Apache Kafka® vs Amazon Kinesis: What’s the Difference?

In this guide, we’ll compare Kafka and Kinesis on architectural design, operational model, key features, and total cost of ownership (TCO) to help you decide which platform is right for your next streaming architecture.

Apache Kafka vs Amazon Kinesis: A Quick Comparison 

Apache Kafka is an open source, distributed event streaming platform used for building real-time data pipelines and streaming applications. Organizations often choose it for high-throughput, fault-tolerant data management with a high degree of control over the infrastructure.

In contrast, Amazon Kinesis is a fully managed, serverless real-time data streaming service provided by Amazon Web Services (AWS). It is designed to simplify the collection, processing, and analysis of streaming data, as AWS manages the underlying infrastructure.

What Is the Core Difference Between Kafka and Kinesis?

Both Kafka and Kinesis utilize a publish-subscribe model to handle event streams. However, their primary differences lie in control, ecosystem, and data retention models.

Side-by-Side Comparison: Key Attributes of Kafka and Kinesis

When Should You Choose Kafka vs. Kinesis?

Organizations often choose one or the other based on their ecosystem and management preferences. Kinesis is a common choice for teams already deeply embedded in the AWS ecosystem who need simple, serverless ingestion. Kafka is often chosen by teams that require ultimate control, hybrid-cloud or multi-cloud flexibility, and an open source standard.

For these reasons, many large organizations use both Kafka and Kinesis: a common pattern involves using Kinesis for simple, serverless data collection within a specific AWS application, which then feeds that data into a central Kafka environment that acts as the organization's "central nervous system" for data.

How Do Kafka and Kinesis Compare on Architecture and Design?

While both platforms move data, their underlying architectures are fundamentally different. The Kafka architecture is designed as a distributed, log-based storage system, whereas Amazon Kinesis is a managed service in which capacity is provisioned as a stream.

Think of Kafka's architecture as a commit log or a file system designed specifically for streaming data. This design gives you granular control over data retention, replication, and storage, treating your data as a continuous log that can be re-read multiple times by different applications:

• Topics and Partitions: Data in Kafka is organized into topics (e.g., "user_clicks"). These topics are split into one or more partitions.

• Durable Storage: Kafka stores ordered records in these partitions, which are appended to a log file on disk. Because the data is stored on disk, it is durable and can be retained for configurable periods—from minutes to years—or even indefinitely.

• Brokers and Replication: These partitions are distributed and replicated across a cluster of servers called brokers. This replication makes Kafka highly fault-tolerant. If one broker fails, another broker with a copy of the partition can take over.

Amazon Kinesis's architecture is a fully managed service focused on data ingestion and throughput. The core concepts are streams and shards, and its design abstracts all infrastructure management, allowing you to focus purely on the data flow and scaling throughput up or down by adding or removing shards.

• Streams and Shards: A Kinesis stream represents your data flow. Within that stream, you provision capacity using shards. Each shard is a fixed unit of capacity, offering 1MB/second of data ingestion and 2MB/second of data egress.

• Managed Infrastructure: Unlike Kafka, you do not manage brokers or servers. Kinesis uses shards managed entirely within AWS. You simply specify the number of shards you need, and AWS handles the underlying infrastructure, provisioning, and scaling.

• Ephemeral Storage: Data is stored temporarily (by default 24 hours, extendable up to one year). Kinesis is designed primarily for real-time processing and forwarding data to another destination (like Amazon S3, Lambda, or Redshift), not as a long-term data store.

Architectural Benefits: Control vs Simplicity

The architectural differences lead to clear, distinct benefits for each platform:

• Kafka provides high control and flexibility. Its durable log-based design allows for configurable retention (including permanent storage), guarantees strict ordering within a partition, and is supported by a massive, open-source client ecosystem (Kafka Connect, Flink, Kafka Streams) that works across any cloud or on-premise environment.

• Kinesis provides operational simplicity. It simplifies operations by abstracting all the underlying infrastructure. Teams can get started quickly without worrying about cluster management, and it scales by simply adjusting the shard count, making it a simple, serverless option for teams inside the AWS ecosystem.

Data Retention and Replay

A critical architectural difference that impacts system design is how each platform handles data retention and the ability to "replay" data. This capability is crucial for auditing, disaster recovery, and running new analytical models on historical data.

• Kafka allows retention to be highly configurable. You can set retention policies per topic based on time (e.g., 30 days, 1 year, or indefinitely) or size (e.g., 1 TB). Because the log is persistent, Kafka supports long-term replay of events. A new application can subscribe and read the entire history of a topic from the very beginning. This makes it simple to replay events to recover from an application failure or train a new machine-learning model.

• Kinesis is designed primarily for real-time processing, not long-term storage. By default, Kinesis offers limited retention windows set by the service configuration, typically from 24 hours to 7 days. While this is sufficient for many in-the-moment processing applications, it does not support the long-term replay use case. Data is expected to be processed and then moved to a permanent store (like Amazon S3) if historical access is required.

Scalability and Throughput

Both platforms are built to handle massive data volumes, but they approach scaling differently.

• In Kafka, scaling is achieved through horizontal scaling via partitioning and cluster expansion. To increase throughput, you can add more partitions to a topic (which distributes the load) and add more brokers to your cluster to handle the increased work. This model offers near-infinite Kafka scalability, but it often requires manual intervention to add the new hardware (or VMs) and rebalance the partitions across the cluster.

• Scaling in Kinesis is accomplished through shard-based scaling with automated management in AWS. A shard is a fixed unit of capacity. To scale up, you increase the number of shards in your stream; to scale down, you decrease them. This can be done on-demand or automatically, and AWS handles all the underlying infrastructure provisioning and management.

When comparing Kafka vs Kinesis throughput, the primary difference is the tradeoff between control and convenience. 

• Kafka gives you full control to fine-tune your cluster for specific performance needs, but you are responsible for the operational work. 

• Kinesis provides the convenience of push-button, serverless scaling, but you are limited to the fixed capacity units (shards) that AWS provides.

Reliability and Delivery Guarantees

In data streaming, a "delivery guarantee" refers to the system's promise for how it handles messages. This is a critical factor for use cases like financial transactions or inventory management, where data loss or duplication is not an option.

• Kafka is well-known for providing strong, configurable delivery guarantees. 

  • With the right configuration, Kafka offers configurable delivery guarantees up to and including exactly-once semantics. This ensures that a message is processed one time and one time only, even in the event of producer or broker failures. Kafka also provides strong ordering guarantees within a single partition.

  • For organizations that cannot compromise on reliability, Confluent extends Kafka's capabilities with enterprise-grade reliability, monitoring, and governance. This includes advanced monitoring tools to proactively detect issues, sophisticated data governance features to ensure data quality, and a complete platform designed for mission-critical 24/7 operations.

• As a managed service, Kinesis provides managed reliability with AWS SLAs. It ensures at-least-once delivery, which guarantees that a message will be delivered, but it might be delivered more than one time (duplicates are possible). Consumers must be designed to handle these potential duplicates. Kinesis provides ordering per shard, similar to Kafka's per-partition guarantee.

Ecosystem and Integration

A streaming platform's value is multiplied by its ability to connect to other systems. Here, the philosophies of Kafka and Kinesis diverge significantly.

• As an open source standard, Kafka benefits from a massive, open Kafka ecosystem. 

  • It has a vast library of Kafka connectors and native integrations with a wide range of data systems, including open-source processing engines (like Apache Flink, Apache Spark, and its own ksqlDB) and data stores (like MongoDB, Elasticsearch, and HDFS).

  • Confluent extends the open Kafka ecosystem by providing a library of 120+ pre-built, fully managed connectors that allow you to easily connect Kafka across all your environments—from on-premise systems to AWS services (including Kinesis itself) and other clouds like Google Cloud and Azure.

• Kinesis is designed for tight integration within the AWS cloud. It works seamlessly with other AWS services like AWS Lambda (for serverless processing), Amazon S3 (for data lake storage), Amazon Redshift (for warehousing), and Amazon EMR (for Spark processing). Its strength is its deep, native connection to this AWS-centric world.

Strategic Assessment: Benefits, Risks, Cost, and Operational Plan

The decision between Apache Kafka and Amazon Kinesis is not just about technology directly impacts one of the most critical factors for any business: the total cost and operational model.

• Amazon Kinesis operates on a pay-as-you-go managed service model. You pay for the resources you provision (e.g., shard-hours) and the data you move (e.g., per GB). This model offers minimal operational overhead, as AWS handles all infrastructure management, patching, and availability. The cost is predictable as long as your data volume is predictable, but it can become expensive at a very high scale or with spiky, unpredictable workloads.

• Self-managed Kafka comes with a higher operational burden. You are responsible for provisioning hardware, installing and configuring the clusters, and managing 24/7 operations, including scaling, patching, and failure recovery. This requires a dedicated operations team with specialized Kafka expertise.

Self-managed Kafka often has the highest TCO. While the software is free, the combined cost of server infrastructure (or cloud VM equivalents), network bandwidth, and—most significantly—the multi-person engineering team required to operate it 24/7, makes it the most expensive option in practice. Kinesis has a low TCO for small-to-medium workloads, especially for teams already using AWS. The costs are primarily your direct AWS bill, but the technology won't be a replacement for Kafka in scenarios where you need fine-grained control, reliability, data retention, and replayability.

Where Does Managed Streaming Fit in the Kafka vs. Kinesis Comparison?

Choosing the right data streaming technology is critical for building reliable, scalable, and cost-efficient real-time systems. Organizations often choose one or the other based on their ecosystem and management preferences. 

• Kinesis is a common choice for teams already deeply embedded in the AWS ecosystem who need simple, serverless ingestion. 

• Kafka is often chosen by teams that require ultimate control, hybrid-cloud or multi-cloud flexibility, and an open-source standard.

Many organizations use Kinesis for simple, serverless data collection within AWS, while those workloads are then streaming into Kafka for centralized integration, governance, and processing. This is where a complete data streaming platform like Confluent becomes essential. Confluent is built on the foundation of open source Kafka but also provides a complete, cloud-native platform that goes beyond what Kafka can do alone. It simplifies management and provides enterprise-grade tools for streaming integration, governance, and security, allowing you to manage data streams from all sources (including Kinesis).

Comparison: Kafka vs. Kinesis vs. Confluent

Here are common use cases where we see customers using both Kinesis and Confluent:

• Hybrid-Cloud Analytics: Imagine a global retailer uses Kinesis to ingest raw clickstream data from its AWS-hosted web app. It can then use a fully managed Confluent connector to stream that Kinesis data into a central Confluent Cloud cluster. This central cluster enriches the data and streams it to both an on-premise data warehouse and a separate cloud (like Google BigQuery on Google Cloud) for different analytics teams.

• Centralized Governance: Financial services firms often use Kinesis for simple log collection within isolated AWS accounts. If all high-value transaction data flows through Confluent, then the organization can use the built-in Schema Registry and Data Portal to provide a single-pane-of-glass for data governance, ensuring all data, regardless of its source, is compliant and trustworthy before it's used by downstream applications.

This table summarizes the key differences between self-managing open-source Kafka, AWS's managed Kinesis, and Confluent's data streaming platform. Explore this Confluent vs. Kinesis guide for a complete managed streaming comparison.

Amazon Kinesis, self-managed Kafka, and Confluent Cloud each offer unique strengths depending on your operational needs, cloud strategy, and use case complexity.

The table below summarizes the key criteria and highlights which platform is best suited for which type of workload and organizational requirement.

Data Streaming Evaluation: Key Criteria for Comparing Kinesis, Kafka, and Confluent Cloud

Get Started With Your Data Streaming Journey

Whether you're building a new real-time application or modernizing your data architecture, a data streaming platform is the foundation for success. Confluent provides a complete, cloud-native platform built for the enterprise, so you can focus on innovation, not operations.

• Try Confluent Cloud: Start your free trial and get a fully managed Kafka cluster in minutes

• Learn More: Explore more Kafka use cases and how to integrate with your tech stack

• Read the Blog: “How to build a hybrid streaming architecture with Confluent”