Kafka vs NATS Comparison

Feature	NATS	Kafka
Message Consumption Model	Pub/Sub with pull-based and push-based architecture	Pub/Sub with pull-based architecture (consumers pull messages)
Delivery Guarantee	At most once (JetStream offers at least and exactly once guarantees)	Exactly once
Scalability	Horizontal scaling by adding/removing servers	Horizontal scaling by adding/removing brokers
Message Ordering	Source-ordered per publisher (no cross-publisher guarantees)	Guarantees within a partition
Use Cases	Services, IoT, edge telemetry, command and control, legacy system replacement	High-throughput data processing, log aggregation, stream processing, event sourcing
Persistence	JetStream supports memory and file persistence	Built-in via log storage
Languages Supported	Official clients in Go, Rust, JavaScript, Python, Java, C#, and more	Clients available in Java, Scala, Go, Python, C/C++, and more
Security	TLS, NKEYS, username/password, token authentication	SSL, SASL, external authorization services
Monitoring	Integration with Prometheus, Grafana, and nats-top	Built-in support for JMX, Prometheus, Grafana
Built-in Patterns	Pub/Sub, request-reply, load-balanced queue group patterns	High-throughput stream processing and event sourcing

NATS is a better fit if:

You need a lightweight, easy-to-deploy messaging system with a focus on real-time communication and IoT applications.
You prioritize performance and low latency over reliability.
Real-time personalized recommendations can handle occasional lost events.

You require a robust, scalable, and fault-tolerant messaging system for high-throughput data processing and event streaming.
Reliability is crucial, where no message loss is tolerated, but duplicate messages can be handled.
Inventory management or stock trading systems require exactly-once processing to maintain accuracy.