Choose Kafka when the job is a durable event log: topics partitioned across brokers, events kept for as long as you configure, replayed by many consumers, with guarantees such as exactly-once processing. Choose NATS when you want lightweight messaging by subject, request-reply and queue groups from a single small binary, with JetStream adding persistence and a design that stretches from cloud to edge. Both are open source.
I read the NATS home page and its What is NATS docs, then Apache Kafka's home page and introduction. The two overlap now that NATS has JetStream, but their documentation still starts from different places: NATS from connecting services, Kafka from storing streams of events.
NATS and Kafka in a table
| NATS | Apache Kafka | |
|---|---|---|
| What it calls itself | An open source messaging system | An open-source distributed event streaming platform |
| Core model | Messages by subject: publish-subscribe, request-reply, queue groups | Events stored in partitioned topics |
| Persistence | JetStream adds streaming with persistence | Durable storage with retention set per topic |
| Footprint | Single binary, minimal resources | Brokers forming a cluster |
| Reach | Leaf nodes and superclusters for cloud, regions and edge | Kafka Connect for data in and out |
| Free option (as of October 2026) | Free and open source | Free and open source |
How NATS works
The NATS docs explain that applications connect to a NATS server and exchange messages by subject, without knowing each other's network addresses. That one idea gives you publish-subscribe, request-reply and queue groups. JetStream adds streaming with persistence, all from a single binary with low resource use and sub-millisecond latency.
NATS leans into edge and AI use cases. Its docs list agent-to-agent communication, IoT telemetry, edge-to-cloud links over intermittent networks and command and control. Leaf nodes and superclusters let you add clouds, regions or edge sites without downtime, and there are 45+ client libraries.
How Kafka works
Kafka's introduction describes events organised and durably stored in topics, which are partitioned across brokers. Retention is configurable per topic, and because performance stays effectively constant as data grows, keeping data for a long time is fine. Producers never wait for consumers, and Kafka offers guarantees such as exactly-once processing.
Around the core sit Kafka Connect, for moving data in and out of other systems, and Kafka Streams, for processing streams in your applications, so you won't need a separate tool for either job. Kafka's home page says it's used by thousands of companies.
NATS also builds in multi-tenancy. Its docs say accounts isolate messaging domains within one server or cluster, which supports secure multi-tenant deployments, team isolation and building SaaS platforms. Applications don't need to know where other services live, because NATS handles service discovery, load balancing and fault tolerance. The project reports 400M+ downloads and 18K+ GitHub stars, so it's far from niche.
The simplest way to tell them apart
Ask whether your problem is talking or remembering. If services need to talk to each other quickly, ask questions and get answers, across clouds and edge devices, NATS was built for that and can remember with JetStream. If the heart of the system is a long-lived record of events that several consumers read and replay, that's what Kafka was built around.
Five checks before you commit
- Need request-reply between services? NATS has it as a core pattern.
- Need months of event history to replay? Kafka's per-topic retention is designed for it.
- Running on small devices or patchy networks? NATS lists edge and constrained bandwidth as core use cases.
- Feeding data warehouses and other systems? Kafka Connect is built for that.
- Unsure? Prototype one real flow in each and measure what matters to you.
For the business side of moving data around, see these data integration challenges and why it pays to monitor data effectively in your business.
My advice on NATS or Kafka
If you're building microservices, AI agents or edge systems that need fast messaging and simple operations, I'd start with NATS and add JetStream where you need persistence. If your platform revolves around a durable, replayable event log feeding analytics and many downstream teams, Kafka is the established choice. You can also run both, NATS at the edges and Kafka at the core.
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Related: the iot page.
Frequently asked questions
Is NATS a replacement for Kafka?
Not always. NATS focuses on messaging by subject, with JetStream for persistence, while Kafka centres on durable, partitioned event logs.
Does NATS store messages?
Yes, through JetStream, which the NATS site describes as streaming with persistence.
How long does Kafka keep events?
Kafka's introduction says retention is configurable per topic, and storing data for a long time is fine.
Are NATS and Kafka free?
Yes. Both are open source as of October 2026.
What languages have NATS clients?
NATS lists official clients in Go, Rust, JavaScript, TypeScript, Python, Java, C#, C, Ruby and Elixir, plus community clients.
