Pulsar 4.x vs Kafka 4.0 for AI Workloads: The Million-Topic Problem

TL;DR — Both Kafka 4.0 and Pulsar 4.x are post-ZooKeeper now (KRaft and Oxia respectively). Kafka is faster on raw throughput. Pulsar is the only sane choice when every customer needs its own topic, when you need built-in geo-replication, and when you want compute and storage to scale independently.

The pattern

You're building a SaaS AI platform. Every customer is a tenant. You want per-customer audit topics, per-customer event isolation, per-customer retention policies. Kafka makes this hard — partitions, ACLs, and broker memory all suffer at 100k+ topics. Pulsar makes this trivial — multi-tenancy is first-class, topics are cheap, BookKeeper handles the storage tier.

How it works (architecture)

flowchart TB
 subgraph Pulsar
 P_Brokers[Stateless brokers] -->|reads/writes| BK[BookKeeper bookies]
 Oxia[Oxia coordination] -.metadata.- P_Brokers
 end
 subgraph Kafka
 K_Brokers[Brokers tier 1: serve + store]
 K_Brokers -.metadata.- KRaft[KRaft controller quorum]
 end
 Producer --> Pulsar
 Producer --> Kafka

Pulsar's two-tier (broker + bookie) lets you scale brokers (CPU, network) independently from bookies (storage). Kafka's single-tier puts both on the broker; tiered storage (KIP-405) moves cold segments to S3.

CallSphere implementation

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Build steps with code

1. Decide topology: single-tenant + Kafka, or multi-tenant + Pulsar.
2. For Pulsar: install via Helm, configure tenants and namespaces.
3. Wire BookKeeper with at least 3 bookies for replication.
4. Use TopicPolicies for per-tenant retention.
5. Pulsar IO connectors replace Kafka Connect.
6. Geo-replication: enable at namespace level, point to remote cluster.
7. Benchmark: don't trust marketing — run OpenMessaging benchmark on your hardware.

import org.apache.pulsar.client.api.*;

PulsarClient client = PulsarClient.builder().serviceUrl("pulsar://pulsar:6650").build();

Producer<byte[]> producer = client.newProducer().topic("persistent://callsphere/real-estate/call-events").compressionType(CompressionType.ZSTD).create();

producer.newMessage().key(callId).property("ce-type", "com.callsphere.call.completed.v1").value(payload).send();

Consumer<byte[]> consumer = client.newConsumer().topic("persistent://callsphere/real-estate/call-events").subscriptionName("embeddings").subscriptionType(SubscriptionType.Shared).subscribe();

Common pitfalls

• Picking Pulsar for throughput — Kafka still wins benchmarks; pick Pulsar for multi-tenancy not speed.
• Picking Kafka for million topics — partition limits and broker memory bite hard.
• Skipping BookKeeper config — bookie disks fill up if Ensemble/Quorum/Ack are wrong.
• Comparing without geo-replication — Pulsar's geo is built-in; Kafka MirrorMaker 2 is bolted-on.
• Underestimating ecosystem — Kafka has 17 language clients and a massive Connect ecosystem; Pulsar is leaner.

FAQ

Is Pulsar still relevant after Kafka shed ZooKeeper? Yes — Pulsar's separation of compute/storage and its multi-tenancy story stand on their own.

Can I run both? Sure — Kafka for fan-out, Pulsar for per-tenant. Adds operator load.

What's Oxia? Pulsar's purpose-built coordination service replacing ZooKeeper, designed for the million-topic case.

Where does CallSphere stand? Kafka + NATS today; we'll re-evaluate Pulsar when per-tenant topic isolation becomes a contractual requirement. See /pricing.

Can I demo our event flow? /demo.

Sources

• Confluent: Kafka vs Pulsar — Performance, Features, Architecture
• Pulsar vs Kafka 2026: The Post-ZooKeeper Era
• Kafka vs Pulsar: Streaming Platform Comparison
• Apache Kafka vs. Apache Pulsar: Differences & Comparison

Pulsar 4.x vs Kafka 4.0 for AI Workloads: The Million-Topic Problem: production view

Pulsar 4.x vs Kafka 4.0 for AI Workloads: The Million-Topic Problem ultimately resolves into one engineering question: when do you use the OpenAI Realtime API versus an async pipeline? Realtime wins on latency for live calls. Async wins on cost, retries, and structured tool reliability for callbacks and SMS flows. Most teams need both, and the routing layer between them becomes the most load-bearing piece of the stack.

Serving stack tradeoffs

The big fork is managed (OpenAI Realtime, ElevenLabs Conversational AI) versus self-hosted on GPUs you operate. Managed wins on cold-start, model freshness, and zero-ops; self-hosted wins on unit economics past a certain conversation volume and on data residency for regulated verticals. CallSphere runs hybrid: Realtime for live calls, self-hosted Whisper + a hosted LLM for async, both routed through a Go gateway that enforces per-tenant rate limits.

Latency budgets are non-negotiable on voice. End-to-end target is sub-800ms ASR-to-first-token and sub-1.4s first-audio-out; anything beyond that and turn-taking feels stilted. GPU residency in the same region as your TURN servers matters more than choosing a slightly bigger model.

Observability is the unglamorous backbone — every conversation produces logs, traces, sentiment scoring, and cost attribution piped to a per-tenant dashboard. HIPAA aligned isolation keeps healthcare traffic separated from salon traffic at the storage layer, not just the API.

FAQ

Is this realistic for a small business, or is it enterprise-only? 57+ languages are supported out of the box, and the platform is HIPAA aligned, which removes most of the procurement friction in regulated verticals. For a topic like "Pulsar 4.x vs Kafka 4.0 for AI Workloads: The Million-Topic Problem", that means you're not starting from scratch — you're configuring an agent template that's already been hardened across thousands of conversations.

Which integrations have to be in place before launch? Day one is integration mapping (scheduler, CRM, messaging) and prompt tuning against your top 20 real call transcripts. Day two through five is shadow-mode running, where the agent transcribes and recommends but a human still answers, so you can compare side-by-side. Go-live is the moment your eval pass-rate clears your internal bar.

How do we measure whether it's actually working? The honest answer: it scales until your tool catalog gets stale. The agent is only as good as the integrations it can actually call, so the operational discipline is keeping schemas, webhooks, and fallback paths green. The platform handles the rest — observability, retries, multi-region routing — without your team owning the GPU layer.

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