources."

"Confluent's price needs improvement."

"It could be improved by including a feature that automatically creates a new topic and puts failed messages."

"The solution could have an extra plugin or upgrading feature. In addition, it could have more integration with different platforms and be more compatible."

"The beginner who doesn't know how to work on HTML will struggle as when you create spaces in the Confluent, if you want to have some meeting notes or anything else, you need to know HTML and which HTML tags to include."

"They should remove Zookeeper because of security issues."

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."

"Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools."

More Confluent cons

Pricing and Cost Advice

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

"It's an open-source solution."

"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."

"Confluent is highly priced."

"You have to pay additional for one or two features."

"Confluent is an expensive solution as we went for a three contract and it was very costly for us."

"It comes with a high cost."

"The solution is cheaper than other products."

"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."

"Confluent is an expensive solution."

"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Retailer

13%

Computer Software Company

Manufacturing Company

Financial Services Firm

16%

Retailer

11%

Computer Software Company

Manufacturing 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	6
Midsize Enterprise	4
Large Enterprise	17

Questions from the Community

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.

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...

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...

What is your experience regarding pricing and costs for Confluent?

They charge a lot for scaling, which makes it expensive.

What needs improvement with Confluent?

What is your primary use case for Confluent?

The main use cases for Confluent are log aggregation and streaming. I'm familiar with Confluent stream processing with KSQL. KSQL helps in terms of data analytics strategies because if we are the d...

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Product Reports

Apache Flink

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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

Confluent offers scalable, open-source flexibility and seamless data replication, supported by strong cloud integration. Key features like Kafka Connect and real-time processing make it valuable for data streaming projects while ensuring high availability with a Multi-Region Cluster.

Confluent is a robust data streaming platform that enables efficient management and integration of real-time data pipelines. Its message-driven architecture and fault tolerance provide reliability, while a user-friendly dashboard and connectors support diverse data sources. Cloud integration reduces costs, and extensive documentation, plugins, and monitoring capabilities enhance collaboration and revision management. Despite some areas needing improvement, including security in the SaaS version and integration flexibility, Confluent remains a staple in industries requiring vast data processing and task automation.

What are Confluent's key features?

Scalability: Handles large volumes of data, ensuring continuous performance.
Kafka Connect: Enables seamless data integration across multiple systems.
Real-time Processing: Facilitates instant data insights for rapid decision-making.
Multi-Region Cluster: Maintains data availability across different geographical locations.
Cloud Integration: Reduces operational costs and enhances deployment flexibility.
User Dashboard: Simplifies management with an intuitive interface.

What benefits and ROI should users consider in reviews?

Cost-Efficiency: Improved processes reduce operational expenses.
Data Reliability: Ensures consistent and accurate data handling.
Increased Collaboration: Boosts team efficiency with comprehensive documentation and management tools.
Scalability: Supports business growth without compromising performance.
Integration Flexibility: Seamlessly connects with diverse data sources and systems.

Confluent is commonly implemented in finance, insurance, and software industries for applications like fraud detection, ETL tasks, and enterprise communication. It supports real-time data processing, project management, and task automation, often integrating with project management tools like Jira, providing valuable solutions for business processes.

Sample Customers

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

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost

Apache Flink vs. Confluent