Resource Kit

NATS vs Kafka

The Definitive Resource Kit

Whether you're evaluating alternatives to Apache Kafka, planning a migration, or tired of Kafka's operational complexity, this resource kit gives you the technical insights, benchmarks, and migration playbooks you need — backed by real data and expert perspectives.

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Quick Feature & Capability Summary

A side-by-side comparison of core capabilities between Apache Kafka and NATS with JetStream.

Capability	Apache Kafka	NATS (with JetStream)
Messaging Model	Topic + partitions	Subject + streams
Persistence & Replay	High durability via log	Durable streams with flexible retention
Latency	Moderate (batch-optimized)	Low-latency real-time first
Scaling	Partition-based horizontal scale	Lightweight clusters + JetStream scaling
Operational Complexity	Higher (brokers + Zookeeper/KRaft)	Lower (single binary, fewer moving parts)
Messaging Patterns	Pub-sub	Pub-sub, request-reply, queue, streaming
Tooling Footprint	JVM + ecosystem	Single Go binary
TCO	Baseline	~88% lower vs Kafka in equivalent workloads

According to independent TCO benchmarking by McKnight Consulting Group.

Core Resources: Videos, Reports & Case Studies

Deep-dive content to help you understand the differences and make informed decisions.

Video Comparisons

Comparing & Contrasting Kafka and NATS: Architecture

Explains the foundational differences in design.

Watch on YouTube

Comparing & Contrasting Kafka and NATS: Consumer Models

Shows how consumption semantics differ between the platforms.

Watch on YouTube

Reports & Whitepapers

Total Cost of Ownership Report – NATS vs Kafka

Independent analysis showing ~88% lower TCO with NATS and comparable or higher throughput under similar configurations.

Download PDF

Beyond Streaming Log Processing — Functional Comparison

Compares design philosophy, operational footprint, and messaging paradigms.

Download PDF

Articles & Case Studies

Why NATS — Explained for Modern Systems

Explains architectural design choices and why many teams adopt NATS for scalable, low-latency messaging.

GitLab Messaging Layer Design Document

A real-world engineering doc showing how one team designed a messaging layer.

Case Study: Dendrite Moves from Kafka to NATS

Concrete example of migration due to Kafka's complexity and operational overhead.

Benchmark Visualizations & TCO Analysis

Visual summary from the independent McKnight Consulting Group study.

Throughput Comparison

Normalized messages per second

Apache KafkaBaseline (100%)

NATS JetStream128–185% higher

Total Cost of Ownership

3-node configuration

Apache Kafka$$$$$

Infrastructure

Engineering Time

NATS~$ (88% lower)

Infrastructure

Engineering Time

Key Finding

NATS achieved up to 185% higher throughput and ~88% lower total cost of ownership in equivalent workloads.

Architectural & Messaging Differences

Understanding the core design philosophies and operational characteristics.

Apache Kafka

Log-based distributed streaming
Best for high-volume pipelines and analytics

NATS

Lightweight messaging with subject-based addressing
Designed for low latency, real-time communication

Deployment & Operations

Kafka

Typically involves multiple JVM processes, Zookeeper/KRaft, and fine-tuning for optimal performance.

NATS

Runs as a simple Go binary with optional JetStream for persistence — fewer moving parts and easier ops.

Messaging Models

Dimension	Kafka	NATS
Messaging Patterns	Pub-sub	Pub-sub + request-reply + queue groups
Consumer Scaling	Partition-based	Any number of consumers
Delivery Guarantees	Durable with offset replay	Durable (JetStream) with flexible policies

Migration Playbook: Kafka → NATS

Whether you want a full migration or hybrid approach, here's a practical playbook.

Define Your Messaging Needs

Throughput & latency targets
Durability & replay requirements
Delivery guarantees

Map Kafka Concepts to NATS

Apache KafkaNATS JetStream

Topic/Partition

Stream + subject patterns

Consumer Groups

JetStream durable consumers

Offset Replay

Stream retention + replay settings

Data Migration Plan

Establish subject naming conventions
Export existing event logs if needed
Configure JetStream retention & replication

Migration Strategies

Phased approach
Mirror traffic for testing
Cutover approach
Route new workloads to NATS while slowly deprecating Kafka
Hybrid
Use NATS as the operational messaging layer and Kafka for analytics

Decision Framework: Which to Choose?

Use this decision tree to guide architectural selection.

Choose NATS if:

You need real-time low latency communication
Operational simplicity is a priority
Your teams are frustrated with Kafka ops
Messaging patterns beyond streaming are important

Choose Kafka if:

You require extremely high volume analytic pipelines
You depend on a mature ecosystem of connectors & stream processing
Data lakes or warehouse integration is paramount

Common Questions & FAQs

Quick answers to frequently asked questions about NATS vs Kafka.

Can NATS replace Kafka completely?

For many microservices and low-latency streaming use cases, yes — especially with JetStream. For large analytic pipelines with heavy ecosystem tooling, Kafka's ecosystem remains valuable.

What about ordering guarantees?

Kafka enforces total order per partition; JetStream enforces order per stream with flexible controls.

How do costs compare in practice?

Independent benchmarking suggests significant infrastructure and personnel savings with NATS, with up to 88% lower TCO in equivalent workloads.

What are the main operational differences?

NATS runs as a single Go binary with fewer moving parts, while Kafka typically requires JVM processes, Zookeeper/KRaft, and more operational overhead.