Redpanda vs NATS vs Apache Kafka 2026

TL;DR: Redpanda is the Kafka-compatible streaming platform written in C++ — same API, no JVM, no ZooKeeper, lower latency and simpler operations. NATS is the lightweight cloud-native messaging system — pub/sub, request-reply, JetStream for persistence, and built for polyglot microservices with minimal overhead. Apache Kafka is the original distributed event streaming platform — massive ecosystem, exactly-once semantics, Kafka Streams, Connect, and battle-tested at every major tech company. In 2026: Redpanda for Kafka compatibility without the operational burden, NATS for lightweight microservice communication, Kafka for the full ecosystem and enterprise-scale streaming.

Key Takeaways

Redpanda: Kafka API compatible, C++ (no JVM). Single binary, no ZooKeeper, built-in Schema Registry and HTTP Proxy. p99 latency 10x lower than Kafka. Best for teams wanting Kafka compatibility with simpler operations
NATS: Lightweight Go binary, 10MB footprint. Core pub/sub + JetStream for persistence. Request-reply pattern, key-value store, object store. Best for microservice communication and edge computing
Apache Kafka: Java/Scala, distributed log. Kafka Streams, Connect, Schema Registry, exactly-once semantics. Massive ecosystem. Best for enterprise event-driven architectures with complex stream processing

Redpanda — Kafka-Compatible, Zero JVM

Redpanda gives you a Kafka-compatible streaming platform in a single binary — no JVM, no ZooKeeper, and 10x lower p99 latency.

Producer — Using KafkaJS (100% Compatible)

// Redpanda is Kafka API compatible — use any Kafka client
import { Kafka, Partitioners } from "kafkajs";

const kafka = new Kafka({
  clientId: "order-service",
  brokers: ["redpanda-0:9092", "redpanda-1:9092", "redpanda-2:9092"],
  // Same config as Kafka — just point to Redpanda brokers
});

const producer = kafka.producer({
  createPartitioner: Partitioners.DefaultPartitioner,
  idempotent: true, // Exactly-once production
});

await producer.connect();

// Send events
await producer.send({
  topic: "orders",
  messages: [
    {
      key: "order-123",
      value: JSON.stringify({
        orderId: "order-123",
        customerId: "cust-42",
        items: [{ sku: "WIDGET-A", quantity: 3, price: 29.99 }],
        total: 89.97,
        timestamp: Date.now(),
      }),
      headers: {
        "event-type": "order.created",
        "correlation-id": correlationId,
      },
    },
  ],
});

// Batch send for high throughput
await producer.sendBatch({
  topicMessages: [
    {
      topic: "orders",
      messages: orders.map((o) => ({
        key: o.orderId,
        value: JSON.stringify(o),
      })),
    },
    {
      topic: "analytics",
      messages: analyticsEvents.map((e) => ({
        key: e.userId,
        value: JSON.stringify(e),
      })),
    },
  ],
});

Consumer with Consumer Groups

const consumer = kafka.consumer({
  groupId: "order-processor",
  sessionTimeout: 30000,
  heartbeatInterval: 3000,
});

await consumer.connect();
await consumer.subscribe({
  topics: ["orders"],
  fromBeginning: false,
});

await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const order = JSON.parse(message.value!.toString());
    const eventType = message.headers?.["event-type"]?.toString();

    console.log(`[${topic}:${partition}] ${eventType}: ${order.orderId}`);

    switch (eventType) {
      case "order.created":
        await processNewOrder(order);
        break;
      case "order.paid":
        await fulfillOrder(order);
        break;
      case "order.cancelled":
        await handleCancellation(order);
        break;
    }
  },
  // Auto-commit offsets after processing
  autoCommitInterval: 5000,
});

// Batch processing for higher throughput
await consumer.run({
  eachBatch: async ({ batch, resolveOffset, heartbeat }) => {
    for (const message of batch.messages) {
      await processMessage(message);
      resolveOffset(message.offset);
      await heartbeat(); // Keep session alive during long batches
    }
  },
});

Redpanda Admin API (rpk + HTTP)

// Redpanda HTTP Proxy — REST API for admin operations
const REDPANDA_URL = "http://redpanda:8082";

// Create a topic
await fetch(`${REDPANDA_URL}/topics`, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    topic: "events",
    partitions: 12,
    replication_factor: 3,
    configs: {
      "retention.ms": "604800000", // 7 days
      "segment.bytes": "1073741824", // 1 GB segments
      "cleanup.policy": "delete",
    },
  }),
});

// Produce via HTTP (useful for serverless/edge)
await fetch(`${REDPANDA_URL}/topics/events`, {
  method: "POST",
  headers: { "Content-Type": "application/vnd.kafka.json.v2+json" },
  body: JSON.stringify({
    records: [
      { key: "evt-1", value: { type: "page_view", url: "/pricing" } },
      { key: "evt-2", value: { type: "signup", email: "user@ex.com" } },
    ],
  }),
});

// Schema Registry — Redpanda includes it built-in
await fetch(`${REDPANDA_URL}/subjects/orders-value/versions`, {
  method: "POST",
  headers: { "Content-Type": "application/vnd.schemaregistry.v1+json" },
  body: JSON.stringify({
    schemaType: "JSON",
    schema: JSON.stringify({
      type: "object",
      properties: {
        orderId: { type: "string" },
        total: { type: "number" },
        items: { type: "array" },
      },
      required: ["orderId", "total"],
    }),
  }),
});

NATS — Lightweight Cloud-Native Messaging

NATS is a simple, high-performance messaging system — core pub/sub, request-reply, and JetStream for persistent streaming.

Core Pub/Sub

import { connect, StringCodec, JSONCodec } from "nats";

const nc = await connect({
  servers: ["nats://localhost:4222"],
  // Cluster:
  // servers: ["nats://node-1:4222", "nats://node-2:4222", "nats://node-3:4222"],
});

const jc = JSONCodec();

// Publish a message (fire-and-forget)
nc.publish("orders.created", jc.encode({
  orderId: "order-123",
  customerId: "cust-42",
  total: 89.97,
}));

// Subscribe to messages
const sub = nc.subscribe("orders.*");
(async () => {
  for await (const msg of sub) {
    const order = jc.decode(msg.data);
    const eventType = msg.subject; // "orders.created", "orders.paid", etc.

    console.log(`Received ${eventType}:`, order);
    await processOrder(eventType, order);
  }
})();

// Wildcard subscriptions
nc.subscribe("orders.>"); // Match all under orders.*.*
nc.subscribe("*.created"); // Match any service's .created events

// Queue groups — load balance across consumers
const qsub = nc.subscribe("orders.created", { queue: "order-processors" });
// Only ONE subscriber in the queue group receives each message
(async () => {
  for await (const msg of qsub) {
    await processOrder(jc.decode(msg.data));
  }
})();

Request-Reply Pattern

// Request-reply — synchronous-style RPC over NATS
// Service: handle requests
const sub = nc.subscribe("inventory.check");
(async () => {
  for await (const msg of sub) {
    const request = jc.decode(msg.data);
    const stock = await checkInventory(request.sku);

    // Reply to the requester
    msg.respond(jc.encode({
      sku: request.sku,
      available: stock > 0,
      quantity: stock,
    }));
  }
})();

// Client: send request and wait for reply
const response = await nc.request(
  "inventory.check",
  jc.encode({ sku: "WIDGET-A" }),
  { timeout: 5000 } // 5 second timeout
);

const inventory = jc.decode(response.data);
console.log(`Stock for WIDGET-A: ${inventory.quantity}`);

// Scatter-gather — request from multiple services
// Each service with the same subscription gets the request
// First response wins (or collect all within timeout)

JetStream — Persistent Streaming

// JetStream adds persistence, exactly-once delivery, and replay
const js = nc.jetstream();
const jsm = await nc.jetstreamManager();

// Create a stream (like a Kafka topic)
await jsm.streams.add({
  name: "ORDERS",
  subjects: ["orders.>"], // Capture all order events
  retention: "limits", // "limits" | "interest" | "workqueue"
  max_msgs: -1, // Unlimited
  max_bytes: 10 * 1024 * 1024 * 1024, // 10 GB
  max_age: 7 * 24 * 60 * 60 * 1000000000, // 7 days (nanoseconds)
  storage: "file", // "file" | "memory"
  num_replicas: 3,
  discard: "old",
});

// Publish to JetStream (with acknowledgment)
const ack = await js.publish("orders.created", jc.encode({
  orderId: "order-123",
  total: 89.97,
}));

console.log(`Published: seq=${ack.seq}, stream=${ack.stream}`);

// Durable consumer — survives restarts
await jsm.consumers.add("ORDERS", {
  durable_name: "order-processor",
  deliver_policy: "all", // "all" | "last" | "new" | "by_start_sequence"
  ack_policy: "explicit",
  ack_wait: 30000000000, // 30s in nanoseconds
  max_deliver: 5, // Max redelivery attempts
  filter_subject: "orders.created",
});

// Consume messages
const consumer = await js.consumers.get("ORDERS", "order-processor");

const messages = await consumer.consume();
for await (const msg of messages) {
  try {
    const order = jc.decode(msg.data);
    await processOrder(order);
    msg.ack(); // Acknowledge successful processing
  } catch (error) {
    msg.nak(); // Negative ack — redelivery
  }
}

Key-Value Store and Object Store

// NATS KV — distributed key-value store built on JetStream
const kv = await js.views.kv("app-config", {
  history: 5, // Keep last 5 versions
  ttl: 0, // No expiry
});

// Put values
await kv.put("feature.dark-mode", jc.encode({ enabled: true, rollout: 0.5 }));
await kv.put("rate-limit.api", jc.encode({ rpm: 1000, burst: 50 }));

// Get values
const entry = await kv.get("feature.dark-mode");
const config = jc.decode(entry!.value);
console.log(`Dark mode: ${config.enabled}, rollout: ${config.rollout}`);

// Watch for changes (real-time config updates)
const watch = await kv.watch({ key: "feature.>" });
(async () => {
  for await (const entry of watch) {
    console.log(`Config changed: ${entry.key} = ${jc.decode(entry.value)}`);
    updateLocalConfig(entry.key, jc.decode(entry.value));
  }
})();

// Object store — large file storage on NATS
const os = await js.views.os("artifacts");

// Store a file
await os.put({ name: "model-v2.onnx" }, readableStream);

// Retrieve a file
const result = await os.get("model-v2.onnx");
const data = await result!.data;

Apache Kafka — Enterprise Event Streaming

Apache Kafka is the industry-standard distributed event streaming platform — billions of events per day, exactly-once semantics, and the richest ecosystem.

Producer with KafkaJS

import { Kafka, CompressionTypes, logLevel } from "kafkajs";

const kafka = new Kafka({
  clientId: "order-service",
  brokers: ["kafka-0:9092", "kafka-1:9092", "kafka-2:9092"],
  ssl: true,
  sasl: {
    mechanism: "scram-sha-256",
    username: process.env.KAFKA_USERNAME!,
    password: process.env.KAFKA_PASSWORD!,
  },
  logLevel: logLevel.WARN,
});

const producer = kafka.producer({
  idempotent: true,
  maxInFlightRequests: 5,
  transactionalId: "order-producer", // Enable transactions
});

await producer.connect();

// Transactional produce — exactly-once across topics
const transaction = await producer.transaction();
try {
  await transaction.send({
    topic: "orders",
    messages: [{ key: orderId, value: JSON.stringify(order) }],
  });

  await transaction.send({
    topic: "inventory-updates",
    messages: [{ key: order.sku, value: JSON.stringify({ delta: -order.qty }) }],
  });

  await transaction.sendOffsets({
    consumerGroupId: "order-processor",
    topics: [{ topic: "incoming-orders", partitions: [{ partition: 0, offset: "42" }] }],
  });

  await transaction.commit();
} catch (error) {
  await transaction.abort();
  throw error;
}

Consumer with Exactly-Once Processing

const consumer = kafka.consumer({
  groupId: "order-processor",
  readUncommitted: false, // Only read committed messages
  sessionTimeout: 30000,
  rebalanceTimeout: 60000,
});

await consumer.connect();
await consumer.subscribe({ topics: ["orders"], fromBeginning: false });

// Manual offset management for exactly-once
await consumer.run({
  autoCommit: false,
  eachMessage: async ({ topic, partition, message }) => {
    const order = JSON.parse(message.value!.toString());

    // Process with idempotency check
    const processed = await isAlreadyProcessed(message.offset, partition);
    if (processed) return;

    await processOrder(order);
    await markProcessed(message.offset, partition);

    // Commit offset after successful processing
    await consumer.commitOffsets([{
      topic,
      partition,
      offset: (BigInt(message.offset) + 1n).toString(),
    }]);
  },
});

// Seek to specific offset (replay events)
consumer.seek({
  topic: "orders",
  partition: 0,
  offset: "1000", // Replay from offset 1000
});

Admin Operations

const admin = kafka.admin();
await admin.connect();

// Create topic with configuration
await admin.createTopics({
  topics: [
    {
      topic: "events",
      numPartitions: 24,
      replicationFactor: 3,
      configEntries: [
        { name: "retention.ms", value: "604800000" },       // 7 days
        { name: "cleanup.policy", value: "compact,delete" }, // Log compaction
        { name: "min.insync.replicas", value: "2" },         // Durability
        { name: "compression.type", value: "zstd" },         // Compression
      ],
    },
  ],
});

// List consumer group offsets and lag
const offsets = await admin.fetchOffsets({ groupId: "order-processor" });
const topicOffsets = await admin.fetchTopicOffsets("orders");

for (const partition of offsets) {
  const latest = topicOffsets.find((t) => t.partition === partition.partition);
  const lag = BigInt(latest!.offset) - BigInt(partition.offset);
  console.log(`Partition ${partition.partition}: offset=${partition.offset}, lag=${lag}`);
}

// Alter consumer group offsets (reset to earliest)
await admin.setOffsets({
  groupId: "order-processor",
  topic: "orders",
  partitions: [
    { partition: 0, offset: "0" },
    { partition: 1, offset: "0" },
  ],
});

await admin.disconnect();

Kafka Connect (REST API)

// Kafka Connect — source and sink connectors
const CONNECT_URL = "http://connect:8083";

// Create a PostgreSQL CDC source connector (Debezium)
await fetch(`${CONNECT_URL}/connectors`, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    name: "postgres-source",
    config: {
      "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
      "database.hostname": "postgres",
      "database.port": "5432",
      "database.user": "debezium",
      "database.password": process.env.DB_PASSWORD,
      "database.dbname": "app",
      "topic.prefix": "cdc",
      "table.include.list": "public.orders,public.users",
      "slot.name": "debezium_slot",
      "publication.name": "debezium_pub",
    },
  }),
});

// Create an Elasticsearch sink connector
await fetch(`${CONNECT_URL}/connectors`, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    name: "elasticsearch-sink",
    config: {
      "connector.class": "io.confluent.connect.elasticsearch.ElasticsearchSinkConnector",
      "topics": "cdc.public.orders",
      "connection.url": "http://elasticsearch:9200",
      "type.name": "_doc",
      "key.ignore": "false",
      "schema.ignore": "true",
    },
  }),
});

Feature Comparison

Feature	Redpanda	NATS	Apache Kafka
Language	C++	Go	Java/Scala
Protocol	Kafka API (compatible)	NATS protocol	Kafka protocol
Dependencies	Single binary	Single binary	JVM + ZooKeeper/KRaft
Latency (p99)	~2ms	~1ms	~10-50ms
Throughput	Millions msg/s	Millions msg/s	Millions msg/s
Persistence	✅ (built-in)	JetStream (opt-in)	✅ (default)
Exactly-Once	✅ (Kafka-compatible)	JetStream (ack-based)	✅ (transactions)
Pub/Sub	Topic-based	Subject-based (wildcards)	Topic-based
Request-Reply	❌ (use separate topics)	✅ (built-in)	❌ (use separate topics)
Consumer Groups	✅ (Kafka groups)	Queue groups + durable	✅ (consumer groups)
Schema Registry	✅ (built-in)	❌	✅ (Confluent)
Stream Processing	Kafka Streams compatible	❌ (external)	Kafka Streams, ksqlDB
Connectors	Kafka Connect compatible	❌ (custom)	Kafka Connect (1000+)
Key-Value Store	❌	✅ (NATS KV)	❌
Object Store	❌	✅ (NATS Object Store)	❌
Replication	Raft consensus	Raft (JetStream)	ISR replication
Multi-Tenancy	✅ (ACLs)	✅ (accounts)	✅ (ACLs)
Monitoring	Built-in console	Built-in monitoring	JMX + external tools
Cloud Managed	Redpanda Cloud	Synadia Cloud	Confluent, MSK, Aiven
License	BSL → Apache 2.0	Apache 2.0	Apache 2.0
Operational Complexity	Low	Very low	High
Best For	Kafka migration	Microservices, edge	Enterprise streaming

When to Use Each

Choose Redpanda if:

You want Kafka compatibility without JVM operational burden
Lower latency matters (10x better p99 than Kafka)
You're migrating from Kafka and need drop-in compatibility
You want built-in Schema Registry and admin console
Simpler deployment (single binary, no ZooKeeper) is important

Choose NATS if:

Lightweight microservice communication is the primary use case
Request-reply pattern is important for your architecture
You need a built-in key-value store for configuration
Edge computing or IoT with minimal resource footprint matters
Pub/sub with wildcard subject routing fits your event model

Choose Apache Kafka if:

You need the full ecosystem (Kafka Streams, Connect, ksqlDB)
1000+ pre-built connectors for sources and sinks are important
Exactly-once transactional processing across topics is required
Enterprise features, compliance, and Confluent support are deal requirements
You're building complex stream processing pipelines

Production Deployment and Operational Complexity

Kafka's reputation for operational complexity is largely earned. A production Kafka cluster historically required ZooKeeper for coordination, separate Schema Registry and Kafka Connect deployments, and significant JVM tuning to avoid garbage collection pauses that spike p99 latency. The move to KRaft mode (Kafka without ZooKeeper) in Kafka 3.x simplified this somewhat, but the JVM is still present, and teams need expertise in JVM memory tuning, GC configuration, and broker sizing to get stable low-latency behavior. Redpanda's single binary deployment with no JVM and no ZooKeeper is genuinely simpler — the default configuration works well for most workloads, and Redpanda's CPU-based resource management is more predictable than JVM heap tuning. NATS is the simplest of the three to operate: a single Go binary with a small configuration file, no external dependencies, and a cluster formed by pointing nodes at each other. NATS Cloud (Synadia) handles managed deployment if self-hosting is not desired.

Security Considerations for Event Streaming

All three platforms support TLS encryption in transit and authentication, but their security models differ in depth. Kafka has the most complete security posture: SASL authentication (SCRAM, GSSAPI/Kerberos, OAuth), SSL mutual TLS, ACLs down to individual topic operations, and Kafka 3.x introduced improved delegation token support for short-lived credentials. For enterprises with compliance requirements (SOC 2, HIPAA, PCI DSS), Kafka's security model is typically sufficient out of the box. Redpanda implements the same Kafka security model at the protocol level, so existing Kafka ACLs and SASL configurations carry over directly. NATS uses a distinct security model based on NKeys (Ed25519 key pairs) and JWT-based credentials, with user-level permissions scoped to specific subjects. The NATS security model is elegant but requires learning a new paradigm if your team is familiar with Kafka's SASL/ACL approach. For zero-trust architectures, NATS's account-based isolation is particularly strong — each account is a completely isolated messaging domain.

Schema Management and Data Contracts

One of Kafka's most underappreciated strengths is its Schema Registry ecosystem. Confluent's Schema Registry (also bundled in Redpanda) enforces schema compatibility rules (backward, forward, full) on a per-subject basis, preventing producers from publishing messages that would break existing consumers. Combined with Avro, Protobuf, or JSON Schema, this creates enforceable data contracts that catch integration bugs at publish time rather than in production. NATS has no built-in schema registry — teams must implement schema validation in application code or use an external service. For microservice architectures where multiple teams produce to shared topics, the absence of schema enforcement in NATS is a real operational risk that tends to manifest as silent data corruption bugs. Redpanda bundles its own Schema Registry that is fully compatible with Confluent's API, making it a drop-in replacement without giving up schema enforcement.

Migration Paths Between Platforms

Teams sometimes need to migrate between these platforms as their requirements evolve. Moving from Kafka to Redpanda is the easiest migration in the streaming world — Redpanda accepts Kafka clients without modification. Point your KafkaJS or kafkajs-based producers and consumers at Redpanda brokers and they work. The main migration concern is offset compatibility: Redpanda uses the same offset format as Kafka, so existing consumer group offsets can be imported via rpk tooling. Moving from NATS to Kafka (or vice versa) is a more significant rewrite because the subject/topic models, consumer patterns, and client libraries are fundamentally different. Teams moving to NATS often do so when they discover their Kafka-based service communication pattern actually maps better to request-reply than pub/sub, and NATS's built-in request-reply is significantly simpler than building it on top of Kafka topics. Moving to Kafka from NATS usually happens when persistence, exactly-once semantics, or the Kafka Connect ecosystem become requirements.

Pricing Models and Self-Hosting Economics

At small scale, all three can be run cheaply on modest hardware. A single-node NATS server handles millions of messages per second on a two-core machine — the footprint is genuinely minimal. Kafka requires more resources even at small scale: three brokers for fault tolerance, each needing at least 8GB of RAM for reasonable performance. Redpanda is more resource-efficient than Kafka but still a multi-process deployment for production use. At large scale, the cost comparison shifts: Kafka on managed services (Confluent Cloud, AWS MSK) charges per CKU (Confluent) or per broker-hour (MSK), which adds up quickly for high-throughput workloads. Redpanda Cloud competes on price and often comes in cheaper than Confluent for equivalent throughput. NATS via Synadia Cloud is priced per message for the cloud tier, which is economical for bursty workloads but can become expensive for sustained high-volume streaming. Self-hosting all three eliminates SaaS costs but adds engineering time for operations and monitoring.

Methodology

Feature comparison based on Redpanda v24.x, NATS v2.x with JetStream, and Apache Kafka 3.x documentation as of March 2026. Performance characteristics from published benchmarks. Code examples use KafkaJS for Redpanda/Kafka and nats.js for NATS. Evaluated on: operational simplicity, latency, throughput, feature set, ecosystem, and Node.js developer experience.

The 2026 JavaScript Stack Cheatsheet