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: June 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

6.8

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 Kafka is 3.9%, up from 3.0% 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 Kafka	3.9%
Confluent	6.6%
Other	89.5%

Streaming Analytics

Featured Reviews

Varuns Ug

Senior Software Developer at NIT

Event-driven workflows have improved payment processing and reduced latency across services

One area for improvement in Apache Kafka is operational complexity. Running and maintaining an Apache Kafka cluster at scale involves handling partitions, replications, retention policies, rebalancing, and monitoring, which requires strong expertise. Debugging and observability can be complex in large systems, as troubleshooting issues such as consumer lag, offset management problems, or uneven partition distribution can become challenging. The learning curve is relatively steep, requiring a good understanding of concepts such as partition, consumer group, offset commit, and delivery guarantees to avoid subtle production issues. One area where Apache Kafka could improve is the developer experience around debugging and tracing events end to end. In distributed systems, when an event passes through multiple topics and consumer services, troubleshooting can become time-consuming. Better built-in observability for tracing event flows across services would be very useful.

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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 use of Kafka's logging mechanism has been extremely beneficial for us, as it allows us to sequence messages, track pointers, and manage memory without having to create multiple copies."

"Apache Kafka is scalable. It is easy to add brokers."

"The processing power of Apache Kafka is good when you have requirements for high throughput and a large number of consumers."

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

"Excellent speeds for publishing messages faster."

"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."

"The solution can handle more speed and has horizontal scalability for both messaging, but more specifically stream processing and data enrichment."

"It is easy to configure."

More Apache Kafka pros

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

"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."

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

"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."

"The monitoring module is impressive."

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

"A person with a good IT background and HTML will not have any trouble with Confluent."

More Confluent pros

Cons

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

"Data pulling and restart ability need improving."

"The user interface is one weakness. Sometimes, our data isn't as accessible as we'd like. It takes a lot of work to retrieve the data and the index."

"The initial setup is simple, but the ongoing management becomes challenging."

"The UI used to access Kafka topics can be further improved."

"For personal preferences, since we use Managed Kafka in AWS, I would appreciate having some kind of UI integrated into Apache Kafka for connecting to it because using code to connect it is basic, but we can use a UI."

"The only reason I give Kafka as product a low rating is because there are far superior and cheaper alternatives in cloud-based solutions, where we save money on manpower, electricity, servers, datacenters, networking, etc."

"would like to see real-time event-based consumption of messages rather than the traditional way through a loop. The traditional messaging system works by listing and looping with a small wait to check to see what the messages are. A push system is where you have something that is ready to receive a message and when the message comes in and hits the partition, it goes straight to the consumer versus the consumer having to pull. I believe this consumer approach is something they are working on and may come in an upcoming release. However, that is message consumption versus message listening."

More Apache Kafka cons

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

"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."

"It would be great if the knowledge based documents in the support portal could be available for public use as well."

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

"In Confluent, there could be a few more VPN options."

"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."

"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."

"The pricing model should include the ability to pick features and be charged for them only."

More Confluent cons

Pricing and Cost Advice

"Apache Kafka is an open-sourced solution. There are fees if you want the support, and I would recommend it for enterprises. There are annual subscriptions available."

"It's a bit cheaper compared to other Q applications."

"I rate Apache Kafka's pricing a five on a scale of one to ten, where one is cheap and ten is expensive. There are no additional costs apart from the licensing fees for Apache Kafka."

"The price for the enterprise version is quite high. For on-premise, there is an annual fee, which starts at 60,000 euros, but it is usually higher than 100,000 euros. The cost for a project including the subscription is usually between 100,000 to 200,000 euros. The cost also depends on the level of support. There are two different levels of support."

"The cost can vary depending on the provider and the specific flavor or version you use. I'm not very knowledgeable about the pricing details."

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

"We use the free version."

"It is approximately $600,000 USD."

More Apache Kafka pricing and cost advice

"The solution is cheaper than other products."

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

"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."

"It comes with a high cost."

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

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

"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."

"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."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

Outsourcing 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	32
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?

From the AWS perspective, the price is on the higher side. However, if you go for Apache Kafka, it is low. From a price perspective, if you are asking about Apache Kafka, I would rate it a nine.

What needs improvement with Apache Kafka?

Apache Kafka is abundant with features which only an expert-level person will be able to manage due to the high volume and high concurrent expectations. Apache Kafka groups could introduce themes o...

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