April 12, 2023

Kinesis vs Kafka - A comparison of streaming data platforms

A detailed comparison of Apache Kafka and Amazon Kinesis that covers categories such as operational attributes, pricing model, and time to production while highlighting their key differences and use cases that they typically address.

Kinesis vs Kafka logos on navy blue background
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Apache Kafka and Amazon Kinesis are two of the technologies that can help you manage real-time data streams. And, although they have a great deal in common, there are some significant differences you’ll need to consider when choosing whether to use Kafka or Kinesis.

In this comparison, we’ll look at the most important differences between them and the impact their deployment and DevOps models will have on your team’s resources. We’ll also look at how Kafka and Kinesis engage with complementary tools such as Apache Spark, Quix, and AWS Lambda.

First, though, if you’re here because you’re planning to build an event-driven application, I recommend the “Guide to the Event-Driven, Event Streaming Stack,” which talks about all the components of EDA and walks you through a reference use case and decision tree to help you understand where each component fits in.

Okay, so let’s look at why we’re pitting Kafka vs Kinesis.

  • 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.
    Overlapping use cases: Kafka and Kinesis play a very similar role in many scenarios, such as building real-time streaming data pipelines, ingesting logs, and implementing event-driven architectures.
  • The rise of the cloud-native Kafka ecosystem: Managed Kafka solutions like Confluent Cloud, Amazon MSK, and Aiven for Apache Kafka allow us 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 you to focus on building applications.
  • The advent of stream processing: Kafka and Kinesis aren’t just about moving data from one place to another. Integrations with tools such as Kafka Streams, Apache Flink, and Quix also enable you to process data in-flight.

So, if you’re trying to decide between Apache Kafka and Amazon Kinesis, you’re in the right place. Here, we’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 systems actually do.

What is Apache Kafka?

Apache Kafka is an open-source distributed streaming platform designed to move high volumes of data at speed around event-driven systems. At its heart, Kafka is an append-only log. What makes it a great fit for a huge variety of use cases is that it can write to that log from almost any data source and, in turn, just about any consumer can read from it. 

To make it scalable and resilient, Kafka partitions and replicates the log across multiple servers in a cluster. Combined with a “shared nothing” architecture, that means both that the cluster can survive the failure of individual nodes and that you can scale-out its capacity by adding more nodes. Originally developed at LinkedIn, and now a project of the Apache Software Foundation, Kafka has become a popular choice for building real-time data pipelines, event-driven architectures, and microservices applications.

Core Capabilities:

High-throughput, low-latency data transport: Publish and subscribe to real-time, event-based data streams known in Kafka as topics.

Highly scalable and resilient: Kafka avoids single points of failure by running as a cluster of multiple servers, partitioning and replicating each topic among them. In the event of failure, or if you add new servers, Kafka rebalances the partitions accordingly.

Data persistence: Each record is appended to Kafka’s read-only log and you can tune retention based on age and size.

Key features:

Decoupled event streaming: Kafka ingests events, or messages, from multiple sources in real-time. Consumers then read and respond to those events at their own pace.

Process data in-flight: Perform real-time processing, including stateful operations, whether that’s using your own code with the Kafka Streams library, or using stream processing tools such as Apache Flink and Quix. You can then write the results back into Kafka for further processing and distribution.

Flexible data formats: Kafka is data-format agnostic, meaning you can work with JSON, XML, Apache’s own Avro, Protobufs, or whatever you prefer. However, there are some performance limits to the size of individual messages and you’ll need to consider how to serialize/deserialize less common formats.

Connect to a rich ecosystem: You can hook Kafka up to just about any data source or processing tool thanks to both official and community supported connectors.

Active open source community. As an open source project, Kafka has a global developer community offering informal support and producing client libraries and connectors to work with a broad range of languages, frameworks, and other technologies.

Self-hosted but cloud options are available: As an open source project, Apache Kafka is free to use on your own hardware, while hosted versions are available from cloud providers.

What is Amazon Kinesis?

Amazon Kinesis is a managed, cloud-based service for real-time data streaming and processing provided by Amazon Web Services (AWS). Kinesis collects, processes, and analyzes 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.

Just like Kafka, the core of Kinesis is an immutable event log. Producers write to Kinesis, consumers read from it, and you can connect stream processing tools, such as Apache Flink and serverless functions running in AWS Lambda. Perhaps the most important difference between Kinesis and Kafka is that Kinesis is proprietary software available only as a cloud service from AWS.

Amazon Kinesis logo

Core Capabilities:

High-throughput, low-latency data transport: Publish and subscribe to real-time, event-based data streams known in Kinesis as streams

On-demand scaling: Kinesis Data Streams On-Demand mode provides a serverless experience, scaling up and down automatically to meet changes in demand.

Highly resilient: Kinesis distributes your data streaming workloads across multiple AWS availability zones, with data persistence for up to 365 days, enabling automatic recovery from failures.

Key features:

Three types of data streaming: Kinesis offers different options depending on your data needs. Kinesis Data Streams is for real-time ingestion and distribution of data to consumers, with the option of connecting processing tools and your own custom code. Kinesis Data Firehose is specifically for transforming and loading data into AWS data storage products, such as S3 and Redshift. Kinesis Video Streams supports streams, stores, and encrypts video data from sources such as surveillance cameras, with integrations into video-specific processing tools, such as AWS Rekognition.

Tight AWS integration: Ingest data from AWS services, such as AWS EventBridge and AWS Simple Queue Service, and then process data using tools such as AWS Lambda and AMS for Apache Flink. There are also some integrations with non-AWS tools, such as Apache Spark, Apache Flink, and Quix.

Fully managed: There’s no set-up and very little ongoing DevOps burden with Amazon Kinesis. That lets you focus engineering resources on building your own software.

Pay as you go: There are no upfront costs but the trade-off is that you’ll pay usage fees for as long as you need Kinesis.

Easy monitoring and management: AWS Management Console and APIs, as well as integration with AWS tools such as CloudFront, give you rich control and visibility of your Kinesis streams.

Summarizing Kafka vs Kinesis

Kafka and Kinesis do pretty much the same core job. They both ingest data in real-time from multiple sources and write it to a highly available log. Consumers and processing tools can then consume data from that log and, optionally, write back into the data stream. 

Both offerings will also scale with your needs but that’s where we hit the main difference between Kafka and Kinesis. As an open source technology, you need to set up and manage Kafka yourself. With its reputation for operational complexity, Kafka can increase the time it takes to deliver your project. Kinesis, on the other hand, requires very little DevOps input, whether that’s for day-to-day running or scaling to meet increased load.

But with multiple hosted Kafka services available, the operational burden between Kafka and Kinesis can be more or less the same. So, let’s compare Kinesis vs Kafka on a wider set of key attributes.

Kinesis vs Kafka: Operational Attributes

When you put either Kafka or Kinesis into production, how can you expect them to perform? This comes down to characteristics such as throughput and scalability, but also how well either tool integrates with other technologies.

| Attribute | Kafka logo | Kinesis logo | |----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------| | Performance |

Can generally handle higher throughput

Low latency


Moderate throughput compared to Kafka

Higher latency than Kafka

| | Message semantics |

At most once: lowest latency but risks losing messages

At least once: could result in duplicates

Exactly once: uses transactions to guarantee a message will be written to the log without duplicates


At least once: Kinesis will write messages to the log one or more times, which could result in duplicate records

| | 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

| | 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


Kinesis vs Kafka: Stream processing

Both Kinesis and Kafka have client libraries that simplify creating your own stream processing functions, as well as offering integrations with third-party stream processing tools, including managed platforms such as Quix and open source projects like Spark. Let’s look at how they compare when it comes to steam processing.

Attribute Kafka Kinesis
Stream processing client libraries Kafka Streams is a library for building stateful, fault-tolerant processing tools that ingest directly from Kafka topics and, optionally, write back to them. Kinesis Client Library acts as a way to build connectors, in multiple languages, between Kinesis data streams and downstream tools, such as your own processing code running in AWS Lambda.
SQL interfaces ksqlDB is a standalone database tool that uses Kafka Streams to read from and write back to Kafka topics, allowing to query and manipulate Kafka data using SQL. AMS for Apache Flink, formerly known as Kinesis Data Analytics, provides a SQL interface (amongst others) to query data from Kinesis data streams and other sources.
Other integrations Both Kafka and Kinesis connect with the Quix Python open source library and cloud service for building and running containerized stream processing applications.

Kinesis vs Kafka: Pricing

Comparing the pricing of Kafka and Kinesis is difficult because there isn’t a one-for-one relation between the total cost of running open source software and using a managed cloud service. For example, it’s hard to draw a direct comparison between the cost of the DevOps headcount you’ll need to run your own Kafka instance versus Kinesis’s usage fees. We can level the playing field by comparing Kinesis with one of the many hosted Kafka providers. Here we’ll look at Confluent Cloud.

There are some other factors that muddy the waters, though, such as package price vs usage costs: 

  • Kinesis offers on-demand pricing, which charges for the number of streams you have and for the storage you use, as well as data ingress and egress. Kinesis provisioned pricing charges per shard hour, as well as for data ingress and storage. 
  • Confluent Cloud’s basic package has per-usage pricing but you can opt for lower usage charges and different limits in exchange for a minimum monthly spend in their standard, enterprise, and dedicated packages.

Confluent Cloud and Kinesis also use different metrics to measure similar things. For example, Kinesis’s on demand pricing charges by stream used whereas Confluent Cloud charges by the partition. Pricing can also vary by the cloud region you choose, in the case of Kinesis, and both the cloud provider and cloud region you choose for Confluent Cloud.

To simplify the comparison, we’ll look at Kinesis’s on demand pricing for Kinesis Data Streams in the US East (Ohio) region. For Confluent Cloud we’ll use their basic package pricing; where there’s a price range for a particular operation, we’ll select the higher price. This is based on publicly available pricing information as of January 2024.

| | Kafka logo | Kinesis logo | |-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 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. 

$0.08 per GB per month
E.g. 1 TB per month = $80 | Retaining data for up to 24 hours is included in the ingest fee.

After the first day and up to 7 days, the charge is $0.10 per GB.

Storing data beyond 7 days is $0.023 per GB (e.g. 1 TB for a 31 day month after the initial 7 days would be $23) | | Horizontal Scaling | Partition hours: Charges for the number of topic partitions used and their duration (in hours).The first 10 are free. 

$0.004 USD/hour
E.g. $0.004 USD/hour
E.g. in a 31 day month, 15 partitions = $14.88
| 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  |

Kinesis vs Kafka: Time to production

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:

| | Kafka logo | Kinesis logo | |---------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 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 for 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 expertise unless you’ve committed to building a dedicated team to manage it. 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. 

Whether you choose Kafka or Kinesis, it’s likely to be just one component amongst several in your data streaming platform. One area where you can quickly hit complexity is in processing your streaming data. That’s why we created Quix; to make Python stream processing simple for building ML pipelines and AI products. We first built Quix Streams, an open source Python library for building containerized stream processing applications with Apache Kafka. We then wrapped it up with Quix Cloud, which provides fully managed containers, Kafka, and observability tools to run your applications in production. With Quix, you can focus entirely on building serverless event streaming applications instead of dealing with the headache of managing the underlying infrastructure. To learn more, check out the Quix docs.

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