Tekton vs Argo Workflows — Which Kubernetes CI/CD Engine to Choose? (2026)
Tekton and Argo Workflows both run pipelines on Kubernetes, but they're built for different jobs. Full comparison with real examples and a clear recommendation.
Both Tekton and Argo Workflows run pipelines natively on Kubernetes. Both use CRDs. Both are CNCF projects. So why do they exist separately, and which should you use?
The short answer: they solve different problems. Let me explain.
What Each Tool Is Designed For
Tekton is a CI/CD framework. It's built specifically for building, testing, and deploying software. Designed to replace Jenkins pipelines.
Argo Workflows is a general-purpose workflow engine. It's excellent for CI/CD but also used for data pipelines, ML training jobs, batch processing, and any DAG-based automation.
Architecture Comparison
Tekton
Tekton has four core concepts:
- Task — a list of steps (each step is a container)
- Pipeline — a sequence of Tasks
- TaskRun — one execution of a Task
- PipelineRun — one execution of a Pipeline
# Tekton Task
apiVersion: tekton.dev/v1
kind: Task
metadata:
name: build-image
spec:
params:
- name: image
type: string
steps:
- name: build
image: gcr.io/kaniko-project/executor:latest
args:
- "--dockerfile=Dockerfile"
- "--destination=$(params.image)"
- "--context=dir://workspace/source"# Tekton Pipeline
apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
name: build-and-deploy
spec:
tasks:
- name: clone
taskRef:
name: git-clone
- name: build
taskRef:
name: build-image
runAfter: [clone]
- name: deploy
taskRef:
name: kubectl-apply
runAfter: [build]Argo Workflows
Argo uses a single Workflow CRD with templates:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: build-and-deploy-
spec:
entrypoint: pipeline
templates:
- name: pipeline
dag:
tasks:
- name: clone
template: git-clone
- name: build
template: build-image
dependencies: [clone]
- name: test
template: run-tests
dependencies: [clone]
- name: deploy
template: kubectl-deploy
dependencies: [build, test] # Both build AND test must pass
- name: git-clone
container:
image: alpine/git
command: [git, clone, "{{workflow.parameters.repo}}", /workspace]
- name: build-image
container:
image: gcr.io/kaniko-project/executor:latest
args: ["--dockerfile=Dockerfile", "--destination={{workflow.parameters.image}}"]Notice: Argo supports true DAGs — deploy waits for both build AND test in parallel. Tekton's runAfter is simpler (linear dependencies only without extra config).
Side-by-Side Comparison
| Tekton | Argo Workflows | |
|---|---|---|
| Primary use case | CI/CD | General workflows + CI/CD |
| DAG support | Basic (runAfter) | Native DAG |
| Parallel execution | Yes | Yes |
| Reusable steps | Catalog (Tekton Hub) | Inline templates |
| UI | Tekton Dashboard (basic) | Argo UI (excellent) |
| Triggers | Tekton Triggers (complex) | Argo Events (cleaner) |
| Learning curve | Medium | Medium |
| CNCF status | Graduated | Graduated |
| Ecosystem | GitHub Actions-like | Broader (ML, data pipelines) |
| Retry logic | Per-step | Per-step + backoff |
| Artifact passing | Workspace volumes | Artifacts S3/GCS native |
Tekton Strengths
1. Catalog of Reusable Tasks
Tekton Hub has hundreds of pre-built tasks: git-clone, kaniko, helm-upgrade, kubectl, Sonar, Trivy.
# Install from Tekton Hub
tkn hub install task git-clone
tkn hub install task buildah
tkn hub install task helm-upgrade-from-repo2. Native CI/CD Concepts
Tekton maps naturally to CI/CD thinking: clone → build → test → deploy. If your team thinks in CI/CD stages, Tekton's model is intuitive.
3. Tekton Triggers
Tekton Triggers can start pipelines from GitHub webhooks, Slack events, or any HTTP source:
apiVersion: triggers.tekton.dev/v1beta1
kind: EventListener
metadata:
name: github-listener
spec:
triggers:
- name: github-push
interceptors:
- ref:
name: github
params:
- name: eventTypes
value: [push]
bindings:
- ref: github-binding
template:
ref: build-pipeline-templateArgo Workflows Strengths
1. Powerful DAG Support
Argo's DAG model is its biggest advantage. You can define complex dependency graphs:
dag:
tasks:
- name: unit-tests
template: test
- name: integration-tests
template: test
dependencies: [unit-tests]
- name: security-scan
template: trivy-scan
- name: build
template: build-image
dependencies: [unit-tests] # Don't build if unit tests fail
- name: deploy-staging
template: deploy
dependencies: [build, security-scan]
- name: smoke-test
template: test-endpoint
dependencies: [deploy-staging]
- name: deploy-prod
template: deploy
dependencies: [smoke-test, integration-tests]2. Artifact Management
Argo has native S3/GCS artifact support. Pass outputs from one step to the next:
- name: generate-report
container:
command: [python, generate.py]
outputs:
artifacts:
- name: report
path: /output/report.json
s3:
bucket: my-artifacts
key: reports/{{workflow.name}}/report.json
- name: send-report
dependencies: [generate-report]
inputs:
artifacts:
- name: report
from: "{{tasks.generate-report.outputs.artifacts.report}}"3. The UI is Exceptional
Argo Workflows has one of the best pipeline UIs in the Kubernetes ecosystem — real-time DAG visualization, log streaming, artifact browsing. Tekton's dashboard is functional but basic.
When to Choose Tekton
- You want a pure CI/CD tool with a strong community catalog
- Your team is coming from Jenkins and wants familiar CI/CD concepts
- You're using Tekton Chains for software supply chain security (SLSA)
- You're building a PaaS where tenants bring their own pipelines
When to Choose Argo Workflows
- You need complex DAGs with fan-out/fan-in patterns
- You're doing ML training pipelines, data processing, or batch jobs alongside CI/CD
- You want excellent native UI without extra setup
- You're already using ArgoCD (same ecosystem, same Argo team)
- You need artifact passing between steps
The Recommendation
For pure CI/CD: Either works. If already in the Argo ecosystem (ArgoCD), use Argo Workflows — better UI, better DAG support, same team manages it.
For ML/data pipelines + CI/CD: Argo Workflows — it handles both.
For enterprise PaaS building: Tekton — better extensibility for multi-tenant scenarios.
Most new Kubernetes-native CI/CD setups in 2026 use Argo Workflows simply because the UX is better and the DAG model scales to complex use cases.
For hands-on labs with both Tekton and Argo Workflows on real Kubernetes clusters, KodeKloud has CI/CD courses covering both tools.
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