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The easiest way to run Kafka on AWS. Confluent Cloud is a cloud-native, complete data streaming platform built for real-time processing and data analytics on AWS. Powered by Apache Kafka®, Apache Flink®, and Confluent's own cloud-native Kafka engine, Kora, it offers serverless stream processing, 60% lower TCO than self-managed Kafka, and 120+ pre-built connectors for services like Amazon S3, AWS Lambda, Amazon Redshift, and Amazon DynamoDB. Start fast with $1,000 in free credits and hands-on support from Confluent's Cloud Engineers to accelerate your proof of concept and power modern applications with a complete, fully managed solution.
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Confluent Cloud: Real-Time Data Streaming for AI, Analytics & Modern Apps
Maximize your Kafka on AWS, minimize your spend. Confluent Cloud is a fully managed, cloud-native, complete data streaming platform built on Apache Kafka®, Apache Flink®, and Apache Iceberg™.
It's 60% more cost-effective than self-managed Kafka, with autoscaling, 120+ pre-built connectors, and enterprise-grade support. It delivers elastic, resilient, and performant event streaming - powering real-time AI, microservices, machine learning, and analytics while helping teams modernize faster in the cloud. Get started with $1,000 in free credits - bill directly through AWS Marketplace.
Built for AWS, Any Way You Need It.
- Expert Support with Enterprise SLAs: Ensure high availability and offload Kafka operations with 99.99% uptime SLA coverage for core Kafka operations - ensuring resilience, availability, and faster resolution.
- Stream Processing with Apache Flink: Go from raw data to insights faster using a serverless Flink service to filter, join, and enrich data in real-time - no ops needed.
- Real-Time Analytics with Tableflow: Convert Kafka topics to Apache Iceberg tables to power downstream analytics across AWS services like Glue, Redshift, Athena, EMR, and SageMaker Lakehouse.
- Deeply Integrated with AWS Services: 120+ pre-built connectors make it easy to stream data to and from Amazon S3, Amazon Redshift, Amazon RDS, Amazon DynamoDB, AWS Lambda, and more - right from the AWS console. Confluent Cloud is billed directly through the AWS Marketplace, so you can draw down on your existing AWS commit and skip the paperwork.
Free $1000 Credit to Build Your PoC
Start for free with $1,000 in credits (includes $400 instantly and $600 via promo code) to build your proof of concept or a demo consumer streaming app for your team.
Free Personalized Onboarding and Engineering Support
All Confluent Cloud signups via AWS Marketplace get white-glove onboarding, architecture reviews, and on-demand training from Confluent's Cloud Engineers - at no additional cost.
*business email address is required to qualify
Highlights
- 60% Lower Total Cost of Ownership: Fully managed, elastic, and performant clusters make Confluent Cloud cheaper to run and manage than self-managed Kafka - giving you a more cost-effective alternative to managed Kafka services.
- Proof-of-Concept Support Included: Personalized architecture reviews, solutions brainstorming, connector setup assistance, and technical guidance from Confluent Engineers to help you design the right data streaming architecture for your needs.
- Complete Data Streaming Platform on AWS: Go beyond Kafka with 120+ pre-built source and sink connectors, stream processing with Apache Flink, real-time analytics with Apache Iceberg, Data Governance, and Security - all in one cloud-native solution.
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Cost/unit |
|---|---|
Confluent Consumption Unit | $0.01 |
Dimensions summary
The Confluent Consumption Unit (CCU) is a standardized measurement for resource usage across Confluent Cloud services on AWS Marketplace. CCU representes a translation or conversion of total $ spend net of discount on various Confluent Cloud resources such as CKUs, throughput, storage, as well as other services like connector tasks, CFUs, schema charge etc. The total $ spent net of discounts is converted to CCUs using the listed rate of $0.01/CCU on the listing. This consumption-based model allows for flexible scaling of Kafka infrastructure without upfront commitments.
Top-of-mind questions for buyers like you
How is Confluent's usage-based pricing calculated on AWS Marketplace?
Confluent Cloud pricing is based on actual resource consumption across Confluent's product offerings. The total consumption net of discounts in $ is then converted to Confluent Consumption Units (CCUs) using the conversion rate listed on the listing. The charges are calculated hourly and billed through your AWS account.
Are there any minimum commitment requirements for Confluent on AWS Marketplace?
There's no upfront commitment required. Customers can start small and scale their usage as needed, paying only for the resources they consume. Customers can choose to commit with Confluent in exchange for additional discounts.
What additional costs should I consider beyond the listed Confluent Consumption Unit price?
The CCU is only a conversion of the underlying charges. To understand the details, please visit the Billing and Payments section in the CC UI or use the Cost API to download the details of your usage across different CC products and services.
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Please contact us at awsteam@confluent.io
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Vendor support
To learn more about our support offerings please visit: https://confluent.io/confluent-cloud/support Technical assistance from the worlds foremost Apache Kafka experts with over 1 million hours of expertise with a paid support plan. Support plans can be added to your subscription directly from the Confluent Cloud web UI. Support portal accessible within the Confluent Cloud web UI.
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Stream Processing Framework
Native integration of Apache Flink for real-time data filtering, joining, and enrichment without operational overhead
Data Streaming Architecture
Cloud-native platform built on Apache Kafka, Apache Flink, and Apache Iceberg for event streaming and data processing
Connector Ecosystem
Supports 120+ pre-built connectors for seamless data integration with AWS services like S3, Redshift, DynamoDB, and Lambda
High Availability Infrastructure
Enterprise-grade service with 99.99% uptime SLA and autoscaling capabilities for resilient data streaming
Real-Time Analytics Transformation
Capability to convert Kafka topics to Apache Iceberg tables for downstream analytics across multiple AWS data services
Event Streaming Platform
Fully managed event streaming platform with Kafka API compatibility and support for real-time and AI applications
Connector Ecosystem
Comprehensive collection of 300+ built-in connectors to popular data systems including Snowflake, MongoDB, Amazon S3, and change data capture for databases
Deployment Flexibility
Supports multiple deployment models including Serverless, Dedicated, and Bring-Your-Own-Cloud (BYOC) options with enterprise-grade security
Performance Optimization
Engineered to maximize hardware performance potential, delivering higher throughput and lower latencies compared to traditional Kafka systems
Data Management
Automated cluster operations including zero-downtime upgrades, data and partition balancing with tiered storage for efficient long-term data retention
Messaging Platform
Distributed messaging and streaming platform supporting multiple programming languages and client libraries
Data Processing Capabilities
Supports real-time analytics, machine learning pipelines, log management, and event-driven architectures
Cloud Integration
Native integration with AWS services including Amazon S3, Amazon Kinesis, and AWS Lambda for seamless data streaming
Storage Compatibility
Supports Apache Iceberg and Delta Lake formats for advanced data lake architectures and real-time analytics
Cluster Management
Fully managed service with enterprise-grade SLAs, automatic version updates, and on-demand technical support
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Customer reviews
4.5
1 ratings
1 AWS reviews
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121 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
reviewer2711817
Improved developer velocity and seamless integration enhance real-time data handling while cost challenges remain
Reviewed on May 28, 2025
Review from a verified AWS customer
What is our primary use case?
We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publish changes about their domain business objects without too much code and work from domain teams. In this way, we can more easily provide a very robust layer of API and events.
The second use case is easier projection of data. We found that many teams were struggling to create projections and read stores with regular event buses, and Apache Kafka on Confluent Cloud helped us because of all sorts of features, such as the log architecture they have, and other features. KSQL also helped us there.
When order is more important, we rely on Apache Kafka on Confluent Cloud .
What is most valuable?
The benefits that I have seen from having a real-time architecture include better velocity for developers. That is the main one. Instead of developing many of those capabilities in each team, we can rely on Apache Kafka on Confluent Cloud to provide those functionalities we want, and the teams can focus on their own business instead of providing all sorts of APIs and dependencies to other domains, allowing everyone to run faster.
We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publish changes about their domain business objects without too much code and work from domain teams. In this way, we can more easily provide a very robust layer of API and events.
The second use case is easier projection of data. We found that many teams were struggling to create projections and read stores with regular event buses, and Apache Kafka on Confluent Cloud helped us because of all sorts of features, such as the log architecture they have. KSQL also helped us there.
What needs improvement?
I think what I would improve about the solution is the cost, mostly. From my standpoint, it's the cost. From an engineering perspective, it works really well.
There's always room for improvement. One more point is sometimes it's more UI-related issues. Some of the more high-end features are more complicated to execute. But overall, it's a good product.
For how long have I used the solution?
I have been using Apache Kafka on Confluent Cloud for around a year, maybe two.
What do I think about the scalability of the solution?
When it comes to assessing the impact of the automated scaling features, we don't measure it, but it's part of our technology stack selection criteria - it's pretty much a must today.
We don't want to increase the headcount in our DBA team. They are the ones managing all our databases, queues, and data sources. So for us, having a very thin layer of management is critical, and we sit with other compute. That's very important for us because headcount is the most expensive part.
How are customer service and support?
We looked at other products, specifically other Kafka providers. We have Apache Kafka and AWS . We looked at self-hosting it, but we wanted Apache Kafka on Confluent Cloud.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
We were looking for specific use cases. We compared different Kafka solutions, not necessarily competitors. We have a message bus already. We wanted the log capability, mostly.
How was the initial setup?
What was our ROI?
Regarding ROI in any capacity, whether it's savings from employees or cloud, the ROI was very significant. Although, specifically with Apache Kafka on Confluent Cloud, it was a bit more challenging to increase adoption because it's very expensive. So we had to pick and choose where we implemented to make sure that ROI is positive.
I don't remember the exact number because it's been a while since we did a pricing talk, but it was expensive.
What's my experience with pricing, setup cost, and licensing?
They charge per topic and other resources. Because we are very cost sensitive, we want to approve it and make sure people don't just use it unnecessarily.
Which other solutions did I evaluate?
I would give Apache Kafka on Confluent Cloud a rating of seven out of ten.
What other advice do I have?
For somebody who's shopping around, looking in this space to decide what to purchase, Apache Kafka on Confluent Cloud is a market leader. It's almost the first choice.
Going with AWS Apache was also very compelling to us because it's very quick to enable stuff in AWS and try it. I would start with those, but first understand if this is actually what you need. There are other much cheaper solutions that serve other use cases, and sometimes people can mix those and just pick the wrong product.
Overall, I would rate Apache Kafka on Confluent Cloud a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Keith Azzopardi
Ensures reliable data management and strengthens real-time innovation
Reviewed on Mar 28, 2025
Review provided by PeerSpot
What is our primary use case?
We send events to Apache Kafka on Confluent Cloud for microservices and in an event-driven system. We are building an event-driven system, and we send all the events for microservices communication via Apache Kafka on Confluent Cloud .
What is most valuable?
The order guarantee of Apache Kafka on Confluent Cloud and the amount of throughput it can handle are valuable. The fact that the consumer pulls the data, not the broker, makes it more resilient and more reliable compared to other technologies. It has been very stable, and since Apache Kafka offers retention of seven days by default, it allowed us to create new consumers and types of data in real time.
What needs improvement?
The ability to implement request-response communication on Apache Kafka needs improvement. The schema registry is a bit misleading in terms of its location and how it works with multi-peer clusters.
For how long have I used the solution?
I have been working with Apache Kafka on Confluent Cloud for seven years.
What was my experience with deployment of the solution?
The deployment was very quick and required just following the wizard and the UI. It took minutes to deploy.
What do I think about the stability of the solution?
Apache Kafka on Confluent Cloud is very stable, though we have had a few issues. I will rate it nine out of ten, as we barely had any issues in production with Apache Kafka on Confluent Cloud.
What do I think about the scalability of the solution?
Scalability is super important for us, and Apache Kafka on Confluent Cloud provides impressive horizontal scaling to reduce risks and improve availability. I would rate the scalability as ten out of ten.
How are customer service and support?
The customer service is very useful. They try to help, though sometimes they go in circles, and I need to remind them of the original objective of the incident. Overall, they are capable people who provide the support needed.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
Not really. Before seven years ago, I might have used other solutions, but in the last seven years, I have not switched.
How was the initial setup?
The initial setup was fairly easy, seven out of ten. The main challenge was understanding the schema registry and how it works with multi-peer clusters.
What about the implementation team?
One person was enough for the deployment. We also need one person for maintenance.
What was our ROI?
The return on investment has been significant, especially in terms of stability, scalability, and the fact that we almost never had any issues in production.
What's my experience with pricing, setup cost, and licensing?
I would rate the pricing as fair, five out of ten. You can pay between 30,000 to 60,000 euros per year.
Which other solutions did I evaluate?
We considered Azure Service Bus and RabbitMQ. However, the availability and architecture of Apache Kafka on Confluent Cloud make it difficult to consider other technologies.
What other advice do I have?
Overall, I would rate Apache Kafka on Confluent Cloud nine out of ten. It is very stable, scalable, and has allowed us to innovate with real-time data consumption. The overall product rating is nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Anand Venugopal
Enables multi-cloud real-time data integration with robust support and value-driven cost management
Reviewed on Mar 04, 2025
Review provided by PeerSpot
What is our primary use case?
I use Apache Kafka on Confluent Cloud as a streaming platform for enterprises to move data in real time from the point of generation to where it needs to be consumed. Use cases for this include point of sale, IoT, financial transactions, and any application that benefits from real-time data processing. My work involves using these solutions for industry verticals and customers in the retail and financial services sectors.
What is most valuable?
Apache Kafka on Confluent Cloud is a serverless, multi-cloud SaaS product that eliminates the need for users to manage their own Kafka clusters. It offers numerous connectors to various sources and destinations, facilitating easier integrations. The powerful integration with Flink and Iceberg (Table Flow) enhances functionality. These features are not present in an open-source product. The scalable, reliable service supports multi-cloud data streaming, making it easier for enterprises to connect disparate data sources and destinations.
What needs improvement?
Improvement can be made by making it easier to build applications on the real-time stream, focusing on real-time pre-processing and anomaly detection. They should enhance their capabilities in real-time data processing to support AI scenarios, in line with their messaging.
For how long have I used the solution?
I have had experience with Apache Kafka on Confluent Cloud for quite a while, about three to four years.
What was my experience with deployment of the solution?
Setting up Apache Kafka on Confluent Cloud is definitely better than setting up open-source Apache Kafka .
What do I think about the stability of the solution?
I find Apache Kafka on Confluent Cloud very stable, and I believe the company is progressive in what they do.
What do I think about the scalability of the solution?
The solution is much more scalable because it is a managed service. The service is reliable, with an entire team dedicated to managing it, in contrast to running Kafka independently. Apache Kafka on Confluent Cloud supports multi-cloud operations, facilitating data streaming between diverse and multi-cloud infrastructures.
How are customer service and support?
The customer support for Apache Kafka on Confluent Cloud is pretty decent. A solid organization supports customers, with many of the original committers and founding team of Apache Kafka involved, reflecting the strong pedigree.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Previously, it became increasingly difficult to manage Kafka at scale independently. We switched to Apache Kafka on Confluent Cloud to use it as a managed service, allowing us to focus more on the application layer and use cases rather than the infrastructure.
How was the initial setup?
The initial setup is reasonable and definitely better than setting up open-source Apache Kafka. On a scale of one to ten, I would rate it an eight.
What was our ROI?
Apache Kafka on Confluent Cloud is critical infrastructure for us. Without it, our infrastructure costs would increase significantly, potentially amounting to hundreds of thousands of dollars each year. Its real-time capabilities accelerate speed to value and enable new use cases, providing significant business value.
What's my experience with pricing, setup cost, and licensing?
Previously, the pricing was on the higher side. However, recent product introductions consider various use cases, like freight clusters, making enterprise clusters more reasonably priced with flexible pricing options.
What other advice do I have?
I would recommend Apache Kafka on Confluent Cloud to others, especially those building out a real-time streaming infrastructure or transitioning to real-time business operations from delayed batch processes. They should consider it if they have assets across different clouds. Overall, I rate the solution at eight or eight and a half. It is a stable and steadily growing company with reliable services.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Ritik Varshney
Enhanced data streaming with reliable features and good analytics
Reviewed on Nov 21, 2024
Review provided by PeerSpot
What is our primary use case?
We use Apache Kafka on Confluent Cloud for streaming large volumes of data in real-time. It's employed in scenarios such as handling events from various countries and streaming them efficiently for our clients.
We also utilize it for data analytics and in client versions for topic creation, consumer consumption, and ACL provisioning.
How has it helped my organization?
Apache Kafka on Confluent Cloud provides an enhanced level of reliability and resources compared to Apache Kafka alone. It offers more features which are beneficial for our clients, including cluster linking, schema registry, error handling, and dead-letter queues. It significantly improves customer and publisher satisfaction, especially with topic integration and data streaming.
What is most valuable?
Apache Kafka on Confluent Cloud is more reliable and frequent to use compared to Apache Kafka . Its features such as schema registry, cluster linking, error handling, and dead-letter queues provide significant benefits. It also offers enhanced visibility and integration for data streaming, helping clients and customers use it efficiently.
What needs improvement?
Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods. This aspect could be smoother to better support migration processes.
For how long have I used the solution?
I have been working with Apache Kafka on Confluent Cloud since September 2022 after joining my current company in June 2022.
What do I think about the stability of the solution?
The solution is stable and monitors activities, ensuring reliable operations. It provides alerts for unusual activities that allow us to take proactive actions.
What do I think about the scalability of the solution?
Confluent Kafka's scalability is rated eight out of ten. It's capable of horizontal scalability by adding more consumers to handle high message throughput.
How are customer service and support?
Technical support for Confluent Kafka is very good. Their efforts to provide timely solutions to bugs and defects have been excellent.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Previously, solutions such as Red Hat AMQ and Google's PubSub were considered, but Apache Kafka on Confluent Cloud was ultimately chosen.
How was the initial setup?
The initial setup is straightforward with provided resources and documentation. I started working with it in the middle stages, not from the initial deployment.
What about the implementation team?
Deployment can be done by two members based on requirements, and a single DevOps engineer can handle both deployment and maintenance.
What's my experience with pricing, setup cost, and licensing?
I'm not sure about the pricing of Apache Kafka on Confluent Cloud.
Which other solutions did I evaluate?
I studied the PubSub and AMQ platforms yet did not have hands-on experience with them since Confluent Kafka was already implemented in my company.
What other advice do I have?
I recommend new users start by going through the Confluence page and training to learn about Confluent Kafka's features and differences from Apache Kafka.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Computer & Network Security
Experience with Confluent in my undergraduate research
Reviewed on Aug 20, 2024
Review provided by G2
What do you like best about the product?
Confluent has great documentation and developer advocacy resources. I learned Kafka and Kafka Streams almost exclusively using Confluent resources.
What do you dislike about the product?
I did not use Confluent tooling deeply or long enough to find downsides.
What problems is the product solving and how is that benefiting you?
Not a business problem, but helped me implement an event-stream processor using Kafka Streams. The streaming portion of the work was abstracted so I could focus on the algorithmic gist of my project.