Apache Kafka vs Confluent comparison

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Apache Kafka

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Executive SummaryUpdated on Dec 17, 2024

Confluent and Apache Kafka are competitors in the event streaming platform market. While Apache Kafka is known for its performance in distributed systems, Confluent gains an edge with its additional enterprise features and support, appealing to those who value added services.

Features: Confluent offers advanced features such as a Schema Registry, ksqlDB, and enhanced security options, making it suitable for enterprises needing more than standard Kafka capabilities. Apache Kafka stands out with core strengths in high throughput, reliable performance, and efficient event streaming, attracting those seeking robust fundamental features.

Room for Improvement: Confluent could improve by offering more flexible open-source options, expanding its ecosystem, and enhancing integration capabilities. Apache Kafka requires better user support, simplified deployment processes, and enhanced management tools to assist those without deep technical expertise.

Ease of Deployment and Customer Service: Confluent simplifies deployment with its cloud-native integrations and managed services, providing comprehensive support that eases scaling. On the other hand, Apache Kafka needs more manual setup and configuration, posing a challenge for users lacking technical knowledge but allowing deeper customization.

Pricing and ROI: Confluent's pricing reflects its heightened features and support services, promising a quicker ROI through reduced deployment time and higher productivity. Apache Kafka offers a cost-effective solution for teams with the technical ability to handle its complexity, highlighting affordability and resource efficiency.

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

Apache Kafka 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 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

Confluent

Ranking in Streaming Analytics

7th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.6%, up from 3.6% compared to the previous year. The mindshare of Confluent is 6.3%, down from 8.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Kafka	3.6%
Confluent	6.3%
Other	90.1%

Streaming Analytics

Featured Reviews

Amandeep Pawar

Senior Engineer at Airy 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.

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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 great thing about Apache Kafka is that we can seamlessly scale the application and, based on demand, we can add nodes without shutting down the environment."

"The most valuable feature of Apache Kafka is the clustering which is very easy to scale and we have multiple servers all over our platforms. It has been useful for stability and performance."

"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."

"Performance-wise, Kafka is better than any of the other products."

"When starting out with minimal hardware, Kafka has a guaranteed delivery mechanism that is very easy to set up and can handle very large data volumes, helping to speed up the timeline from prototype all the way to production volumes."

"Kafka is good, but Kafka as a cloud service is awesome!!"

"The stability is very nice, and we currently manage 50 million events daily."

"In my view, valuable features relate to microservices architecture and working on KStream and KSQL DB as a microservices event bus."

More Apache Kafka pros

"The client APIs are the most valuable feature."

"We ensure seamless management of Kafka through Confluent, allowing all of our Kafka activities to be handled by a third party."

"The monitoring module is impressive."

"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."

"The design of the product is extremely well built and it is highly configurable."

"The most valuable is its capability to enhance the documentation process, particularly when creating software documentation."

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

"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."

More Confluent pros

Cons

"Something that could be improved is having an interface to monitor the consuming rate."

"Kafka requires non-trivial expertise with DevOps to deploy in production at scale."

"I suggest using cloud services because the solution is expensive if you are using it on-premises."

"The support on Apache Kafka could be improved."

"We haven't seen a return on investment with Apache Kafka. It's used for a specific use case rather than cost reduction."

"GUI for Kafka infrastructure monitoring and deployment"

"It’s a trial-and-error process with no one-size-fits-all solution. Issues may arise until it’s appropriately tuned."

"The support from Apache Kafka could improve. Their engineers at times do not know what the solutions can do."

More Apache Kafka cons

"The pricing was high for our needs. We should not have to pay for features we do not use."

"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."

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

"Confluent has fallen behind in being the tool of the industry. It's taking second place to things such as Word and SharePoint and other office tools that are more dynamic and flexible than Confluent."

"It could have more themes. They should also have more reporting-oriented plugins as well. It would be great to have free custom reports that can be dispatched directly from Jira."

"They should remove Zookeeper because of security issues."

"It could have more themes. The themes in the version I'm using are very limited; they offer two to three themes."

"From the control center perspective, there is a lot of room for improvement in the visualization."

More Confluent cons

Pricing and Cost Advice

"We use the free version."

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

"Kafka is an open-source solution, so there are no licensing costs."

"This is an open-source solution and is free to use."

"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."

"Licensing issues are not applicable. Apache licensing makes it simple with almost zero cost for the software itself."

"Kafka is more reasonably priced than IBM MQ."

"Apache Kafka has an open-source pricing."

More Apache Kafka pricing and cost advice

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"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."

"The solution is cheaper than other products."

"Confluent is an expensive solution."

"Confluent is highly priced."

"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."

"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."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Outsourcing Company

12%

Manufacturing Company

10%

Computer Software Company

Financial Services Firm

14%

Retailer

12%

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	4
Large Enterprise	17

Questions from the Community

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...

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...

What needs improvement with Apache Kafka?

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 Kafka

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Overview

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

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

Uber, Netflix, Activision, Spotify, Slack, Pinterest

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

Apache Kafka vs. Confluent