Skip to main content

What's the Difference Between Amazon MSK and MSK Serverless?

Compare Amazon MSK and MSK Serverless side by side — features, pricing, and ideal use cases to help you choose the right product.

Compare side-by-side

Comparisons
Amazon MSK
MSK Serverless
Category

Analytics, Managed Kafka

Analytics, Serverless Streaming
Description

Fully managed Apache Kafka service

Run Apache Kafka without managing and scaling cluster capacity.
Best for
  • Event streaming
  • Log aggregation
  • Metrics collection
  • Stream processing
  • Event sourcing
  • Event streaming
  • Serverless Kafka
  • Variable workloads
  • Development environments
Key features
  • Apache Kafka compatible
  • MSK Serverless
  • MSK Connect
  • Tiered storage
  • Multi-AZ
  • No capacity planning
  • Auto scaling
  • Kafka compatible
  • IAM authentication
  • Pay-per-use
Pricing model

Pay per broker hour + storage

Pay per cluster hour + partition hour + data
Free tier

No

No
Expert take

“MSK is for teams that need Apache Kafka compatibility with existing tooling (Kafka Connect, Schema Registry, KSQL). MSK Serverless removes broker sizing entirely. If you do not need Kafka-specific APIs and just want to stream data into S3 or Redshift, Kinesis Data Firehose is simpler.”
— Riku Kobayashi, re:Post Top Contributor [profile]

“MSK Serverless auto-scales Kafka cluster capacity based on throughput. You create topics and produce/consume messages without choosing broker types or managing partitions.”
— Oleksii Bebych, re:Post Top Contributor [profile]
Product page

Learn more about Amazon MSK »

Learn more about MSK Serverless »

When to use Amazon MSK or MSK Serverless

Use Amazon MSK when:

  • Event streaming
  • Log aggregation
  • Metrics collection
  • Stream processing
  • Event sourcing

Learn more about Amazon MSK »

Use MSK Serverless when:

  • Event streaming
  • Serverless Kafka
  • Variable workloads
  • Development environments

Learn more about MSK Serverless »

AWS product comparisons

What's the difference between Amazon Aurora and Amazon RDS?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

Learn more

What's the difference between Amazon DynamoDB and Amazon RDS?

DynamoDB is a serverless NoSQL database for applications with well-defined access patterns that need consistent single-digit millisecond performance. RDS is a managed relational database for applications that need SQL, joins, and transactional integrity across multiple engines.

Learn more

What's the difference between Amazon DynamoDB and Amazon Keyspaces?

Both are fully managed, serverless-capable NoSQL databases on AWS. DynamoDB is AWS-native with its own API and data model; Keyspaces is Apache Cassandra-compatible, letting you use existing Cassandra drivers, tools, and expertise.

Learn more

What are the differences between Amazon DynamoDB and Amazon DocumentDB?

Both are fully managed NoSQL databases on AWS, but they serve different data models. DynamoDB is a key-value store optimized for known access patterns at massive scale; DocumentDB is a document database for flexible JSON schemas with MongoDB compatibility.

Learn more

What's the difference between Amazon DocumentDB and Amazon Neptune?

Both handle semi-structured data, but they're optimized for different query patterns. DocumentDB stores and queries JSON documents; Neptune stores and traverses relationships between entities.

Learn more

What's the difference between Amazon Aurora and Amazon RDS?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

Learn more

What's the difference between Amazon Aurora and Amazon Neptune?

Both are purpose-built, fully managed AWS databases, but they solve fundamentally different problems. Aurora handles structured relational data with SQL; Neptune handles highly connected data where relationships between entities are the primary query pattern.

Learn more

What's the difference between Amazon Aurora and Amazon DynamoDB?

Both are fully managed AWS database services, but they serve fundamentally different data models and access patterns. Aurora is a relational database for structured data with relationships; DynamoDB is a NoSQL key-value store built for single-digit millisecond performance at any scale.

Learn more

What's the difference between Amazon RDS and Amazon Redshift?

Both run SQL queries, but they're optimized for fundamentally different workloads. RDS handles transactional operations (inserts, updates, lookups) for your application. Redshift handles analytical queries (aggregations, scans, joins across billions of rows) for your data team.

Learn more

Did you find what you were looking for today?

Let us know so we can improve the quality of the content on our pages