Kafka vs NATS 2026: Throughput vs Latency
Kafka is a distributed event streaming platform optimized for high-throughput, persistent message storage with strong ordering guarantees, while NATS is a lightweight, low-latency messaging system designed for real-time cloud-native applications with minimal overhead. Kafka stores messages durably on disk; NATS keeps them primarily in memory by default.
Apache Kafka
Distributed event streaming platform for high-throughput, persistent message storage and processing
Data engineers, event-driven architectures, log aggregation, stream processing, audit trails, analytics pipelines needing data durability
NATS
Lightweight, open-source messaging system optimized for low-latency cloud-native and microservices communication
Microservices, IoT platforms, real-time metrics, cloud-native systems, command/control messaging, developers prioritizing simplicity
Quick Answer
AI SummaryKafka is a distributed event streaming platform optimized for high-throughput, persistent message storage with strong ordering guarantees, while NATS is a lightweight, low-latency messaging system designed for real-time cloud-native applications with minimal overhead. Kafka stores messages durably on disk; NATS keeps them primarily in memory by default.
Our Verdict
AI-assistedChoose Kafka if you need persistent, ordered event streams with high throughput, complex consumer group management, and long-term data replay capabilities for data engineering and analytics pipelines. Choose NATS if you prioritize operational simplicity, ultra-low latency, minimal resource consumption, and real-time request-response patterns in cloud-native or microservices architectures.
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Choose Apache Kafka if
Data engineers, event-driven architectures, log aggregation, stream processing, audit trails, analytics pipelines needing data durability
Choose NATS if
Best pickMicroservices, IoT platforms, real-time metrics, cloud-native systems, command/control messaging, developers prioritizing simplicity
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Key Differences at a Glance
- Message Persistence:✓ Apache Kafka wins(Disk-based (default, configurable retention) vs In-memory (optional persistence with JetStream))
- Throughput Capacity:✓ Apache Kafka wins(1M+ messages/sec per broker vs 250K-500K messages/sec per server)
- Latency (p99):✓ NATS wins(1-5ms typical vs 10-50ms typical)
Key Facts & Figures
49 numeric metrics compared
| Metric | Apache Kafka | NATS | Ratio |
|---|---|---|---|
| P99 Latency(milliseconds) | 5-10ms | 0.1-0.5ms | |
| Memory Usage (Single Instance)(MB) | 2048+ | 10-50 | |
| Installation Size(GB) | ~20 | ~5 | |
| GitHub Stars (2026)(stars) | ~40K | ~8K | |
| Replication Factor (Durability)(copies) | 2-3+ (multi-node) | Optional clustering | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 2-3 | — | — |
| Available Built-in Connectors(count) | 200+ | — | — |
| Typical Throughput (single node)(events/sec) | 1,000,000+ | — | — |
| Minimum Operational Complexity(components to manage) | 3-5 (brokers, ZK/KRaft, optional monitoring) | — | — |
| Throughput per Broker(messages/sec) | 1,000,000 | — | — |
| P99 End-to-End Latency(milliseconds) | 50-100ms | — | — |
| Minimum Memory Requirement per Broker(GB) | 4GB | — | — |
| Production Deployments Worldwide(estimated count) | 500,000+ | — | — |
| Available Connectors(count) | 10,000+ | — | — |
| Enterprise Support Vendors(count) | 15+ vendors | — | — |
| First Release Year(year) | 2011 | — | — |
| Throughput (msgs/sec on standard 3-node cluster)(msgs/second) | 1,000,000+ | — | — |
| Message Latency (P99 end-to-end)(milliseconds) | 100-200ms | — | — |
| Replication Factor (fault tolerance)(copies) | 3 (configurable 1-N) | — | — |
| Time to Production Cluster(hours) | 8-16 (complex coordination) | — | — |
| Minimum JVM Heap (3-node cluster)(GB) | 12-18GB recommended | — | — |
| Open Source Community Size(GitHub stars (2026)) | 27,000+ stars | — | — |
| Maximum Message Retention(days) | Configurable (365+ days possible) | — | — |
| Time to Production(minutes) | 14-28 days | — | — |
| Minimum Infrastructure Cost (Monthly)(USD) | $500-2,000 (3-broker cluster + ops) | — | — |
| Operational Complexity Rating(1-10 scale) | 8 (cluster management, monitoring, tuning) | — | — |
| Setup Complexity (1-10)(complexity score) | 9 (ZooKeeper, brokers, topics, replication) | — | — |
| End-to-End Latency (p99)(milliseconds) | 10-20ms | — | — |
| Throughput per Partition(messages/second) | ~1M | — | — |
| GitHub Stars (Community)(stars) | ~28K | — | — |
| Enterprise Market Share(percent) | ~65% | — | — |
| Managed Cloud Offerings(vendors) | 8+ major (Confluent Cloud, Aiven, Redpanda, AWS MSK) | — | — |
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (cluster management required) | — | — |
| Peak Message Throughput(msgs/sec) | 1M-3M msgs/sec | 100K-500K | |
| Minimum Deployment Nodes(nodes) | 3 nodes (3 ZK brokers minimum) | — | — |
| Available Connectors (Ecosystem)(count) | 1000+ connectors | — | — |
| Setup Time to Production(minutes) | 2-4 weeks | — | — |
| Cost per Million Messages (at scale)(USD) | $0.10-0.30 (self-hosted) | — | — |
| Typical Monthly Cost (1M msgs/day)(USD) | $60-150 (self-hosted only) | — | — |
| Maximum Throughput(messages/second) | 1,000,000+ | — | — |
| Average Message Latency(milliseconds) | 10-100ms | — | — |
| Minimum Cluster Nodes (HA)(nodes) | 3 nodes recommended | — | — |
| Consumer Group Scaling(consumers per group) | Up to partition count (unlimited partitions) | — | — |
| Throughput (messages/sec per node)(msg/sec) | 1,000,000+ | 250,000-500,000 | |
| Latency (p99)(ms) | 10-50 | 1-5 | |
| Base Memory Footprint(MB) | 500-2000 | 10-50 | |
| Deployment Complexity (nodes required)(minimum nodes) | 3+ (with ZooKeeper or KRaft quorum) | 1 (standalone), 3+ (HA cluster) | |
| Time to First Message (cold start)(ms) | 50-200 | 10-30 | |
| Ecosystem Integrations(approximate count) | 100+ | 30-40 |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Disk-based (default, configurable retention)(winner)Message PersistenceIn-memory (optional persistence with JetStream)
- 1M+ messages/sec per broker(winner)Throughput Capacity250K-500K messages/sec per server
- 10-50ms typicalLatency (p99)1-5ms typical(winner)
- 500MB-2GB per brokerMemory Footprint (base)10-50MB per server(winner)
- Advanced offset management, rebalancing(winner)Consumer Group SemanticsBasic subscription-based (improved in JetStream)
- High (ZooKeeper/KRaft, tuning required)Operational ComplexityLow (standalone or clustered easily)(winner)
- Per-partition strict ordering(winner)Message Ordering GuaranteePer-subject ordering (with limitations in clustered mode)
- Message Persistence
Apache Kafka
Disk-based (default, configurable retention)(winner)
NATS
In-memory (optional persistence with JetStream)
- Throughput Capacity
Apache Kafka
1M+ messages/sec per broker(winner)
NATS
250K-500K messages/sec per server
- Latency (p99)
Apache Kafka
10-50ms typical
NATS
1-5ms typical(winner)
- Memory Footprint (base)
Apache Kafka
500MB-2GB per broker
NATS
10-50MB per server(winner)
- Consumer Group Semantics
Apache Kafka
Advanced offset management, rebalancing(winner)
NATS
Basic subscription-based (improved in JetStream)
- Operational Complexity
Apache Kafka
High (ZooKeeper/KRaft, tuning required)
NATS
Low (standalone or clustered easily)(winner)
- Message Ordering Guarantee
Apache Kafka
Per-partition strict ordering(winner)
NATS
Per-subject ordering (with limitations in clustered mode)
Full Comparison
| Attribute | Apache Kafka | NATS |
|---|---|---|
| P99 Latency(milliseconds) | 5-10ms | 0.1-0.5ms(winner) |
| Typical Throughput (single node)(events/sec) | 1,000,000+ | — |
| Throughput per Broker(messages/sec) | 1,000,000 | — |
| P99 End-to-End Latency(milliseconds) | 50-100ms | — |
| Throughput (msgs/sec on standard 3-node cluster)(msgs/second) | 1,000,000+ | — |
Show 9 more attributesMessage Latency (P99 end-to-end)(milliseconds) 100-200ms — End-to-End Latency (p99)(milliseconds) 10-20ms — Throughput per Partition(messages/second) ~1M — Peak Message Throughput(msgs/sec) 1M-3M msgs/sec 100K-500K Maximum Throughput(messages/second) 1,000,000+ — Average Message Latency(milliseconds) 10-100ms — Throughput (messages/sec per node)(msg/sec) 1,000,000+ 250,000-500,000 Latency (p99)(ms) 10-50 1-5 Time to First Message (cold start)(ms) 50-200 10-30 | ||
| Memory Usage (Single Instance)(MB) | 2048+ | 10-50(winner) |
| Base Memory Footprint(MB) | 500-2000 | 10-50(winner) |
| Message Persistence | Built-in, configurable retention (time/size) | Optional via JetStream add-on |
| Replication Factor (Durability)(copies) | 2-3+ (multi-node) | Optional clustering |
| Exactly-Once Delivery | Supported (transactional API) | Not natively supported |
| Delivery Semantics | At-least-once (default) | — |
| Replication Factor (fault tolerance)(copies) | 3 (configurable 1-N) | — |
| Installation Size(GB) | ~20 | ~5(winner) |
| GitHub Stars (2026)(stars) | ~40K(winner) | ~8K |
| GitHub Stars (Community)(stars) | ~28K | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 2-3 | — |
| Available Built-in Connectors(count) | 200+ | — |
| Available Connectors(count) | 10,000+ | — |
| Open Source Community Size(GitHub stars (2026)) | 27,000+ stars | — |
| Managed Cloud Offerings(vendors) | 8+ major (Confluent Cloud, Aiven, Redpanda, AWS MSK) | — |
| Available Connectors (Ecosystem)(count) | 1000+ connectors | — |
Show 1 more attributeEcosystem Integrations(approximate count) 100+ 30-40 | ||
| Watermark Support | No (event time not native) | — |
| Maximum Message Retention(days) | Configurable (365+ days possible) | — |
| Consumer Group Support | Yes (multiple subscribers per topic) | — |
| Multi-Tenancy Support | External solution required | — |
| Native Geo-Replication | No (requires MirrorMaker) | — |
Show 6 more attributesMessage Retention Period(days (maximum)) Unlimited (configurable) — Message Replay Capability Full consumer offset control (replay from any timestamp) — Multi-Consumer Support (native) Yes (consumer groups with independent offsets) — Supported Routing Patterns Topic-based only (8 types) — Message Replay Support Full replay from any offset/timestamp — Consumer Offset Management(text) Advanced broker-side offset tracking with rebalancing Basic subscriptions (improved with JetStream consumer API) | ||
| State Size Capacity(GB) | Not applicable | — |
| Consumer Group Scaling(consumers per group) | Up to partition count (unlimited partitions) | — |
| Minimum Operational Complexity(components to manage) | 3-5 (brokers, ZK/KRaft, optional monitoring) | — |
| Minimum Memory Requirement per Broker(GB) | 4GB | — |
| External Dependencies | ZooKeeper required | — |
| Time to Production Cluster(hours) | 8-16 (complex coordination) | — |
| Minimum JVM Heap (3-node cluster)(GB) | 12-18GB recommended | — |
Show 3 more attributesOperational Complexity Rating(1-10 scale) 8 (cluster management, monitoring, tuning) — Minimum Deployment Nodes(nodes) 3 nodes (3 ZK brokers minimum) — Deployment Complexity (nodes required)(minimum nodes) 3+ (with ZooKeeper or KRaft quorum) 1 (standalone), 3+ (HA cluster) | ||
| Production Deployments Worldwide(estimated count) | 500,000+ | — |
| First Release Year(year) | 2011 | — |
| Enterprise Support Vendors(count) | 15+ vendors | — |
| Message Retention | Indefinite (configurable by time/size) | — |
| Message Retention (default)(text) | 7 days (configurable to years) | In-memory until eviction (optional JetStream) |
| Time to Production(minutes) | 14-28 days | — |
| Setup Time to Production(minutes) | 2-4 weeks | — |
| Minimum Cluster Nodes (HA)(nodes) | 3 nodes recommended | — |
| Minimum Infrastructure Cost (Monthly)(USD) | $500-2,000 (3-broker cluster + ops) | — |
| Cost per Million Messages (at scale)(USD) | $0.10-0.30 (self-hosted) | — |
| Typical Monthly Cost (1M msgs/day)(USD) | $60-150 (self-hosted only) | — |
| Setup Complexity (1-10)(complexity score) | 9 (ZooKeeper, brokers, topics, replication) | — |
| Tiered Storage Support | Plugin Required | — |
| Enterprise Market Share(percent) | ~65% | — |
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (cluster management required) | — |
| Enterprise Adoption Rate(businesses) | 78-82% | — |
| Storage Architecture Type | Broker-attached | — |
| Default Storage Type | Disk-based persistent log | — |
Show 9 more attributes
Show 1 more attribute
Show 6 more attributes
Show 3 more attributes
Pros & Cons
10 pros·6 cons across both
Apache Kafka
Pros
- 1M+ messages/sec throughput per broker enables massive data volumes
- Disk-based persistence allows unlimited message retention and historical replay
- Strict per-partition ordering guarantees critical for stateful processing
- Advanced consumer groups with offset management and rebalancing for scalable consumption
- Mature ecosystem with 100+ integrations, Kafka Streams, and ksqlDB
Cons
- Requires ZooKeeper or KRaft cluster adding 3+ extra nodes and significant operational overhead
- Base memory footprint of 500MB-2GB per broker increases infrastructure costs
- Complexity in configuration and tuning (replication factors, retention policies, partition count) requires expertise
NATS
Pros
- Ultra-low latency (p99 1-5ms) ideal for real-time and time-sensitive applications
- Minimal memory footprint (10-50MB base) reduces infrastructure and cloud costs
- Simple deployment as single binary with no external dependencies (no ZooKeeper needed)
- Native request-reply pattern and subject-based routing for microservices patterns
- JetStream addon provides persistence with same simplicity as core NATS
Cons
- Lower throughput (250K-500K msg/sec) insufficient for mega-scale event streaming
- Message ordering guarantees weaker in clustered mode compared to Kafka's strict per-partition ordering
- Smaller ecosystem with fewer production integrations and limited stream processing tooling
Frequently Asked Questions
5 questions
Use NATS for typical microservices communication—its low latency (1-5ms p99), minimal footprint, and simple deployment make it ideal for service-to-service messaging. Use Kafka if you need to build an event-driven architecture with persistent event history for audit/replay or if you require complex stream processing with consumer groups across hundreds of services.
Resources & Learn More
Curated sources to dive deeper
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