Quick decision
- Choose Kafka if
- Choose Pulsar if
- You have existing Kafka workloads or Kafka client libraries in production.
- Your primary pattern is event streaming: event sourcing, log aggregation, CDC, clickstream, or telemetry.
- You want native Kafka API behavior with full compatibility for producers, consumers, consumer groups, and Kafka Connect.
- You are migrating from Amazon MSK, Confluent Cloud, or self-managed Kafka and want a drop-in replacement.
Feature comparison
| Capability | Kafka Cluster | Pulsar Cluster |
|---|---|---|
| Primary protocol | Kafka (native) | Pulsar (native) |
| Multi-protocol on same cluster | Kafka only | Pulsar, Kafka (via KSN), MQTT (via MoP), REST |
| Multi-tenancy | Topic-level ACLs | Tenant and namespace isolation |
| Geo-replication | UniLink | Built-in, active-active across regions |
| Queue semantics | Queues for Kafka | Shared subscriptions with per-message acknowledgment |
| Consumer model | Partition failover + queue | Flexible subscriptions (exclusive, shared, failover, key-shared) |
| Lakehouse integration | Built-in with Ursa Engine | Built-in (Ursa) or available as add-on (Classic) |
| Status | Public Preview | GA |
Messaging vs. streaming
The core difference between the two services maps to two data movement patterns: messaging and streaming.Messaging (Pulsar strength)
Messaging is consumer-centric delivery. Messages are dispatched to consumers, acknowledged individually, and removed from the backlog once processed. This model works well when you need:- Competing consumers: Multiple workers sharing a workload through shared subscriptions.
- Per-message acknowledgment: Track processing at the individual message level.
- Flexible routing: Route messages to different consumers based on subscription type (exclusive, shared, failover, key-shared).
The messaging features in Pulsar have seen widespread adoption and have been running in production for many years. Queue support for Kafka is relatively new — evaluate carefully before adopting it for production messaging workloads.
Streaming (Kafka strength)
Streaming is log-centric processing. Events are appended to a durable, ordered log. Consumers read at their own pace using offsets and can replay the log at any time. This model works well when you need:- Event replay: Reprocess historical data by resetting offsets.
- Ordered processing: Maintain strict ordering within partitions.
- Decoupled consumers: Multiple independent consumer groups reading the same log without coordination.
Powered by Ursa Engine
Both services are built on the Lakestream architectural paradigm and powered by the Ursa Engine, which provides:- Leaderless brokers: Any broker handles any partition, eliminating leader bottlenecks.
- Compute/storage separation: Provides the flexibility to choose between local disks for low latency and shared storage (lakehouse storage) for cost-efficiency.
- Lakehouse-native storage: Data stored in open formats for direct query by analytics engines.
- Cost reduction: Up to 95% lower infrastructure costs compared to traditional architectures by eliminating inter-AZ replication.
Next steps
Get Started with Kafka Service
Native Apache Kafka on Ursa. 100% Kafka compatible, leaderless, and lakehouse-native.
Explore Pulsar Service
Apache Pulsar with multi-protocol support for messaging and streaming workloads.