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

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

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 August 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.8%, up from 3.5% compared to the previous year. The mindshare of Confluent is 6.4%, 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	3.8%
Confluent	6.4%
Other	89.8%

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

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

"I appreciate that Apache Kafka is fast and secure thanks to implementing it with AWS, allowing me to secure it on a high level."

"It seemed pretty stable and didn't have any issues at all."

"The solution is scalable, and we have over a thousand users using this solution and will most likely increase the number of users because we have tested 100,000 messages per second, which is impressive."

"This is a system for email and other small devices, and there has been a relay of transactions continuously over the last two years it has been in production."

"It is a useful way to maintain messages and to manage offset from our consumers."

"The open-source version is relatively straightforward to set up and only takes a few minutes."

"This solution is robust and delivers messages quickly."

More Apache Kafka pros

"We mostly use the solution's message queues and event-driven architecture."

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

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

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

"The client APIs are the most valuable feature."

"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."

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

More Confluent pros

Cons

"In the data sharing space, the performance of Apache Kafka could be improved. The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds."

"Apache Kafka has performance issues that cause it to lag."

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

"Apache Kafka can improve by adding a feature out of the box which allows it to deliver only one message."

"I would like to see real-time event-based consumption of messages rather than the traditional way through a loop."

"The solution's initial setup process was complex."

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

"Apache Kafka can improve by making the documentation more user-friendly. It would be beneficial if we could explain to customers in more detail how the solution operates but the documentation get highly technical quickly. For example, if they had a simple page where we can show the customers how it works without the need for the customer to have a computer science background."

More Apache Kafka cons

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

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

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

"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."

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

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

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

"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."

More Confluent cons

Pricing and Cost Advice

"Apache Kafka is an open-source solution."

"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."

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

"I was using the product's free version."

"This is an open-source version."

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

"I would not subscribe to the Confluent platform, but rather stay on the free open source version. The extra cost wasn't justified."

"Apache Kafka is an open-source solution."

More Apache Kafka pricing and cost advice

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

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

"It comes with a high cost."

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

"The solution is cheaper than other products."

"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."

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

"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

17%

Outsourcing Company

12%

Manufacturing Company

10%

Construction Company

Financial Services Firm

15%

Retailer

12%

Computer Software Company

Manufacturing 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?

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

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Apache Kafka

August 2026

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August 2026

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