NATS JetStream vs Apache Kafka vs Apache Pulsar: Latency SLAs, Throughput & Multi-Tenancy

Executive Summary & Decision Matrix

All three systems are durable, replayable logs. The differences are in the tax each one charges on every message: JetStream buys low latency with a single Raft-group-per-stream design (low coordination overhead, limited per-stream scale ceiling), Kafka buys throughput and ecosystem gravity with a partition-per-consumer-thread model (high per-partition cost, no native multi-tenancy), and Pulsar buys horizontal elasticity and tenancy isolation with a BookKeeper-ledger / broker-compute-split architecture (extra network hop, extra p99 tail).

Benchmark environment used throughout this article (all numbers measured on this rig unless a hosted-SLA figure is cited as vendor-published):

Decision Matrix (8+ quantified metrics)

Decision matrix in one sentence: Run JetStream if your service mesh already speaks NATS and you need sub-10 ms p99 durable delivery at moderate fan-out; run Kafka if throughput per dollar, the connector ecosystem, and exactly-once pipelines dominate and your tenancy count is in the dozens, not thousands; run Pulsar if you need hard multi-tenant isolation or elastic topic counts above ~20k and can pay the extra hop in tail latency.

Ratings: JetStream 4.8/5 for latency-sensitive infrastructure workloads; Kafka 4.5/5 for data-platform backbones; Pulsar earns 4.3/5 (not carried in frontmatter ratings which follow the primary comparison slots) — the difference is operational surface area, not capability.

Architectural Deep-Dive

Three storage topologies, three latency profiles

flowchart TB
    subgraph JetStream["NATS JetStream (co-located log + Raft)"]
        P1[Publisher] --> S1[NATS Server A<br/>stream + Raft leader<br/>local file store]
        S1 <-->|Raft append| S2[Server B<br/>Raft follower]
        S1 <-->|Raft append| S3[Server C<br/>Raft follower]
        S1 --> C1[Consumer push/pull]
    end
    subgraph Kafka["Apache Kafka (partition-binned broker storage)"]
        P2[Producer] -->|partition hash| K1[Broker 1<br/>leader of P0..P2]
        K1 <-->|ISR replicate| K2[Broker 2]
        K1 <-->|ISR replicate| K3[Broker 3]
        K1 --> C2[Consumer group<br/>partition-owned]
    end
    subgraph Pulsar["Apache Pulsar (stateless broker + BookKeeper)"]
        P3[Producer] --> B1[Broker 1<br/>stateless, owns topic cursor]
        B1 -->|ledger ensembles| BK1[Bookie 1]
        B1 --> BK2[Bookie 2]
        B1 --> BK3[Bookie 3]
        BK1 --> B1 --> C3[Consumer]
    end

JetStream places the log and the Raft quorum inside the same process as the client-facing server. A publish is a Raft append; with 3 replicas and SyncAlways-equivalent file sync, the ack returns after one Raft round (client → leader, leader → 2 followers, fsync). That's 1 network hop of replication, no metadata service in the data path. The cost: every stream carries its own Raft group — 5,000 streams means 5,000 election timers, snapshot schedules, and per-group WAL buffers. Heartbeat traffic scales O(streams × peers); on our 3-node rig, 10k streams produced a steady 2.1 Mbps of pure Raft heartbeat and measurable CPU tax even at zero message load.

Kafka bins partitions onto brokers; the leader serves both produce and fetch, replication is a pipelined pull (fetch requests from followers). Zero-copy sendfile from page cache to TLS-less sockets is why Kafka wins the throughput column — data path bypasses userspace entirely. The costs are (a) partition count is your parallelism and your metadata burden — KRaft metadata snapshots grow linearly, and a controlled shutdown of one broker with 4,000 leader partitions takes 25–40 s of leadership transfer even with controlled.shutdown.enable=true; (b) consumer groups require rebalance protocols that, even in cooperative-sticky mode, stop consumption on the affected partitions for seconds.

Pulsar splits compute (brokers, stateless, cache topic cursors) from storage (BookKeeper bookies, ledger-fragment appends). A publish crosses broker → bookie (journal write) → ack, so it's structurally one extra network hop versus Kafka. What you buy: topic ownership migrates in milliseconds (brokers hold no data, so adding brokers rebalances nothing on disk), ledger ensembles stripe across bookies for write scalability, and tenants/namespaces are enforced at the protocol level with per-namespace bundle routing and optional bookkeeper ensemblePlacementPolicy pinning to dedicated hardware. The trap: hot namespaces pin to hot bookies unless you run EnsemblePlacementPolicy=ZoneAware with enough zones.

Disk, kernel, and network mechanics

• fsync math dominates latency. On gp3 at 16k IOPS provisioned, a synced 4 KiB write lands at 1.9–2.4 ms p99 (measured with fio --rw=write --iodepth=1 --fsync=1, giving ~430 fsyncs/s per volume — this, not CPU, is the per-node ceiling for all three systems under acks=all). Kafka amortizes fsync across log.flush.interval.messages; JetStream's file store batches via a 2 ms sync window by default (SyncInterval), which is where its 6.8 ms p99 comes from — set it to 0 and p99 rises to ~9 ms with true per-message durability.
• Page cache asymmetry. Kafka reads are cache-or-nothing: a consumer lagging beyond memory goes straight to disk with sendfile, and on gp3 that's a p99 read stall of 8–15 ms. JetStream and Pulsar do userspace reads, which costs a memcpy but lets them prefetch — JetStream's file store uses its own page cache with 10 GiB default and evicts LRU.
• Network topology. All three need replication traffic separate from client traffic at scale. At 1 GB/s produce, Kafka ISR replication adds ~1 GB/s east-west; on a 12.5 Gbps NIC that's 64% of the wire consumed by replication — rack-aware placement and 25 Gbps NICs are non-optional above ~500 MB/s per broker. Pulsar's extra hop means every message crosses the wire twice client-side-adjacent (broker→bookie), so cross-AZ traffic is ~1.4× Kafka's for the same durability posture.

Real-World Benchmarks & Hard Data

fio baseline (what the disks can actually do)

fio --name=wal --rw=write --bs=4k --iodepth=1 --fsync=1 --runtime=300 \
    --filename=/var/lib/streamdata/fio
# Results (gp3, 16k IOPS):
#   fsync/s sustained:        431
#   write latency p50/p99:    2.1 ms / 2.4 ms
#   sequential 1MiB write:    985 MB/s (peak), 790 MB/s sustained 10 min

431 synced writes/s per volume is the hard physics. A 3-node cluster with 3 volumes each therefore tops out near ~1,290 synced appends/s per stream-quorum path unless writes are batched — which is exactly why all three systems batch, and why advertised "millions of msg/s" figures assume acks=all with micro-batching, not per-message fsync.

Produce/consume throughput (1 KiB JSON, fsync/durability-max, 3-node cluster)

JetStream's ceiling at 64 streams is Raft group heartbeats + per-stream lock contention in the file store; it plateaus rather than degrades. Kafka's p99 at max load shows the classic 10–20 ms "linger batch flush" sawtooth. Pulsar's CPU overhead is the broker↔bookie serialization (protobuf framing + ledger checksum compute: CRC32C on every entry measured at 0.9 core per 100k msg/s).

Hosted-SLA comparison (vendor-published, 2026 pricing, us-east-1)

TCO at three tiers (1 KiB messages, 7-day retention)

Startup tier — $100/mo budget, ~5 GB/day, 5 consumers:

Scale-up tier — $2,500/mo, ~300 GB/day, 50 consumers, 200 topics:

Enterprise tier — $25,000/mo, 5 TB/day, 500+ tenants:

Failure Modes & Hidden Traps

1. JetStream Raft heartbeat collapse at high stream counts. Past ~8–10k streams on a 3-node group, Raft heartbeat and flowcontrol frames saturate the server's inbound queue before the data path does. Symptom: publish latency inflation with zero message throughput increase; the nats monitoring endpoint shows raft.group counts and pending bytes. Debug with -m 8222 /varz and watch in_msgs on the $JS system subjects. Mitigation: cap streams per cluster, use stream sharding across leaf nodes. This is the single reason the throughput column caps at 310k msg/s.

2. Kafka's partition-count ratchet and the unexpected rebalance bill. Partitions are trivially easy to create and never cheap to remove. Teams that create a topic per feature (with 12 partitions for "parallelism") hit KRaft metadata pressure at ~30–50k partitions; broker restarts then take 2–5 minutes of leadership transfer, and consumer groups doing cooperative rebalances during deployments stop affected partitions for 2–6 s each — at 50k msg/s per partition that's silently dropped downstream SLAs. The hidden bill on managed platforms: Confluent and MSK bill on partition-hours beyond included counts — a 10k-partition cluster adds $1,400–2,600/mo of pure partition tax. Pin num.partitions policy in CI and reject partition counts above 6× consumer parallelism.

3. Pulsar's split-brain of attention: bookie disk saturation is invisible on broker dashboards. Because brokers are stateless, broker CPU/network look healthy while a single bookie's journal disk hits its fsync ceiling (see the 431 fsync/s figure — a hot ledger ensemble pinned by an isolation policy to 3 bookies inherits one journal disk's ceiling for the whole namespace). Producers see timeouts (ScopedBookKeeperTimeoutException), autoscalers add brokers, nothing improves. Debug path: BookKeeper bookie_server.conf journalMaxGroupWaitMSec, prometheus bookie journal metrics (BOOKIE_JOURNAL_FSNC_LATENCY), and ledger-ensemble placement audit. Mitigation: separate journal and ledger devices (the docs say optional; at scale it is not).

4. Cross-AZ transfer on all three is the line item nobody models. At 1 GB/s produce with synchronous replication, expect 2–3× the message volume in east-west bytes (replication + consumer fetch). On AWS that's $10–14k/mo at enterprise tier — enough to flip a managed-vs-self-hosted decision and enough to justify rack/zone-aware replica placement (Kafka broker.rack, Pulsar ensemble placement, JetStream placement via Placement tags) as a financial control, not just a resilience one.

5. Exactly-once gap. JetStream has publish dedup but no consume-transform-produce transactions; if your pipeline requires end-to-end EOS (payments, ledgers), Kafka's transactional API is the only native answer — otherwise you're writing idempotency keys downstream, which is fine but must be a decision, not a discovery at audit time.

Final Buying Decision

The rankings hold across every workload class we tested:

• Latency-critical, moderate scale (≤300k msg/s, ≤1k tenants): NATS JetStream — 4.8/5. Half the p99 of Kafka, one binary to operate, and the NATS core+JetStream pairing replaces both your RPC bus and your log. Best value per dollar at every tier below enterprise. Get Synadia Cloud free tier → (self-managed NATS is free — start there and upgrade only when you need multi-region support SLAs).
• Data-platform backbone, EOS pipelines, ecosystem gravity: Apache Kafka — 4.5/5. 4× JetStream's throughput ceiling, the only native exactly-once story, and Connect/ksqlDB/Flink integration nobody matches. Pay the partition tax consciously. Try Confluent Cloud with $400 of credits → or MSK if you're AWS-committed.
• 500+ tenants, elastic topic counts, hard isolation: Apache Pulsar — 4.3/5. Only pick with a team that can operate BookKeeper; the multi-tenancy is real and the elasticity is real, and both are worth one network hop. Spin up StreamNative Cloud →.

If you must pick one number to guide you: your steady-state p99 budget. Under 10 ms, JetStream is the only one of the three that meets it on commodity disks with durability on. Between 10–20 ms with >1 GB/s throughput needs, Kafka. Above 20k topics or dozens-to-thousands of tenants, Pulsar. Everything else is marketing.

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