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Apache Kafka vs. Apache Pulsar is not a universal performance contest. Kafka is usually the safer default for partitioned event streaming, CDC, analytics, and stream processing because its ecosystem is broader. Pulsar is often the stronger candidate for queue-plus-stream workloads, first-class multi-tenancy, native geo-replication, and independently scalable serving and storage.
Both platforms provide durable topics, replay, fan-out, retention, replication, client APIs, and integrations for stream processing. The defensible choice depends on workload semantics, operating model, existing skills, delivery guarantees, retention, geography, and the total cost of running the platform—not on a generic claim that one is faster or cheaper.
Key takeaways
- Kafka is generally the safer default for conventional enterprise event streaming, CDC, Kafka Connect, Kafka Streams, and partition-based workloads.
- Pulsar deserves priority evaluation when one platform must combine streaming with queue-like subscriptions, first-class tenants and namespaces, native multi-cluster replication, or independently scalable brokers and storage.
- Kafka guarantees ordering within a partition, while Pulsar’s ordering depends on topic partitioning, producer configuration, keys, and subscription mode; neither platform provides high-parallelism global ordering without a bottleneck.
- Kafka 4.0, announced on March 18, 2025, uses KRaft mode by default and does not require a separate ZooKeeper ensemble in the standard architecture.
- “Exactly once” describes a bounded processing design, not automatic duplicate-free effects in an arbitrary database, API, or external system.
- Managed-service architecture, pricing, support, and feature maturity can reverse a comparison based only on the open-source projects.
What is the difference between Apache Kafka and Apache Pulsar?
Apache Kafka is primarily a distributed, partitioned commit log used for durable event streaming. Apache Pulsar is a distributed messaging and streaming platform designed to support both stream-oriented and queue-oriented workloads. The two products overlap substantially, but their default abstractions and scaling models differ.
| Decision area | Apache Kafka | Apache Pulsar |
|---|---|---|
| Core abstraction | Distributed, partitioned commit log | Distributed messaging and streaming platform |
| Primary scaling unit | Topic partitions distributed across brokers | Topics and partitions served by brokers, with persistence handled separately by BookKeeper |
| Consumer model | Consumer groups with partition assignment | Exclusive, failover, shared, and key-shared subscription modes |
| Storage model | Broker-served log segments, with configurable remote or tiered storage | Brokers for traffic and dispatch, BookKeeper bookies for persistent storage, plus storage offload options |
| Messaging orientation | Historically stream-first | Designed for both messaging and streaming |
| Multi-tenancy | ACLs, quotas, conventions, and deployment-level isolation | First-class tenants, namespaces, policies, and resource controls |
| Geo-replication | Commonly implemented with cross-cluster mirroring or managed-service features | Native multi-cluster replication and client failover capabilities |
| Ecosystem | Larger and more mature overall | Smaller, but differentiated by flexible subscriptions, multi-tenancy, and layered storage |
The practical question is therefore not “Which platform wins?” The practical question is whether the organization values Kafka’s ecosystem-led operating model or Pulsar’s messaging-and-streaming platform model enough to accept the corresponding trade-offs.
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How does each platform’s architecture affect operations?
Kafka brokers handle client requests, partition leadership, replication, and local log storage. Kafka scales mainly by distributing partitions across brokers. The partition count therefore affects parallelism, throughput, ordering, consumer assignment, rebalancing, metadata, and recovery. The Apache Kafka documentation describes topics as partitioned and records with the same key as being written to the same partition.
Pulsar separates client-serving brokers from persistent storage. Brokers handle client traffic and dispatch, while Apache BookKeeper bookies store messages in ledgers. The Pulsar architecture documentation describes this layered design, which can allow serving capacity and storage capacity to scale more independently than in a traditional Kafka deployment.
| Architecture choice | Potential advantage | Operational cost |
|---|---|---|
| Kafka broker-local log model | Familiar partition-based design with a comparatively direct operational model | Partition placement, disk utilization, replication, reassignment, and recovery must be planned together |
| Pulsar brokers plus BookKeeper | Broker traffic and persistent storage can be scaled and managed more independently | Operators must understand brokers, bookies, ledgers, metadata, storage policies, and additional failure domains |
| Kafka with tiered storage | Completed log segments can move to remote storage such as HDFS or S3 | Tiered storage must be configured; implementation, support, and limitations vary by version, distribution, and managed service |
| Pulsar with tiered storage | Older data can be offloaded to long-term systems including Amazon S3 and Google Cloud Storage | Cold replay, object-storage requests, retrieval, and network charges must be modeled |
Pulsar’s separation can be attractive when consumers have uneven read patterns, retention greatly exceeds hot-serving requirements, many tenants share one platform, or brokers need to scale independently from disk capacity. Separation does not automatically mean lower total operational complexity: Pulsar introduces more subsystems for a self-managed team to understand and monitor.
Kafka’s historical broker-and-disk comparison also needs updating. Kafka has configurable remote or tiered storage in the 4.0 documentation, including remote log segments backed by systems such as HDFS or S3. The Kafka tiered-storage documentation says that the documented implementation does not support compacted topics, so the comparison must be qualified by version and distribution.
Which platform has the better consumer and messaging model?
Kafka’s standard model is consumer groups over topic partitions. Within one consumer group, a partition is assigned to one consumer at a time. Different consumer groups independently read the same records, making fan-out straightforward. Consumer-group parallelism is bounded by the number of partitions and the group’s assignment.
Pulsar provides more explicit subscription modes. Pulsar’s messaging documentation defines the following patterns:
| Pulsar subscription | Behavior | Useful when |
|---|---|---|
| Exclusive | One consumer owns the subscription | A single active consumer should process the subscription |
| Failover | One consumer is active while other consumers remain available as standby | An application needs a primary consumer with failover |
| Shared | Messages are distributed among consumers; global ordering is not preserved | Consumers share queue-like work and throughput matters more than subscription-wide ordering |
| Key_Shared | Messages with the same key can remain associated with the same consumer while consumption is parallelized | Work needs per-key affinity without assigning an entire topic partition to one consumer |
Pulsar’s flexibility matters when an application needs individual acknowledgements, queue-like work distribution, standby consumers, or multiple delivery patterns. Kafka’s consumer-group model is simpler and deeply integrated with partitioning, replay, Kafka Streams, and the wider Kafka ecosystem. More subscription choices are not automatically better if the application only needs standard partition assignment.
How do Kafka and Pulsar handle ordering?
Kafka ordering is guaranteed within a partition, not across an entire topic. A topic-wide total order requires one partition, which creates a parallelism bottleneck. Key-based partitioning routes records sharing a key to the same partition, allowing events for a customer, account, or device to remain ordered while unrelated keys are processed in parallel. Kafka’s design documentation explains the partition and ordering model.
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Decision rule: if the business invariant is “all events for customer X must be processed in order,” route or partition by customer key in either platform. If the invariant is “every event globally must be ordered,” neither platform can provide that at high parallelism without creating a bottleneck.
Partition and topic design can create permanent or expensive constraints. Too few Kafka partitions limit future parallelism. Too many partitions increase metadata, file, rebalance, monitoring, and recovery overhead. Increasing Kafka partition count can also change key-to-partition mapping, so partition growth should be part of the ordering and migration plan.
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Which platform provides stronger delivery guarantees?
Kafka supports transactions and exactly-once processing patterns, particularly through a transactional producer/consumer workflow or Kafka Streams. Kafka transactions can atomically write records and consumer offsets within the Kafka workflow. The Kafka transaction design documentation describes the foundation for these patterns.
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Exactly-once processing is not automatic end-to-end deduplication. A transaction covering Kafka records does not automatically make an HTTP call, database write, payment, email, or other external side effect exactly once. External systems need idempotency, an appropriate transaction boundary, a deduplication key, or a coordinated transactional design.
Pulsar designs require the same precision. Separate at-least-once delivery, producer deduplication, acknowledgement and redelivery behavior, transactions across messages or topics, stream-processing guarantees, connector behavior, and exactly-once effects in an external database. The official Pulsar sources in this comparison establish Pulsar’s architecture, replication, retention, and subscription behavior but do not support a broad claim that Pulsar’s end-to-end transactional semantics are equivalent to Kafka’s across every client, API, connector, and sink.
| Claim | What the claim actually needs to specify |
|---|---|
| Exactly-once production | Producer client, broker version, acknowledgement behavior, retries, and deduplication scope |
| Exactly-once stream processing | Processing engine, checkpoint or transaction model, state store, and failure recovery |
| Exactly-once connector delivery | Connector implementation, offset handling, sink transaction support, and retry behavior |
| Exactly-once business effect | External database or API idempotency, transaction participation, and duplicate-recovery logic |
Use the phrase “exactly once” only after naming the scope: producer, broker, stream-processing pipeline, connector, sink, or external side effect.
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How do retention, replay, compaction, and tiered storage compare?
Both platforms retain events for later replay, but their storage mechanics and state-rebuild features differ.
Kafka retention and compaction
Kafka retains topic data according to time or size policies. Log compaction retains at least the latest known value for each key within a partition, which is useful for rebuilding caches, materialized views, and application state. Kafka’s ecosystem also makes replay, CDC, schema management, and stateful stream processing familiar to many data teams.
Kafka tiered storage can move completed log segments to remote storage, but the 4.0 documentation says the feature is configurable rather than universally enabled and lists no support for compacted topics in that implementation. A design that depends on compacted state topics should verify the exact Kafka distribution and storage implementation.
Pulsar retention and offload
Pulsar supports retention and backlog management and can offload older data to long-term object storage, including Amazon S3 and Google Cloud Storage, according to the Pulsar concepts overview. This can be attractive when event history is long-lived but only a small working set needs hot serving.
Long retention does not automatically make Pulsar cheaper. Model object-storage request charges, retrieval and read amplification, cross-availability-zone or cross-region transfer, recovery time, replay speed, and the frequency with which consumers reread cold data. Also verify whether the application needs compaction, tombstones, or a state-rebuild workflow that is better supported by Kafka tooling.
Which platform is better for multi-region and geo-replicated systems?
Pulsar exposes multi-cluster replication and client failover as part of its platform model. The Pulsar concepts documentation describes multiple clusters within a Pulsar instance and geo-replication capabilities.
Kafka also supports cross-data-center and cross-region replication. Kafka deployments commonly use cross-cluster mirroring, including MirrorMaker 2, or managed-service replication. The Kafka geo-replication documentation covers the relevant operational model.
The accurate distinction is not “Pulsar supports geo-replication and Kafka does not.” The distinction is that Pulsar treats multi-cluster replication as a native part of the platform model, while Kafka commonly uses separate mirroring or managed-service tooling. Managed Kafka providers may integrate replication, networking, and failover closely enough to narrow the practical difference.
Either platform requires explicit decisions about active/passive versus active/active operation, topic naming, offset translation, schema and ACL replication, conflict handling, consumer failover, recovery-point objectives, and recovery-time objectives. Replicating records does not guarantee that offsets, application state, identities, schemas, and external side effects fail over consistently. Test the complete recovery process.
Why does Pulsar’s multi-tenancy model matter?
Pulsar’s tenant and namespace model is useful when one platform serves multiple teams, products, customers, or environments with different policies. Pulsar documents tenants, namespaces, authentication, authorization, quotas, retention, replication, and other namespace-level controls in its multi-tenancy documentation.
First-class boundaries can support separate ownership, retention, replication, quotas, and operational policy without relying only on naming conventions and deployment-level controls. This is especially relevant to a multi-tenant SaaS platform or a shared internal event backbone.
Kafka can also support multiple tenants through ACLs, quotas, naming standards, separate clusters, and platform automation. However, the design may require more conventions and external control-plane work. Kafka’s high topic and partition counts also need careful capacity planning because metadata, file handles, controller activity, rebalances, monitoring, and recovery can become material operational factors.
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The Pulsar project homepage advertises support for up to one million unique topics in a single cluster. That is a project capability statement, not a universal production limit. Practical capacity depends on topic activity, partitions, subscriptions, message rate, retention, replication, metadata, client behavior, and recovery objectives. Do not compare that headline with a Kafka topic count unless all of those variables are normalized.
Which ecosystem and developer experience should you choose?
Kafka usually wins on ecosystem breadth, hiring familiarity, operational precedent, and third-party integration. The Kafka documentation identifies Admin, Producer, Consumer, Kafka Streams, and Kafka Connect APIs as core parts of the platform. Kafka is also common in CDC pipelines, schema-management workflows, observability platforms, cloud offerings, and data engineering tools.
Kafka’s strongest ecosystem advantages include Kafka Connect, Kafka Streams, schema-registry integrations, CDC connectors, external stream processors, cloud-provider offerings, and a large body of operational knowledge. Existing Kafka expertise can reduce adoption, migration, and incident-response risk.
Pulsar offers native clients, Pulsar Functions, Pulsar IO connectors, flexible subscriptions, multi-tenancy, and geo-replication. Selected distributions and managed services also expose Kafka-compatible protocols. “Kafka-compatible” does not mean identical to Kafka: verify transactions, consumer-group behavior, compaction, ACLs, administrative APIs, connectors, schemas, rebalance protocols, offset semantics, and exactly-once workflows.
Pulsar can win when its native capabilities eliminate application-level workarounds or remove the need to combine a stream platform with a separate queue, multi-tenant messaging layer, or replication system. Kafka usually wins when the required features already exist in its mature ecosystem.
What changed in Kafka 4.0 operations?
Kafka 4.0 was announced on March 18, 2025. The Kafka 4.0 release announcement states that Kafka 4.0 runs in KRaft mode by default and no longer requires a separate ZooKeeper ensemble in the standard architecture.
Current Kafka comparisons should therefore not say that Kafka requires ZooKeeper. Kafka operations still include partition placement and reassignment, broker disk utilization, replication lag, under-replicated partitions, controller quorum management, consumer rebalances, partition-count planning, JVM and page-cache behavior, tiered-storage configuration where used, and cross-region mirroring.
Pulsar self-management includes broker placement and load balancing, BookKeeper bookie capacity and journal performance, ledger and ensemble configuration, metadata and configuration-store operation, namespace policies, backlog growth, replication, storage offload, and bookie recovery. Managed distributions may abstract or replace some components, so the exact deployment and version matter.
Which platform is better for specific workloads?
| Workload or requirement | First platform to evaluate | Reason |
|---|---|---|
| CDC and analytical pipelines | Kafka | Kafka Connect, connector availability, schema tooling, and established data-platform integrations are a strong fit. |
| Event-driven microservices | Kafka by default | Kafka’s partitioned log and consumer groups suit common event-streaming designs; evaluate Pulsar when queue-specific acknowledgement or subscription behavior is required. |
| Multi-tenant SaaS messaging | Pulsar | Tenants, namespaces, policies, quotas, retention, replication, and ownership boundaries are central capabilities. |
| Global messaging | Pulsar candidate | Native multi-cluster replication and client failover can reduce platform-level work, but Kafka managed services may offer comparable operational features. |
| Long-term event retention | Both | Pulsar’s offload model is attractive, while Kafka’s compaction, replay, and ecosystem may be more important. |
| Kafka Streams applications | Kafka | Kafka Streams is deliberately integrated with Kafka’s storage and protocol model. |
| Queue-like work distribution | Pulsar candidate | Shared and key-shared subscriptions provide natural queue patterns; Kafka can implement queues through consumer groups. |
| Existing Kafka estate | Kafka | Keeping the existing platform preserves client knowledge, tooling, schemas, operations, and deployment automation. |
| Independent storage and serving scale | Pulsar candidate | Brokers and BookKeeper provide a layered architecture, although the additional subsystems increase operational scope. |
When should you choose Kafka?
Choose Kafka when the main requirement is durable event streaming around topics, partitions, consumer groups, CDC, analytics, and stream processing. Kafka is particularly defensible when the organization values the broadest ecosystem and hiring pool, needs Kafka compatibility, already operates Kafka, or relies on Kafka Connect, Kafka Streams, transactions, or log compaction.
When should you choose Pulsar?
Choose Pulsar when one platform must serve both streaming and queue-like messaging workloads, when individual acknowledgements or flexible fan-out patterns matter, or when multi-tenancy, many clusters, geo-replication, long retention, and storage separation are first-class requirements. Pulsar is a better candidate when the team is prepared to operate or buy a platform built around brokers plus persistent storage services.
How should you compare total cost of ownership?
The cheaper platform is workload-dependent. A credible cost model includes infrastructure and the engineering labor required to keep the system reliable.
| Cost category | Questions to model |
|---|---|
| Compute | How much broker, bookie, stream-processing, connector, and control-plane capacity is required? |
| Storage | What are the hot-retention, long-retention, replication, compaction, and backup requirements? |
| Object storage | What are request, retrieval, read-amplification, and cold-replay costs? |
| Network | What are the cross-AZ, cross-region, ingress, egress, and replication charges? |
| Platform services | Are schema management, governance, connectors, stream processing, observability, and support billed separately? |
| Operations | How much platform-engineering, on-call, upgrade, security, backup, and disaster-recovery work is required? |
| Migration | What will client rewrites, replay, dual publishing, data validation, cutover, and rollback cost? |
| Lock-in | Which APIs, connectors, protocols, storage engines, or managed-service features make a later move expensive? |
Managed platforms can materially change the comparison. Confluent Cloud’s architecture documentation describes a cloud-native Kafka re-architecture with independent autoscaling of compute and storage in its managed platform. Its billing documentation describes usage dimensions including compute and post-replication storage. Exact cost depends on region, throughput, storage, networking, connectors, and add-ons.
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Amazon MSK is another managed Apache Kafka option for organizations already standardized on AWS. Its pricing page should be evaluated with the target AWS region, capacity model, storage, traffic, and retention assumptions rather than with a generic monthly estimate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you benchmark Kafka and Pulsar fairly?
A benchmark is useful only when it represents the production workload. Do not publish or rely on generic claims such as “Pulsar is faster” or “Kafka is cheaper” without controlling the test.
- Define the workload: record size, serialization, key distribution, producer count, consumer count, topic and partition count, retention, replication, acknowledgement level, and replay behavior.
- Match infrastructure: use equivalent storage media, network bandwidth, availability-zone layout, instance types, cloud region, TLS, authentication, compression, and runtime versions.
- Test normal traffic: measure sustained ingress and egress throughput, CPU, memory, disk, I/O, consumer lag, and p50, p95, p99, and p99.9 latency.
- Test failure: stop brokers or bookies, observe tail latency, replication lag, recovery time, consumer rebalances, duplicate work, and backlog catch-up rate.
- Test retention and replay: measure storage growth, cold-read latency, object-storage charges, network cost, and the time needed to rebuild state.
- Test the application path: include schema services, connectors, stream processing, sinks, checkpointing, and external side effects instead of benchmarking only the broker.
- Record operator effort: measure deployment, scaling, partition or topic changes, upgrades, alerting, incident response, and disaster recovery.
Vendor benchmarks can identify useful test dimensions, but they are not neutral proof. A small proof of concept using the intended message sizes, key distribution, retention, failure modes, and consumer behavior is more persuasive than a headline throughput number.
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A migration is not merely a protocol or endpoint change. Plan the semantic, operational, and recovery differences before moving production consumers.
| Migration area | Questions to answer |
|---|---|
| Clients and protocols | Can existing clients connect, and do the target APIs preserve transactions, acknowledgements, retries, and administration behavior? |
| Topic and partition mapping | How do source topics, partitions, keys, ordering, retention, and compaction map to the target? |
| Offsets | How will consumer positions be translated, validated, and recovered if the target uses different offset semantics? |
| Schemas | Will serialization, schema compatibility, subjects, versioning, and evolution rules remain valid? |
| Identity and security | How will ACLs, tenants, namespaces, authentication, authorization, and service identities map? |
| Connectors and processing | Do connector delivery guarantees, state stores, checkpoints, retries, and dead-letter behavior remain equivalent? |
| Cutover | Will the migration use dual publishing, mirroring, backfill, a staged consumer move, or a maintenance window? |
| Rollback | How will the team stop divergent writes, preserve ordering, reconcile duplicates, and return to the source platform? |
Kafka-compatible access can reduce client changes, but compatibility must be tested feature by feature. Check transactions, consumer groups, compaction, ACLs, administrative APIs, connectors, schema tooling, rebalance protocols, offsets, and exactly-once workflows. A migration that preserves the wire protocol but changes acknowledgement or ordering behavior can still break an application.
What are the most common Kafka and Pulsar design mistakes?
- Assuming one platform is universally faster: throughput and latency depend on batching, message size, replication, storage, network, consumer behavior, and failure conditions.
- Assuming Pulsar is always cheaper: BookKeeper, replication, cross-AZ traffic, object-storage requests, and operational labor can offset storage-separation advantages.
- Assuming Kafka cannot replicate across regions: Kafka supports cross-cluster replication through mirroring and managed-service features.
- Assuming Kafka requires ZooKeeper: Kafka 4.0 uses KRaft mode by default in the standard architecture.
- Assuming Kafka has no tiered storage: Kafka’s 4.0 documentation describes configurable remote storage, with version-specific limitations.
- Assuming shared consumption preserves order: Kafka consumer groups and Pulsar shared subscriptions distribute work; they do not provide unrestricted global ordering.
- Assuming exactly once means no duplicates anywhere: broker and stream transactions do not automatically cover external databases or APIs.
- Assuming one topic per tenant is free: topic, partition, subscription, metadata, retention, and monitoring counts must be included in capacity planning.
- Assuming Kafka-compatible means drop-in equivalent: protocol compatibility does not guarantee feature, administrative, or semantic compatibility.
- Assuming managed services are the same as open source: providers may add proprietary control planes, storage engines, autoscaling, billing dimensions, and profile-dependent features.
Final decision framework
Choose Kafka for ecosystem-led, partitioned event streaming. Kafka is the stronger default for CDC, analytics, Kafka Connect, Kafka Streams, compacted topics, established enterprise tooling, and teams that already operate Kafka.
Choose Pulsar for platform-led messaging and streaming when queue semantics, multiple subscription modes, first-class multi-tenancy, native geo-replication, long retention, or independent broker-and-storage scaling are central to the architecture.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallChoose a managed service when the organization lacks a dedicated streaming platform team or when managed replication, upgrades, observability, support, and elastic capacity are worth the service premium. Compare the provider’s actual profile, semantics, quotas, SLA, region, network model, and billing—not only the Kafka or Pulsar label.
The safest final recommendation is workload-specific: Kafka is the defensible conventional default, while Pulsar deserves serious preference for multi-tenant, multi-region, queue-plus-stream platforms with storage separation. Run a representative proof of concept before committing to a migration or a large self-managed deployment.
Frequently Asked Questions
Is Apache Kafka better than Apache Pulsar?
Apache Kafka is usually the safer default for mainstream event streaming because its ecosystem, tooling, hiring pool, and operational familiarity are broader. Apache Pulsar can be better when queue-like subscriptions, first-class multi-tenancy, native geo-replication, or independently scalable brokers and storage are core requirements.
Is Apache Pulsar faster or cheaper than Kafka?
Neither Apache Pulsar nor Apache Kafka is universally faster or cheaper. Performance and total cost depend on message size, batching, replication, storage, retention, network topology, consumer behavior, managed-service pricing, and operational labor; a representative proof of concept is required.
Does Kafka still require ZooKeeper?
Kafka 4.0 uses KRaft mode by default and does not require a separate ZooKeeper ensemble in the standard architecture. Older Kafka versions and specific legacy deployments may have different requirements, so the deployed version must be checked.
Can Kafka and Pulsar provide exactly-once processing?
Kafka supports exactly-once processing patterns in specified transactional workflows, while any Pulsar guarantee must be checked for the exact client, processing engine, connector, and sink. Neither platform automatically makes arbitrary external database or API side effects exactly once without idempotency or coordinated transactions.
The Bottom Line
Bottom line: Start with Kafka unless your requirements clearly favor Pulsar’s queue-and-stream model, tenant isolation, native multi-cluster replication, or storage separation. Then validate the choice with a workload-specific benchmark and a full cost model that includes replication, network, retention, managed services, and engineering labor.
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