Apache Kafka vs Confluent comparison

Read 25 Confluent reviews

6,849 Views
4,041 Comparison Views

95% willing to recommend

Apache Kafka

Comparison Buyer's Guide

Download the report

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

September 2026

Download the complete report

Helped 920,574 peers since 2012

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

6th

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 November 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.5%, down from 3.8% compared to the previous year. The mindshare of Confluent is 6.1%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Kafka	3.5%
Confluent	6.1%
Other	90.4%

Streaming Analytics

Featured Reviews

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

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.

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

"Resiliency is great and also the fact that it handles different data formats."

"Deployment is speedy."

"A great streaming platform."

"We implemented the notification system between our components, and we found that Apache Kafka performs well in scalability, improving our organization because of the scalability and the comfort of a fail-safe or disaster recovery it provides."

"Apache Kafka is a good solution with many good features but for large deployments, I would choose IBM MQ over Kafka."

"Supports more than 10,000 events/second, scalability, and replication, and it is a good product for event-driven architecture."

"The most important feature for me is the guaranteed delivery of messages from producers to consumers."

"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."

More Apache Kafka pros

"Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance."

"The documentation process is fast with the tool."

"The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category."

"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 solution can handle a high volume of data because it works and scales well."

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

"Overall, it's a great company and they have excellent software."

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

More Confluent pros

Cons

"Too much dependency on the zookeeper and leader selection is still the bottleneck for Kafka implementation."

"The solution could always add a few more features to enhance its usage."

"Pulsar gives more scalability to an even grouping, but Apache Kafka is used more if you want to send something in a time series-based. If this does not matter to you then Pulsar could be more customizable. Apache Kafka is nothing but a streaming system with local storage."

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

"The standard Kafka Java library, which is shipped with the product, is too complex for inexperienced users."

"The support on Apache Kafka could be improved."

"Kafka can allow for duplicates, which isn't as helpful in some of our scenarios."

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

More Apache Kafka cons

"It could have more integration with different platforms."

"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."

"Confluent's price needs improvement."

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

"The formatting aspect within the page can be improved and more powerful."

"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."

"It requires some application specific connectors which are lacking. This needs to be added."

More Confluent cons

Pricing and Cost Advice

"This is an open-source version."

"The solution is open source."

"Apache Kafka is an open-source solution."

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

"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."

"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."

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

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

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."

"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."

"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."

"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."

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

"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."

More Confluent pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

920,574 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

13%

Manufacturing Company

10%

Construction Company

Financial Services Firm

14%

Retailer

12%

Manufacturing Company

Construction 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...

PubSub+ Platform vs Apache Kafka

Comparisons

Compared 11% of the time

Red Hat AMQ vs Apache Kafka

Compared 9% of the time

Azure Stream Analytics vs Apache Kafka

Compared 8% of the time

Databricks vs Apache Kafka

Compared 7% of the time

Redpanda vs Apache Kafka

Compared 3% of the time

More Apache Kafka Competitors

Palantir Foundry vs Confluent

Compared 9% of the time

Azure Stream Analytics vs Confluent

Compared 7% of the time

Databricks vs Confluent

Compared 6% of the time

Amazon Kinesis vs Confluent

Compared 6% of the time

Apache Spark Streaming vs Confluent

Compared 6% of the time

More Confluent Competitors

Product Reports

Download Apache Kafka product report

Apache Kafka

October 2026

Download Confluent product report

October 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