RudderStack vs Jitsu vs Snowplow: Best Self-Hosted CDP 2026

Customer data platforms (CDPs) sit at the center of your data infrastructure. They collect events from your websites, apps, and servers, then route that data to warehouses, analytics tools, and marketing platforms. For years, Segment was the default choice — until its acquisition by Twilio, rising costs, and data residency concerns pushed teams toward open-source alternatives.

In this guide, we compare the three leading self-hosted CDPs: RudderStack, Jitsu, and Snowplow. Each takes a different architectural approach, and the right choice depends on your team’s scale, technical expertise, and destination requirements.

For related data infrastructure reading, see our data pipeline comparison (Airbyte vs Meltano vs Singer), our data orchestration guide, and the OpenTelemetry collector pipeline overview.

Why Self-Host Your Customer Data Platform?

Running a CDP on your own infrastructure solves several problems that SaaS solutions introduce:

• Data sovereignty: Customer events never leave your network. This matters for GDPR, HIPAA, and financial compliance regimes where cross-border data transfer is restricted or audited.
• Cost control: Segment’s pricing scales with monthly tracked users (MTUs) — a model that penalizes growth. Self-hosted CDPs incur infrastructure costs that are typically a fraction of SaaS pricing at scale.
• No vendor lock-in: Open-source CDPs let you swap destinations, add custom transformations, and modify the pipeline without waiting on a vendor’s roadmap.
• Lower latency: When the CDP runs in your own VPC or data center, event ingestion and delivery happen over private networks, avoiding public internet round-trips.
• Full auditability: You own the event logs, the transformation code, and the destination connectors. Debugging data quality issues doesn’t require opening a support ticket.

RudderStack: The Segment-Compatible CDP

RudderStack is an open-source CDP written in Go and React. It positions itself as a direct Segment alternative, offering a nearly identical SDK API and a broad destination ecosystem. With 4,396 GitHub stars and recent activity as of April 2026, it is one of the most actively maintained open-source CDPs.

Architecture

RudderStack’s architecture consists of four main components:

1. SDKs — JavaScript, Android, iOS, Python, Go, and more, compatible with the Segment Analytics.js API
2. RudderServer (backend) — The core Go service that receives events, applies transformations, and routes them to destinations
3. RudderTransformer — A Node.js service for custom event transformations (User Tracking Plan enforcement, field mapping, filtering)
4. Storage — PostgreSQL for metadata and event buffering, with optional MinIO/S3 for long-term storage

Events flow from SDKs through the backend, optionally through the transformer, then fan out to configured destinations in parallel. RudderStack uses a warehouse-first approach: events are batched and written to a data warehouse, then synced downstream.

Key Features

docker Compose Deployment

Here is a production-ready Docker Compose setup based on the official docker-compose.yml:

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version: "3.7"

services:
  rudder-db:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: rudder
      POSTGRES_PASSWORD: rudder_password
      POSTGRES_DB: rudderdb
    volumes:
      - rudder-db-data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  rudder-transformer:
    image: rudderstack/rudder-transformer:latest
    ports:
      - "9090:9090"

  rudder-server:
    image: rudderstack/rudder-server:latest
    depends_on:
      - rudder-db
      - rudder-transformer
    ports:
      - "8080:8080"
    environment:
      JOBS_DB_HOST: rudder-db
      JOBS_DB_PORT: 5432
      JOBS_DB_USER: rudder
      JOBS_DB_PASSWORD: rudder_password
      JOBS_DB_NAME: rudderdb
      JOBS_DB_SSL_MODE: disable
      CONFIG_BACKEND_URL: http://rudder-server:8080
      CONFIG_BACKEND_TOKEN: <your-workspace-token>
      REACT_APP_BACKEND_URL: http://localhost:8080
      RUDDER_TMPDIR: /tmp/rudder
      TRANSFORMER_URL: http://rudder-transformer:9090
    volumes:
      - rudder-config:/etc/rudderstack
      - /tmp/rudder:/tmp/rudder

volumes:
  rudder-db-data:
  rudder-config:

Start the stack:

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docker compose up -d

The RudderStack dashboard will be available at http://localhost:8080.

SDK Integration

RudderStack’s JavaScript SDK is drop-in compatible with Segment’s API:

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<script>
  rudderanalytics = window.rudderanalytics = [];
  var methods = ["load", "page", "track", "identify", "alias", "group", "ready", "reset"];
  for (var i = 0; i < methods.length; i++) {
    (function(methodName) {
      rudderanalytics[methodName] = function() {
        rudderanalytics.push([methodName].concat(Array.prototype.slice.call(arguments)));
      };
    })(methods[i]);
  }
</script>
<script src="https://cdn.rudderlabs.com/v1.1/rudder-analytics.min.js"></script>
<script>
  rudderanalytics.load("<WRITE_KEY>", "http://localhost:8080");
  rudderanalytics.page();
  rudderanalytics.track("Signed Up", { plan: "Pro", source: "Website" });
</script>

Pricing and Licensing

RudderStack is available under the MIT License for the core server. The company offers an Enterprise edition with additional features like SSO, advanced RBAC, and SLAs.

Jitsu: The Real-Time Data Ingestion Engine

Jitsu is an open-source data ingestion engine written in TypeScript. It takes a broader view than a traditional CDP — calling itself a “fully-scriptable data ingestion engine for modern data teams.” With 4,693 GitHub stars and active development on its newjitsu branch, Jitsu has grown a dedicated following.

Architecture

Jitsu’s architecture is built around three core services:

1. Console — The Next.js web UI for configuration, event browser, and stream management
2. Rotor — Event processing engine that applies JavaScript-based transformations and routes events
3. Bulker — High-throughput data loader that writes events to destinations in bulk

The platform uses PostgreSQL for metadata, ClickHouse for analytics, MongoDB for profile storage, and Redpanda (Kafka-compatible) as the event bus. This gives Jitsu strong real-time processing capabilities.

Key Features

Docker Compose Deployment

Jitsu’s Docker Compose setup is more complex than RudderStack’s, reflecting its multi-service architecture. Based on the official docker/docker-compose.yml:

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name: jitsu

services:
  # Infrastructure dependencies
  dep-postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_PASSWORD: postgres-pass
    volumes:
      - pg-data:/var/lib/postgresql/data

  dep-clickhouse:
    image: clickhouse/clickhouse-server:24
    environment:
      CLICKHOUSE_DB: default
      CLICKHOUSE_USER: default
      CLICKHOUSE_PASSWORD: clickhouse-pass
    volumes:
      - ch-data:/var/lib/clickhouse

  dep-mongodb:
    image: mongo:7
    environment:
      MONGO_INITDB_ROOT_USERNAME: admin
      MONGO_INITDB_ROOT_PASSWORD: mongo-pass
    volumes:
      - mongo-data:/data/db

  dep-redpanda:
    image: docker.redpanda.com/redpandadata/redpanda:v24.2
    command:
      - redpanda start
      - --smp 1
      - --overprovisioned
      - --kafka-addr internal://0.0.0.0:9092
      - --advertise-kafka-addr internal://dep-redpanda:9092
    volumes:
      - rp-data:/var/lib/redpanda/data

  # Jitsu services
  console:
    image: jitsucom/console:latest
    ports:
      - "3000:3000"
    environment:
      DATABASE_URL: postgresql://postgres:postgres-pass@dep-postgres:5432/postgres?schema=newjitsu
      CLICKHOUSE_URL: http://dep-clickhouse:8123/
      CLICKHOUSE_PASSWORD: clickhouse-pass
      MONGODB_URL: mongodb://admin:mongo-pass@dep-mongodb:27017/admin
      KAFKA_BOOTSTRAP_SERVERS: dep-redpanda:9092
      JWT_SECRET: <your-jwt-secret>
      CONSOLE_RAW_AUTH_TOKENS: dev-auth-key

  rotor:
    image: jitsucom/rotor:latest
    environment:
      DATABASE_URL: postgresql://postgres:postgres-pass@dep-postgres:5432/postgres?schema=newjitsu
      CLICKHOUSE_URL: http://dep-clickhouse:8123/
      CLICKHOUSE_PASSWORD: clickhouse-pass
      KAFKA_BOOTSTRAP_SERVERS: dep-redpanda:9092
      REPOSITORY_BASE_URL: http://console:3000/api/admin/export
      REPOSITORY_AUTH_TOKEN: service-admin-account:dev-auth-key
      ROTOR_RAW_AUTH_TOKENS: dev-auth-key

  bulker:
    image: jitsucom/bulker:latest
    environment:
      BULKER_KAFKA_BOOTSTRAP_SERVERS: dep-redpanda:9092
      BULKER_RAW_AUTH_TOKENS: dev-auth-key
      BULKER_CONFIG_SOURCE: http://console:3000/

volumes:
  pg-data:
  ch-data:
  mongo-data:
  rp-data:

Start with:

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cd docker && docker compose up -d

The Jitsu Console will be available at http://localhost:3000.

SDK Integration

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import { JitsuClient } from "@jitsu/js";

const jitsu = new JitsuClient({
  host: "http://localhost:3000",
  cookieName: "__eventn_id",
});

jitsu.track("purchase_completed", {
  order_id: "ORD-12345",
  amount: 49.99,
  currency: "USD",
});

Pricing and Licensing

Jitsu is released under the MIT License. The core engine is fully open-source. Jitsu also offers a cloud-hosted version for teams that prefer managed infrastructure.

Snowplow: The Enterprise-Grade Data Collection Platform

Snowplow is the oldest and most established of the three, with 7,008 GitHub stars. It is written primarily in Scala and takes an event schema-first approach to data collection. Snowplow is designed for large organizations that need granular data governance, detailed event schemas, and the ability to process billions of events per day.

Architecture

Snowplow’s pipeline is a multi-stage, streaming architecture:

1. Trackers — SDKs for web, mobile, server-side, and IoT that collect events
2. Collector — A Scala-based HTTP service (or CloudFront/NGINX) that receives events and writes them to a stream (Kinesis, Kafka, NSQ, or SQS)
3. Enrich — A Spark/Beam/Flink job that reads from the stream, applies enrichments (IP lookup, user agent parsing, referral extraction), validates against Iglu schemas, and writes enriched events back to the stream
4. Storage — Loaders write events from the stream to PostgreSQL, Redshift, BigQuery, Snowflake, or S3

This pipeline is designed for high-throughput, batch-or-stream processing. Unlike RudderStack and Jitsu, Snowplow does not include a built-in web UI — configuration is managed through JSON/YAML files and the Iglu schema registry.

Key Features

Docker Deployment

Snowplow does not ship a single docker-compose.yml because its pipeline comprises multiple independent components. A minimal self-hosted setup typically uses the following Docker images:

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version: "3.7"

services:
  # Kafka as the streaming backbone
  kafka:
    image: confluentinc/cp-kafka:7.6
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1

  zookeeper:
    image: confluentinc/cp-zookeeper:7.6
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181

  # Iglu schema registry
  iglu-server:
    image: snowplow/iglu-server:latest
    ports:
      - "8081:8080"
    environment:
      IGLU_PG_USERNAME: iglu
      IGLU_PG_PASSWORD: iglu_pass
      IGLU_PG_URL: jdbc:postgresql://iglu-db:5432/iglu

  iglu-db:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: iglu
      POSTGRES_PASSWORD: iglu_pass
      POSTGRES_DB: iglu

  # Snowplow Stream Collector
  collector:
    image: snowplow/stream-collector-stdout:latest
    ports:
      - "8080:8080"
    depends_on:
      - kafka
    # Production: use the Kafka or NSQ collector instead of stdout

  # Enrich
  enrich:
    image: snowplow/enrich-kafka:latest
    depends_on:
      - kafka
      - iglu-server
    environment:
      IGLU_RESOLVER: |
        {
          "schema": "iglu:com.snowplowanalytics.iglu/resolver-config/jsonschema/1-0-1",
          "data": {
            "cacheSize": 500,
            "repositories": [
              {
                "name": "Iglu Central",
                "priority": 0,
                "vendorPrefixes": ["com.snowplowanalytics"],
                "connection": {
                  "http": {
                    "uri": "http://iglu-server:8080"
                  }
                }
              }
            ]
          }
   [kubernetes](https://kubernetes.io/)

Snowplow also provides Helm charts for Kubernetes deployments, which is the recommended approach for production at scale.

### SDK Integration

```javascript
import { newTracker } from "@snowplow/javascript-tracker";

newTracker("sp1", "http://localhost:8080", {
  appId: "my-website",
  discoverRootDomain: true,
  cookieSameSite: "Lax",
});

window.snowplow("trackPageView");
window.snowplow("trackSelfDescribingEvent", {
  event: {
    schema: "iglu:com.acme/purchase/jsonschema/1-0-0",
    data: {
      orderId: "ORD-12345",
      total: 49.99,
      currency: "USD",
    },
  },
});

Pricing and Licensing

Snowplow is released under the Apache 2.0 License. The core pipeline components are fully open-source. Snowplow offers a managed cloud version (Snowplow Insights) and an enterprise support tier.

Head-to-Head Comparison

Choosing the Right CDP

Choose RudderStack if:

• You are migrating from Segment and want minimal SDK changes
• You need the broadest destination ecosystem (200+ connectors)
• You prefer a simple Docker Compose setup with few moving parts
• You want a web-based dashboard for configuration and monitoring
• Your team values Go-based performance and reliability

Choose Jitsu if:

• You want real-time event processing with a Kafka-compatible backbone
• You value a rich web UI with a live event browser
• You need built-in user profile storage and enrichment
• You want JavaScript-based transformations with npm package support
• ClickHouse as an analytics destination is important to you

Choose Snowplow if:

• You need enterprise-grade data governance with schema validation at ingestion
• You process billions of events and need a streaming architecture
• You have dedicated data engineering resources to manage the pipeline
• You want the most granular control over event schemas and enrichments
• You plan to deploy on Kubernetes with Helm

For teams already using data orchestration tools like Airflow or Prefect, Snowplow’s warehouse-first output integrates cleanly with downstream dbt transformations. For teams evaluating the broader data quality landscape, all three CDPs feed clean, validated events into your warehouse where quality tools can take over.

FAQ

What is the difference between a CDP and a data pipeline tool like Airbyte?

A CDP (Customer Data Platform) focuses on real-time event collection from user-facing applications (websites, mobile apps) and routing those events to downstream systems. Data pipeline tools like Airbyte are designed for batch ETL — moving data between databases, APIs, and warehouses on a schedule. They complement each other: a CDP handles live user events, while Airbyte handles periodic batch syncs from SaaS APIs.

Can I run RudderStack or Jitsu on a single server?

Yes. RudderStack’s minimum setup requires PostgreSQL and the RudderServer process — both can run on a 2-core, 4GB RAM machine for low-to-moderate traffic. Jitsu requires more resources due to its multi-service architecture (PostgreSQL, ClickHouse, MongoDB, Redpanda, Console, Rotor, Bulker), so a 4-core, 8GB RAM machine is a more realistic minimum.

Does Snowplow require Kafka?

Snowplow’s production architecture uses a streaming backbone (Kafka, Kinesis, NSQ, or SQS) between the Collector and Enrich stages. For testing or low-traffic scenarios, you can use a single-node Kafka or NSQ instance. Snowplow Micro — a minimal testing version — runs entirely in memory without any streaming infrastructure.

How do I migrate from Segment to a self-hosted CDP?

RudderStack is the easiest migration path because its SDKs are drop-in compatible with Segment’s Analytics.js API. You typically only need to change the SDK initialization URL from cdn.segment.com to your self-hosted endpoint and swap the write key. Jitsu and Snowplow require SDK code changes since they use their own tracking APIs.

Are these CDPs production-ready for high-traffic websites?

Yes. All three platforms are used in production by companies processing millions of events daily. RudderStack and Jitsu handle traffic spikes through horizontal scaling of their backend services. Snowplow’s streaming architecture is specifically designed for enterprise-scale event processing. The limiting factor is usually your destination systems (warehouse write throughput) rather than the CDP itself.