Azure Event Hubs vs Apache Kafka: Key Differences

Apache Kafka is the default choice for event streaming when teams are not specifically constrained to Azure. It is battle-tested, widely understood, and platform-agnostic. Azure Event Hubs exists because running Kafka yourself is not trivial: it requires ZooKeeper (or KRaft in newer versions), careful partition planning, broker maintenance, and operational expertise your team may not have.

The practical question for most Azure-based teams is not which is technically superior. It is whether the operational burden of Kafka is worth what you gain in flexibility and control, compared to Event Hubs doing the same job as a managed service where you pay for usage and someone else manages the infrastructure.

How they compare architecturally

Apache Kafka is a distributed commit log. Producers write events to topics, which are partitioned across brokers. Consumers read from partitions at their own pace, maintaining offsets. You manage the brokers, the replication factor, partition counts, retention settings, and scaling. Every configuration decision is yours.

Azure Event Hubs shares the same conceptual model: topics (called event hubs), partitions, consumers maintaining their position in the stream (via checkpointing in Azure Storage or Cosmos DB). The difference is that Microsoft manages the broker infrastructure. You configure throughput units (Standard tier) or processing units (Premium/Dedicated), set retention, and write code against the SDK.

Event Hubs exposes a Kafka-compatible protocol endpoint. Applications using the Kafka producer and consumer APIs can connect to Event Hubs without code changes, by pointing to the Event Hubs endpoint and using the namespace connection string as credentials. This is the migration path for Kafka workloads moving to Azure: change the bootstrap server, update the authentication configuration, and the application works as-is.

The managed service trade-off

What you give up with Event Hubs compared to self-managed Kafka:

Partition count is fixed at creation. In Standard tier, you can choose 32 partitions per event hub. In Premium and Dedicated tiers you get more. But unlike Kafka, you cannot increase partition count on an existing event hub. Plan partition counts based on your expected consumer parallelism before creating the resource.

Less granular configuration. Kafka exposes hundreds of tunable parameters: exactly-once semantics, custom log compaction, complex ACLs, per-topic configurations that override broker defaults. Event Hubs does not expose this level of control. For most workloads this does not matter. For workloads that need exactly-once semantics, specific log compaction behaviour, or Kafka Streams (the stream processing library), self-managed Kafka is the better choice.

Kafka Streams is not supported. Event Hubs' Kafka protocol compatibility covers producers and consumers. Kafka Streams, the client-side stream processing library, relies on internal Kafka topics for state management and does not work with Event Hubs. Use Azure Stream Analytics or Azure Functions with the Event Hubs trigger for stream processing on Event Hubs.

What you gain:

Zero infrastructure to manage. No brokers, no ZooKeeper, no disk planning, no replication configuration. Azure handles availability, durability, and scaling within the tier.

Autoscaling within tiers. Standard tier Event Hubs can enable Auto-inflate, which automatically increases throughput units when approaching the limit. Premium tier scales processing units automatically.

Native Azure integration. Event Hubs output connectors exist for Azure Stream Analytics, Azure Functions, Synapse Analytics, and Azure Data Lake Storage. Capture writes raw events directly to Blob Storage or Data Lake on a schedule, without consumer code.

Consumption-based pricing. Standard tier charges per throughput unit-hour plus per-million events ingested. Premium tier charges per processing unit-hour. There is no fixed infrastructure cost: scale down to one throughput unit in quiet periods.

Performance comparison

Both platforms can handle millions of events per second. The differences are at the margins:

Event Hubs Standard tier delivers typical end-to-end latency of approximately 10ms. Apache Kafka delivers 20-80ms depending on configuration, producer acknowledgment settings, and flush intervals. For most real-time analytics use cases, both are fast enough. For applications with strict latency requirements in the single-digit millisecond range, Event Hubs Premium or Dedicated tier is designed to target sub-10ms.

Kafka's theoretical throughput ceiling is higher for a given hardware investment because you control the hardware. Event Hubs Dedicated tier removes most practical throughput constraints for Azure workloads.

Security comparison

Event Hubs uses Azure RBAC and Shared Access Signatures for authentication. TLS is enforced for all connections. Integration with Microsoft Entra ID is built in: Kafka producers and consumers can authenticate via Entra ID OAuth tokens rather than connection strings, which is better for managed identity scenarios where you want to avoid storing credentials.

Kafka security requires explicit configuration: TLS for transport, SASL for authentication (PLAIN, SCRAM, Kerberos), and ACLs for authorisation. It is powerful and flexible, but it is also something you configure and maintain. An improperly secured Kafka cluster is a common finding in security reviews.

When to choose Event Hubs

  • You are building a new streaming workload on Azure and do not have existing Kafka investment
  • You want to run Kafka-compatible applications without managing Kafka infrastructure
  • Your team does not have Kafka operational expertise
  • You want seamless integration with Azure analytics services (Stream Analytics, Synapse, Data Lake)
  • Consumption-based pricing suits your workload (variable throughput, cost predictability)

When to choose self-managed Kafka

  • You are migrating an existing on-premise Kafka deployment and need feature parity (Kafka Streams, exactly-once semantics, custom topic configurations)
  • You need to run Kafka workloads on multiple clouds or on-premise without Azure dependency
  • You have existing Kafka expertise and tooling investment (Kafka Connect, Kafka Streams, ksqlDB)
  • Your throughput requirements exceed Event Hubs limits for your tier at acceptable cost

Event Hubs as a Kafka migration target

For teams running Kafka on-premise or in VMs and wanting to reduce operational burden, Event Hubs Kafka protocol compatibility makes migration straightforward for producer/consumer workloads:

  1. Create an Event Hubs namespace with Kafka protocol enabled (Premium or Dedicated tier)
  2. Create event hubs corresponding to your Kafka topics
  3. Update producer and consumer configuration to point to the Event Hubs bootstrap endpoint
  4. Replace SASL credentials with Entra ID authentication or SAS tokens
  5. Update consumer checkpoint storage to Azure Storage

Kafka Connect connectors, Kafka Streams applications, and anything using internal Kafka topics for state management require code changes or replacement with Azure-native equivalents.

Where Critical Cloud comes in

Choosing the right streaming architecture, migrating from Kafka to Event Hubs, and operating the result with the observability needed to catch consumer lag, throughput drops, and partition hot spots before they affect downstream systems is what we do for technology-led businesses on Azure. As the world's first Powered by Datadog accredited partner, we monitor Event Hubs consumer lag, throughput unit utilisation, and throttling as live signals alongside application performance. See how Critical Support works.