Kinesis vs Kafka - A comparison of streaming data platforms

Apache Kafka and Amazon Kinesis are both technologies that can help organizations manage real-time data streams, but they’re each quite different. For one, Kinesis is an AWS managed service whereas Kafka can be installed anywhere. So why are they often compared? Well, a for a few reasons:

Similar core goals: Both platforms aim to provide high-throughput, low-latency, and fault-tolerant data streaming capabilities. They are designed to handle massive amounts of data in real-time, making them suitable for use cases such as event-driven architectures, real-time analytics, and log aggregation.

 

Overlapping use cases: Despite their differences, Kafka and Kinesis can be used interchangeably in many scenarios, such as building real-time streaming data pipelines, ingesting logs or metrics, or implementing event-driven applications. As a result, users often compare the two platforms to determine which one suits their specific needs and requirements better.

 

The rise of the cloud-native Kafka ecosystem: With the availability of managed Kafka solutions like Confluent Cloud, Amazon MSK, and Aiven, it is now easier to compare Kafka and Kinesis on a more level playing field in terms of operational ease. Both managed Kafka services and Amazon Kinesis take care of infrastructure management, scaling, and maintenance, allowing users to focus on building applications.

Thus, if you’re trying to decide between Apache Kafka and Amazon Kinesis, you’re in the right place—I’ll guide you through the most important points of comparison while highlighting the key differences between the two event streaming platforms. But first, let’s define what these two system actually do:

 

Apache Kafka is an open-source distributed streaming platform designed to handle high-velocity, high-volume, and fault-tolerant data streams. It was originally developed by LinkedIn and later donated to the Apache Software Foundation. Kafka has quickly become a popular choice for building real-time data pipelines, event-driven architectures, and microservices applications.
 

Core Capabilities:   Publish and subscribe to streams of records Store streams of records in a fault-tolerant and durable way Works with complimentary services to process streams of records as they occur (Kafka Streams and ksqlDB) Key features: High-throughput, low-latency messaging for real-time data streaming Scalable architecture that supports data partitioning and replication Strong durability guarantees with a distributed and fault-tolerant design Stream processing capabilities with complementary services (Kafka Streams and ksqlDB) Rich ecosystem of connectors and integrations through Kafka Connect Active open-source community and support for various programming languages

Amazon Kinesis is a managed, cloud-based service for real-time data streaming and processing provided by Amazon Web Services (AWS). Kinesis enables you to collect, process, and analyze large volumes of data in real-time, enabling quick decision-making and responsive applications. It is designed to handle massive amounts of data with low-latency and high-throughput capabilities.

 

Core capabilities:   Ingest and process real-time data streams Store data streams for later analysis Enable real-time analytics and decision-making Key features: Fully managed, scalable, and secure data streaming service Integration with other AWS services for data storage, processing, and analytics Stream processing capabilities with Kinesis Data Analytics service Support for popular data processing frameworks like Apache Flink and Apache Spark Pay-as-you-go pricing model, eliminating upfront costs and maintenance overhead Easy monitoring and management through AWS Management Console and APIs

To summarize, Kafka is a complex, open-source technology that can be deployed anywhere with few limits on horizontal scalability whereas Kinesis is a more user-friendly but proprietary technology that runs exclusively in the AWS ecosystem.

Now let’s compare Kinesis vs Kafka side-by-side on a wider set of key attributes.
 

To make this comparison easier to digest, I’ve tried to generalize about how each system compares based on the important attributes of a stream processing system.

Attribute
Performance	Can generally handle higher throughput Low latency	Moderate throughput compared to Kafka Higher latency than Kafka
Scalability	Highly scalable due to its distributed architecture Can add more nodes to the cluster for increased capacity	Scales with the number of shards Shard limits per Kinesis stream, but multiple streams can be used for greater scalability
Data Retention	Configurable retention period Data can be stored indefinitely if desired	Retention period of 24 hours up to 7 days, extendable up to 365 days with Extended Data Retention
Ecosystem	Rich ecosystem with many connectors and integrations Supported by Confluent Platform, which offers extra features and support	Limited ecosystem compared to Kafka - Primarily supported by Amazon services
Data Durability	Replicates data across multiple nodes for fault tolerance Can be configured for stronger durability with higher replication factors	Replicates data across three availability zones
Cost	Can be self-hosted or managed by a third-party provider (e.g., Confluent) Self-hosting requires hardware and maintenance costs	Pay-as-you-go pricing model based on shards and data throughput No need to manage infrastructure, as it is fully managed by AWS
Security	Supports SSL/TLS encryption, SASL authentication, and ACLs for access control Security features depend on deployment and configuration	Supports server-side encryption and AWS Identity and Access Management (IAM) policies Integrated with AWS infrastructure and services
Stream Processing	Stream processing via Kafka Streams and ksqlDB Supports powerful stream processing features	Stream processing via Kinesis Data Analytics Limited stream processing features compared to Kafka
Community and Support	Large open-source community and commercial support from Confluent Extensive documentation and resources	Primarily supported by Amazon, with fewer community resources Detailed AWS documentation, but fewer community resources
Monitoring	Requires setting up monitoring tools (e.g., JMX, Grafana, Prometheus) Can use third-party tools or Confluent Control Center for enhanced monitoring capabilities	Integrated with AWS CloudWatch for monitoring and alerting Can be combined with other AWS services for additional monitoring options

Given that Apache Kafka itself is an open-source framework, it can’t be compared directly with Amazon Kinesis in terms of pricing. What we can do instead is compare managed versions of Kafka with Kinesis. For this comparison, I’ll use Confluent Cloud. However, Confluent and Amazon will charge you in slightly different ways.

Let's compare the line items you'll typically see on your bill using each service. Note that all price examples are approximate and might have changed since the time of writing (April 2023). They also do not include new starter incentives such as free credits. 
 

Input	Writes: volume of data ingested into the Kafka cluster. $0.13 per GB E.g. 1 TB per month = $130	Data-in: the amount of data ingested into the Kinesis Data Streams (billed per GB) $0.08 per GB E.g. 1 TB per month = $80
Output	Reads: volume of data consumed from the Kafka cluster.  $0.13 per GB E.g. 1 TB per month = $130	Data-Out: the amount of data retrieved from Kinesis Data Streams (billed per GB)  $0.04 per GB E.g. 1 TB per month = $40
Storage	Storage: volume of data stored in the Kafka cluster based on the retention period.  $0.10 per GB per month E.g. 1 TB per month = $100	Extended Data Retention (optional): Additional charges for extending the data retention period beyond the default 24 hours up to 7 days, or up to 365 days with Extended Data Retention.  $0.10 per GB beyond the first 24hrs up to 7 days  $0.023 per GB beyond 7 days (both calculated and billed per month) E.g. 1 TB per month = $36.23 (approx)
Horizontal Scaling	Partition hours: Charges for the number of topic partitions used and their duration (in hours).  $0.004 USD/hour E.g. 1 month of 5 partitions = $14.4	Stream hours: The number of hours you are accessing a Kinesis Data Stream in “on-demand” (auto scaling) mode.  $0.004 USD/hour Shard hours: 1 month of 1 stream = $28.88  Charges for the number of shards used in your Kinesis Data Streams and their duration (in hours) when in “provisioned” mode.  $0.015 per hour 1 month of 5 shards (1TB per month)= $55 approx

For Confluent, there are other pricing variables such as cluster type and the cloud provider where you’ll be hosting Confluent Cloud (AWS, Azure or GCP) but this comparison covers the core variables.

To generalize, Confluent Cloud’s pricing model is a little more expensive than the Kinesis “on demand” mode if you're a small-scale startup with low horizontal scaling requirements (i.e. partitions and shards). The Kinesis “on-demand” option might seem more expensive per hour, but it takes care of the horizontal scaling for you and you don’t have to worry about whether you’re using 5 or 50 shards. However, Confluent does offer generous free credit bundles for new customers and free partition allowances.

Generally speaking, once your use cases get more advanced or your data volumes and processing requirements increase, Confluent starts to become cheaper than Kinesis (since Kinesis charges extra for features which allow give your more control over horizontal scaling such as shard hours and Enhanced fan-out).
 

While cost is a critical factor, the time it takes to get the system up and running in production is just as important, if not more so.

However, time to production depends on various factors such as your team's familiarity with the technology, the complexity of your application, and your existing infrastructure.

Here is a general comparison of the typical ranges of time for Kinesis vs Kafka:

Setup and configuration	Weeks  Setting up and configuring a Kafka cluster can be time-consuming, especially if the team has limited experience with Kafka. You'll need to install and configure Kafka brokers, Zookeeper nodes, and other components such as connectors or stream processing libraries. This process can take anywhere from a few days to a couple of weeks, depending on the complexity of the setup.	Days  Setting up Amazon Kinesis is generally quicker and simpler than Kafka, as it's a fully managed service by AWS. You'll need to create and configure Kinesis streams and shards, which can be done using the AWS Management Console or AWS SDKs. This process can take a few hours to a couple of days, depending on the complexity of your use case and your familiarity with AWS.
Infrastructure management:	Days  If you're self-hosting Kafka, you'll need to spend time provisioning, monitoring, and maintaining the infrastructure. This includes setting up monitoring and alerting systems, patching and updating the software, and managing hardware or virtual machines.	Hours  With Kinesis, you don't have to worry about provisioning or maintaining infrastructure, as AWS handles it for you. Especially if you’re using the on-demand version. This reduces the time and effort spent on infrastructure management.
Learning curve:	Weeks  There is a learning curve associated with Apache Kafka, which can take some time for teams to familiarize themselves with the technology. Depending on the team's prior experience, this can take anywhere from a few days to a few weeks.	Days  While the learning curve for Kinesis is typically shorter than Kafka, teams still need some time to familiarize themselves with the service and how it integrates with other AWS services. Kinesis also has some unique concepts that are less written about online.

If you opt for a managed Kafka service like Confluent Cloud, the setup and configuration time can be significantly reduced. In this case, getting up-and-running may also only take a couple of days, as you'll need to configure your application to interact with the managed service.

However, while Confluent Cloud reduces some complexity associated with managing Kafka, there is still a learning curve related to Kafka concepts, APIs, and stream processing libraries. The learning curve for Confluent Cloud may be shorter than self-managed Kafka, but it might still take a few days to a couple of weeks, depending on your team's prior knowledge and experience.

Of course, Confluent is not the only managed Kafka solution. There are other solutions such as Amazon MSK and Aiven Apache Kafka. There are also solutions that use Kafka under the hood, namely our own—Quix. Quix doesn’t fit in the managed Kafka category, because it is focused on stream processing. As such it includes a fully managed Kubernetes environment where you can build and run serverless containers using an online IDE and integrated data exploration tools. Quix connects to any Kafka instance and has data source and sink connectors for Kinesis.

When choosing between Apache Kafka and AWS Kinesis for your event streaming platform and distributed messaging needs, it's essential to forecast your throughput requirements while considering factors such as performance, architecture, features, and the overall ecosystem of each platform.

Kafka is an excellent choice if your organization is sensitive to vendor-lock-in and needs a high-performance, scalable, and feature-rich event streaming platform (provided you have the in-house Kafka expertise).

Kinesis may be more suitable if your organization is already heavily invested in the AWS ecosystem and you prefer the ease of a fully managed service that seamlessly integrates with other AWS services.

Ultimately, the choice between Kinesis vs Kafka will depend on your appetite for complexity versus cost. Kafka can be a lot cheaper but riskier because it has the potential to tie up your technical experts. Kinesis, on the other hand, can make your life a lot easier but you’ll risk bigger infrastructure bills somewhere down the line. And, in the middle are the managed Kafka services which all claim to offload some of Kafka’s complexity for a price. The choice is yours. But if you want the simplicity of Kinesis with the power of Kafka, check out Quix first.