Apache Kafka vs Apache Pulsar

Pulsar offers a Kafka-on-Pulsar (KoP) wrapper that lets Kafka clients connect to Pulsar brokers. For basic produce/consume operations, this works well. For advanced Kafka features — transactions, exactly-once semantics, certain consumer group behaviors, idempotent producers — compatibility is imperfect. In practice, most teams migrating end up rewriting clients to use native Pulsar APIs to unlock Pulsar's features like multi-subscription modes and namespaces. Treat KoP as a migration bridge, not a permanent compatibility layer.

Pulsar's multi-tenancy is built into the architecture: tenants contain namespaces, namespaces contain topics, and each tenant/namespace has its own isolation policies, quotas, auth, and rate limits. Many tenants can share one Pulsar cluster with strong isolation. Kafka's answer to multi-tenancy is weaker — ACLs and quotas exist but don't provide the same level of isolation, and the practical result is most large Kafka deployments run separate clusters per tenant. If you're building an internal streaming platform for dozens of teams, Pulsar's design genuinely makes this easier.

Yes. Pulsar has been in production at Yahoo (where it was invented), Tencent, Verizon Media, Comcast, and others for years, handling millions of messages per second at scale. The codebase is mature; the core features are stable. What's less mature is the surrounding ecosystem — managed services, connectors, observability tooling — all smaller than Kafka's. Pulsar itself is production-ready; the question is whether the ecosystem gap matters for your specific integration needs.

Pulsar had tiered storage first and the implementation is mature — offload older segments from BookKeeper to S3/GCS/Azure Blob, read transparently during catch-up reads. Kafka added tiered storage via KIP-405 in recent versions; the feature is newer but generally works well now. Both achieve the same goal (cost-effective long retention), but Pulsar's separation of compute and storage makes tiered storage feel more natural architecturally. For very long retention (months to years), both are viable choices.

Pulsar supports four subscription modes: Exclusive (one consumer per subscription), Shared (load-balanced across consumers — similar to a queue), Key-Shared (ordered per key but parallel across keys), and Failover (one active consumer, others standby). Kafka primarily supports consumer-group semantics, which are closest to Pulsar's Failover mode. The Shared and Key-Shared modes are genuinely useful for work-queue patterns where you want parallelism without partition-level coupling. If you need classic message-queue semantics (like RabbitMQ) plus streaming semantics, Pulsar covers both; Kafka forces partition-based parallelism.

Kafka operations today are a well-known problem — many playbooks, monitoring dashboards, tuning guides, experienced operators on the market. Pulsar operations are less commoditized: you're running brokers plus BookKeeper plus optionally Pulsar Functions, monitoring and debugging requires more custom work, and finding experienced Pulsar operators is harder. Managed services (StreamNative Cloud) dramatically reduce this gap, but the managed Pulsar options are fewer than managed Kafka. Budget more operational learning for Pulsar unless you go managed.

Yes, better than Kafka can. Pulsar's Shared and Key-Shared subscription modes provide proper queue semantics (work distribution without partition coupling), while Kafka's model is strictly partition-based. For teams who need both message-queue patterns (task distribution) and streaming patterns (event logs) in one platform, Pulsar is genuinely well-suited. Kafka works for queue-like workloads but feels forced because every consumer must be mapped to partitions.