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Amazon Kinesis vs Apache Kafka on Confluent Cloud comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
7.8
Amazon Kinesis offers significant cost savings, seamless integration, improved monitoring, and reduces data ingestion costs, enhancing ROI and architecture.
Sentiment score
3.1
Apache Kafka on Confluent Cloud boosts ROI and reliability, but adoption may be challenging due to associated costs.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
Returns depend on the application you deploy and the amount of benefits you are getting, which depends on how many applications you are deploying, what are the sorts of applications, and what are the requirements.
 

Customer Service

Sentiment score
7.1
Amazon Kinesis customer support is generally quick and effective, but experiences vary in technical guidance and response times.
Sentiment score
4.3
Apache Kafka's Confluent Cloud support is well-rated, effective with tools, timely, despite minor communication issues and preference for forums.
We receive prompt support from AWS solution architects or TAMs.
I would rate them eight if 10 was the best and one was the worst.
 

Scalability Issues

Sentiment score
7.3
Amazon Kinesis is scalable for reliable streaming, but complex processing and costs may vary with implementation and data volumes.
Sentiment score
3.8
Apache Kafka on Confluent Cloud is praised for scalability, despite some reliability issues, with managed services reducing operational burdens.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
I would rate the scalability of Amazon Kinesis as a nine.
 

Stability Issues

Sentiment score
7.8
Amazon Kinesis is praised for stability and fault tolerance, though some users report slowdowns and capacity issues.
Sentiment score
3.5
Users consider Apache Kafka on Confluent Cloud stable but report performance drops with traffic spikes and dashboard management challenges.
I would rate the stability of Amazon Kinesis as high, giving it a 10.
 

Room For Improvement

Amazon Kinesis requires improvements in throughput, automation, setup complexity, data retention, machine learning features, and user-friendly interfaces.
Confluent Cloud improves Kafka integration with PyTRAN and Microsoft, but faces challenges in real-time processing, monitoring, and cost.
There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it.
Observability and monitoring are areas that could be enhanced.
 

Setup Cost

Amazon Kinesis is cost-effective compared to self-managed solutions, but prices can increase with high data usage and features.
Enterprise users see Apache Kafka on Confluent Cloud's pricing as flexible but requiring careful management for cost optimization.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
 

Valuable Features

Amazon Kinesis offers easy configuration, real-time analytics, and robust AWS integration, ideal for managing large, complex data workflows.
Apache Kafka on Confluent Cloud offers scalable streaming, seamless integration, and efficient data processing, simplifying microservices and multi-cloud support.
Amazon Kinesis integrates easily with the AWS environment.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
These features are important due to scalability and resiliency.
The Kafka Streams API helps with real-time data transformations and aggregations.
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
29
Ranking in other categories
No ranking in other categories
Apache Kafka on Confluent C...
Ranking in Streaming Analytics
11th
Average Rating
8.6
Reviews Sentiment
3.7
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 6.7%, down from 10.5% compared to the previous year. The mindshare of Apache Kafka on Confluent Cloud is 0.1%, up from 0.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Amazon Kinesis6.7%
Apache Kafka on Confluent Cloud0.1%
Other93.2%
Streaming Analytics
 

Featured Reviews

Rajni Kumar Jha - PeerSpot reviewer
Used for media streaming and live-streaming data
It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.
FABIO LUIS VELLOSO DA SILVA - PeerSpot reviewer
Has enabled asynchronous communication and real-time data processing with strong performance
The valuable features with Apache Kafka on Confluent Cloud are the messaging and the asynchronous messages; it's the basic, not advanced usage. It's only to create clusters to receive and send messages. The point is the asynchronous messages and the scalability; it is important for us. To guarantee the compliance of the architecture and the patterns for the company, to provide scalability, and to guarantee the security to send the messages. The Kafka Streams API helps with real-time data transformations and aggregations. It's very fast and helps us to create the project, guarantee the message delivery, and the performance. It's a good experience with very impressive processing and a very impressive project and product.
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
16%
Manufacturing Company
10%
Comms Service Provider
5%
Financial Services Firm
13%
Manufacturing Company
7%
Educational Organization
6%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business8
Midsize Enterprise10
Large Enterprise9
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise3
Large Enterprise6
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
What do you like most about Apache Kafka on Confluent Cloud?
Kafka and Confluent Cloud have proven to be cost-effective, especially when compared to other tools. In a recent BI integration program over the past year, we assessed multiple use cases spanning s...
What needs improvement with Apache Kafka on Confluent Cloud?
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it. The clustering is a little hard for juniors and clients....
What is your primary use case for Apache Kafka on Confluent Cloud?
We need to send a lot of asynchronous messages in this project, and we use the middleware and Apache Kafka on Confluent Cloud to guarantee asynchronous messaging between the services. We use Apache...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
No data available
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Information Not Available
Find out what your peers are saying about Amazon Kinesis vs. Apache Kafka on Confluent Cloud and other solutions. Updated: September 2025.
869,202 professionals have used our research since 2012.