Jaeger vs Zipkin: Distributed Tracing Comparison on Kubernetes

Distributed tracing has become essential for diagnosing latency issues in microservices architectures.

Jaeger vs Zipkin distributed tracing Kubernetes represents the major technical choice every Kubernetes software engineer must master. With 82% of container users running Kubernetes in production (CNCF Annual Survey 2025), choosing a performant tracing tool directly impacts the ability to resolve incidents quickly.

TL;DR: Jaeger offers better scalability and native integration with the CNCF ecosystem, while Zipkin suits teams looking for a lightweight and mature solution. The choice depends on your cluster size and advanced feature needs.

To deepen these skills, discover the LFD459 Kubernetes for Application Developers training.

What is Distributed Tracing on Kubernetes?

Distributed tracing is an observability method that follows a request's path through multiple services. Each service generates spans containing temporal metadata. Aggregating these spans forms a complete trace.

On Kubernetes, distributed tracing addresses a specific challenge: pods communicate via overlay networks, services can be replicated, and requests traverse dozens of components. Without tracing, identifying the source of latency becomes impossible.

Remember: Distributed tracing transforms microservices opacity into actionable visibility. Each trace tells the complete story of a request.

Consult our guide on Kubernetes observability: metrics, logs and traces to understand how tracing fits into an overall strategy.

Jaeger vs Zipkin Distributed Tracing Kubernetes: Architecture Comparison

Jaeger Architecture

Jaeger, a CNCF graduated project, uses a modular architecture:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
name: production-jaeger
spec:
strategy: production
storage:
type: elasticsearch
options:
es:
server-urls: https://elasticsearch:9200
collector:
maxReplicas: 5

Zipkin Architecture

Zipkin offers a simpler architecture:

apiVersion: apps/v1
kind: Deployment
metadata:
name: zipkin
spec:
replicas: 3
template:
spec:
containers:
- name: zipkin
image: openzipkin/zipkin:3.4
env:
- name: STORAGE_TYPE
value: elasticsearch

Remember: Jaeger decouples its components for fine-grained horizontal scalability. Zipkin favors operational simplicity with fewer moving parts.

Jaeger vs Zipkin Distributed Tracing Kubernetes: Selection Criteria

Performance and Scalability

Jaeger outperforms Zipkin on massive deployments. Its architecture with Kafka as a buffer can absorb traffic spikes without span loss. Organizations managing 20+ clusters (Spectro Cloud State of Kubernetes 2025) prefer Jaeger.

Zipkin suits environments processing less than 100,000 spans/second. Beyond that, the monolithic server becomes a bottleneck.

Kubernetes Ecosystem Integration

Jaeger benefits from CNCF graduated project status. The Jaeger Operator simplifies deployment:

kubectl create namespace observability
kubectl apply -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.62.0/jaeger-operator.yaml -n observability

Zipkin requires manual configuration of Deployments, Services, and ConfigMaps. The lack of an official operator increases operational burden.

To master essential kubectl commands, consult our kubectl cheatsheet for debugging.

OpenTelemetry Compatibility

OpenTelemetry has become the de facto standard for instrumentation. Jaeger natively supports the OTLP (OpenTelemetry Protocol). Zipkin requires the Zipkin Exporter, adding a configuration layer.

# OpenTelemetry Collector configuration for Jaeger
exporters:
otlp:
endpoint: jaeger-collector:4317
tls:
insecure: true

# Configuration for Zipkin
exporters:
zipkin:
endpoint: http://zipkin:9411/api/v2/spans

Detailed Comparison Table Jaeger vs Zipkin

Remember: Jaeger dominates on advanced features (SPM, trace comparison, adaptive sampling). Zipkin remains relevant for teams valuing simplicity.

How to Deploy Jaeger on Kubernetes?

Step 1: Install the Jaeger Operator

kubectl create namespace observability
kubectl apply -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.62.0/jaeger-operator.yaml -n observability

Step 2: Deploy a production-ready Jaeger instance

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
name: jaeger-prod
namespace: observability
spec:
strategy: production
storage:
type: elasticsearch
elasticsearch:
nodeCount: 3
resources:
requests:
memory: 4Gi
cpu: 1

Step 3: Configure sidecar injection

apiVersion: apps/v1
kind: Deployment
metadata:
annotations:
sidecar.jaegertracing.io/inject: "true"

The Kubernetes production monitoring architecture details complete integration with Prometheus and Grafana.

How to Deploy Zipkin on Kubernetes?

Step 1: Create the Zipkin Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
name: zipkin
namespace: observability
spec:
replicas: 2
selector:
matchLabels:
app: zipkin
template:
metadata:
labels:
app: zipkin
spec:
containers:
- name: zipkin
image: openzipkin/zipkin:3.4
ports:
- containerPort: 9411
env:
- name: STORAGE_TYPE
value: elasticsearch
- name: ES_HOSTS
value: http://elasticsearch:9200

Step 2: Expose via a Service

apiVersion: v1
kind: Service
metadata:
name: zipkin
spec:
selector:
app: zipkin
ports:
- port: 9411
targetPort: 9411

Which Tool to Choose Based on Your Context?

Choose Jaeger if:

• You manage more than 5 Kubernetes clusters
• You process more than 100,000 spans/second
• You already use CNCF projects (Prometheus, Fluentd)
• You need trace comparison for debugging
• Your team includes a Backend developer following the LFD459 Kubernetes for Application Developers training

Choose Zipkin if:

• Your infrastructure has fewer than 50 services
• Operational simplicity outweighs features
• You have a small team without advanced Kubernetes expertise
• Trace volume remains moderate

Integration with the Kubernetes Observability Stack

Tracing doesn't work in isolation. Integration with Prometheus metrics and logs improves diagnosis. Our Prometheus vs Datadog comparison helps choose the metrics stack.

Prometheus and Grafana reach 75% adoption for Kubernetes monitoring (Grafana Labs). Jaeger integrates natively with Grafana via the Tempo/Jaeger datasource.

Also consult our essential Kubernetes metrics to complete your observability.

Toward Unified Observability

The current trend converges toward OpenTelemetry as the single instrumentation standard. Jaeger and Zipkin both support OpenTelemetry, but Jaeger benefits from more native integration.

For teams starting on Kubernetes, the Monitoring and Troubleshooting Kubernetes hub offers a complete path.

System administrators can go deeper with the LFS458 Kubernetes Administration training.

Take Action: Master Kubernetes Distributed Tracing

Distributed tracing on Kubernetes requires practical skills. SFEIR offers certification training to master observability:

• LFD459 Kubernetes for Application Developers: 3 days to master cloud-native development
• LFS458 Kubernetes Administration: 4 days to administer production clusters
• Kubernetes Fundamentals: 1 day to discover container orchestration. To go deeper, consult our Kubernetes Deployment and Production training.

Configure your first tracing pipeline today and transform your microservices visibility. Contact our advisors to define your training path.