NATS vs Kafka: Choosing Your Messaging System

Choosing a messaging system is one of the most consequential infrastructure decisions a team makes. It affects latency, reliability, operational burden, and the mental model every developer carries when writing distributed code. NATS and Apache Kafka are both mature, production-proven systems, but they embody fundamentally different philosophies. NATS optimizes for simplicity and speed. Kafka optimizes for durability and throughput at massive scale. Understanding these trade-offs in concrete terms is essential for making the right choice.

This article compares NATS and Kafka across the dimensions that matter most in practice: architecture, performance, operational complexity, ecosystem, and cost. We do not argue that one is universally better. Instead, we map each system's strengths to specific use cases and organizational contexts.

Architectural Philosophy

NATS was designed as a simple, high-performance messaging system. Its core is a lightweight server (a single Go binary, under 20MB) that routes messages between publishers and subscribers using subject-based addressing. There is no built-in persistence in core NATS; messages are delivered to active subscribers and discarded. JetStream, the persistence layer, was added later as an integrated component of the same server. A NATS cluster is a mesh of servers that share routing information, and clients connect to any server to access the full subject namespace.

Kafka was designed as a distributed commit log. Every message is written to a partitioned, replicated, append-only log on disk before any consumer reads it. Kafka's architecture assumes persistence as the default state: even real-time consumers read from the log. A Kafka cluster consists of brokers that manage partitions, and since 2022, KRaft mode replaces the previous ZooKeeper dependency for metadata management.

These philosophical differences ripple through every aspect of each system:

                    NATS                          Kafka
Persistence:        Optional (JetStream)          Always-on
Default latency:    Sub-millisecond               Single-digit milliseconds
Message ordering:   Per-subject                   Per-partition
Consumer model:     Push or Pull                  Pull (consumer polls)
Protocol:           Text-based, simple            Binary, complex
Server binary:      ~20MB single binary           JVM-based, ~100MB+

The implication for TypeScript applications using our node-nats client is significant. With NATS, a developer can start publishing and subscribing with five lines of code and no broker configuration. With Kafka, the developer needs to create topics, configure partitions, set up consumer groups, and manage offsets before the first message flows.

Performance Characteristics

Raw throughput numbers without context are misleading, but relative performance characteristics reveal genuine architectural differences.

NATS core (without JetStream) delivers messages with sub-millisecond latency in most configurations. The server performs no disk I/O on the publish path; messages go directly from the publisher's TCP connection to the subscriber's TCP connection through an in-memory routing table. A single NATS server can handle millions of messages per second for small payloads.

NATS JetStream adds persistence, which introduces disk I/O. Latency increases to low single-digit milliseconds, and throughput depends on storage configuration. With file-based storage and replication factor 3, expect sustained throughput of 100,000-500,000 messages per second per stream, depending on message size and hardware.

Kafka is optimized for high-throughput sequential writes. A single partition can sustain 100,000+ messages per second, and throughput scales linearly with partitions. End-to-end latency (publisher to consumer) is typically 2-10 milliseconds in well-configured clusters. Kafka achieves high throughput through batching: producers batch messages before sending, and consumers fetch batches from the log. This batching improves throughput but adds latency.

For a TypeScript application using node-nats, the practical difference is this: if you need the absolute lowest latency for real-time communication (chat, gaming, IoT telemetry), core NATS is hard to beat. If you need high-throughput log processing with strong durability, Kafka's architecture is purpose-built for it. JetStream occupies an interesting middle ground, offering persistence with latency closer to NATS core than to Kafka.

// NATS: Publish and subscribe are nearly instantaneous
import { connect, StringCodec } from "nats";
 
const nc = await connect({ servers: "nats://localhost:4222" });
const sc = StringCodec();
 
// This publish completes in microseconds (core NATS)
nc.publish("telemetry.temperature", sc.encode("72.5"));
 
// JetStream publish waits for persistence acknowledgment
const js = nc.jetstream();
const ack = await js.publish("events.readings", sc.encode("72.5"));
// ack confirms the message is persisted

Operational Complexity

This is where the differences become most tangible for engineering teams.

A NATS cluster is a single binary per node. Configuration is a single file. Upgrading is replacing the binary and performing a rolling restart. Lame Duck Mode signals clients to migrate to other servers, enabling zero-downtime upgrades. Monitoring uses built-in HTTP endpoints. There is no dependency on ZooKeeper, KRaft controllers, or Schema Registry servers.

A Kafka cluster requires more components. Even with KRaft mode eliminating ZooKeeper, Kafka brokers are JVM processes that need careful heap tuning, garbage collection configuration, and monitoring of JVM-specific metrics. Topic creation, partition rebalancing, and consumer group management are ongoing operational tasks. Schema Registry (Confluent or alternatives) adds another service to deploy and monitor.

The operational difference is not just about initial setup. It is about the ongoing burden:

Day-1 setup:

• NATS: Download binary, write a 10-line config, start three servers
• Kafka: Provision JVM instances, configure broker properties (100+ settings), set up KRaft controllers, configure network between brokers

Day-30 operations:

• NATS: Monitor subject throughput, stream sizes, and connection counts via HTTP API
• Kafka: Monitor partition lag, ISR shrink/expand, under-replicated partitions, consumer group offsets, JVM heap usage, GC pauses

Scaling events:

• NATS: Add a server to the cluster, it joins via gossip, clients discover it automatically
• Kafka: Add a broker, manually reassign partitions using partition reassignment tools, wait for data replication to complete

For teams without dedicated infrastructure engineers, NATS's operational simplicity is a decisive advantage. For organizations with mature platform teams, Kafka's operational complexity is manageable and the additional capabilities justify it.

Ecosystem and Tooling

Kafka has a larger ecosystem, built over its longer history and driven by Confluent's commercial investment. Kafka Connect provides hundreds of pre-built connectors for databases, data warehouses, and SaaS services. Kafka Streams and ksqlDB enable stream processing directly within the Kafka ecosystem. Schema Registry provides centralized schema management.

NATS has a growing but smaller ecosystem. Its strengths are in areas Kafka does not traditionally serve well: edge computing (NATS can run on resource-constrained devices), IoT (leaf node connections for constrained networks), and multi-cloud connectivity (NATS superclusters span cloud regions natively).

For TypeScript developers specifically, the comparison looks like this:

// NATS client: Single package, zero native dependencies
// npm install nats
import { connect } from "nats";
const nc = await connect();
 
// Kafka client: Multiple packages, native dependencies for some features
// npm install kafkajs (or @confluentinc/kafka-javascript for librdkafka bindings)
import { Kafka } from "kafkajs";
const kafka = new Kafka({ brokers: ["localhost:9092"] });
const producer = kafka.producer();
await producer.connect();

The node-nats client library is a pure TypeScript/JavaScript implementation with zero native dependencies. It works in Node.js, Deno, and browser environments without compilation steps. KafkaJS is also pure JavaScript but offers fewer platform options, and the Confluent client relies on native librdkafka bindings that complicate builds in containerized environments.

Use Case Mapping

Rather than declaring a winner, we find it more useful to map each system to the use cases where it excels:

Choose NATS when:

• You need real-time request/reply communication between microservices
• Sub-millisecond latency matters more than guaranteed durability
• Your team is small and cannot dedicate resources to messaging infrastructure
• You are building IoT or edge systems where lightweight clients are essential
• You want a single technology for both ephemeral messaging and persistent streaming (with JetStream)
• Multi-cloud or hybrid-cloud connectivity is a requirement

Choose Kafka when:

• You need a durable event log that multiple consumers can replay independently
• You are processing high-volume event streams (billions of events per day)
• You need Kafka Connect for integration with databases and data warehouses
• Stream processing with Kafka Streams or ksqlDB is part of your architecture
• Your organization has a platform team that can manage Kafka operations
• You need a mature ecosystem of third-party integrations

Consider both when:

• You have real-time communication needs (NATS) and batch processing needs (Kafka)
• Some services need sub-millisecond latency while others need durable event logs
• You are migrating from one system to the other and need a transition period

It is worth noting that NATS JetStream has narrowed the gap considerably. For many workloads that previously required Kafka, JetStream now provides sufficient durability and replay capability with dramatically lower operational overhead. The decision increasingly comes down to ecosystem requirements (Kafka Connect, Kafka Streams) versus operational simplicity (NATS).

Total Cost of Ownership

Cost comparisons must account for more than infrastructure spending. The true cost of a messaging system includes infrastructure, personnel time, and opportunity cost.

Infrastructure costs for NATS are typically lower. Three NATS servers with 4 vCPUs and 8GB RAM each can handle most workloads up to 100,000 messages per second with JetStream persistence. Equivalent Kafka throughput often requires larger instances (more RAM for page cache, more disk I/O capacity) and additional instances for KRaft controllers.

Personnel costs are where NATS shines for smaller teams. A team of 5-10 developers can operate NATS without dedicated infrastructure expertise. Kafka typically requires at least one engineer with specialized knowledge for partition management, consumer group tuning, and capacity planning. For a team evaluating total cost over three years, the personnel savings from choosing NATS often exceed the infrastructure savings.

Opportunity cost is harder to quantify but equally real. Every hour spent debugging partition rebalancing or tuning JVM garbage collection is an hour not spent building product features. For startups and growing fintech companies, this trade-off often favors NATS.

For larger organizations processing billions of events daily, Kafka's ecosystem advantages (Connect, Streams, mature monitoring) reduce the total cost by eliminating custom integration work. The break-even point varies, but organizations processing fewer than 50,000 messages per second with fewer than 20 engineering staff typically find NATS more cost-effective.

Migration Considerations

If you are considering migrating from Kafka to NATS (or vice versa), plan for a coexistence period. Both systems can run simultaneously, with bridge services forwarding messages between them. The node-nats client library makes it straightforward to build such bridges:

import { connect as natsConnect, JSONCodec } from "nats";
import { Kafka } from "kafkajs";
 
const jc = JSONCodec();
 
async function bridgeKafkaToNats() {
  const nc = await natsConnect({ servers: "nats://localhost:4222" });
  const kafka = new Kafka({ brokers: ["localhost:9092"] });
  const consumer = kafka.consumer({ groupId: "nats-bridge" });
 
  await consumer.connect();
  await consumer.subscribe({ topic: "orders", fromBeginning: false });
 
  await consumer.run({
    eachMessage: async ({ topic, message }) => {
      if (message.value) {
        const data = JSON.parse(message.value.toString());
        nc.publish(`bridge.kafka.${topic}`, jc.encode(data));
      }
    },
  });
}

This approach lets you migrate consumers one at a time, validating each migration before moving the next. Once all consumers have migrated, you can decommission the bridge and the original system.

Conclusion

NATS and Kafka are both excellent messaging systems that serve different needs. NATS excels in simplicity, low latency, and operational ease. Kafka excels in durable event logging, high-throughput batch processing, and ecosystem breadth. With JetStream, NATS has expanded into territory that previously required Kafka, making it a compelling single-technology solution for teams that need both real-time messaging and persistent streaming. The right choice depends on your specific workload characteristics, team size, operational capacity, and ecosystem requirements. For many TypeScript-based microservice architectures, particularly in fintech, NATS with JetStream provides the best balance of capability, performance, and operational simplicity.