Tekton — cloud-native pipelines for building and testing AI artifacts.

TL;DR — Tekton is a Kubernetes-native CI/CD framework built from reusable pipeline resources and controllers. It's a good fit for AI platforms that need container builds, model packaging, eval jobs, and promotion workflows all in the same cluster.

What it is

Tekton is an open-source framework for building CI/CD pipelines on Kubernetes. It breaks workflows into Tasks and Pipelines and runs them as pods. In the AI Native landscape it sits in AI Native Infra › Continuous Integration and Delivery.

Why it exists

AI delivery is a chain of reproducible steps: train, validate, package, scan, evaluate, publish. Tekton keeps those steps declarative and cluster-native instead of in a separate CI server.

How it works

A PipelineRun instantiates a Pipeline composed of Tasks. Each Task runs in a pod, receives inputs via workspaces or params, and emits results for the next step. That maps cleanly to model builds and evaluation gates.

Key features

• Composable Tasks for reusable steps.
• PipelineRuns for execution.
• Kubernetes-native runtime.
• Good fit for build/test/eval/promote.

Quick start

apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
  name: model-pipeline
spec:
  tasks:
    - name: test
      taskRef:
        name: model-test

When to use, when to skip

Use it if you want pipeline steps to run in Kubernetes with full control over execution. Skip it if your org already has a CI platform that handles the same AI build and test flow.

vs / alongside

References

• Tekton — project home.
• Tekton docs — concepts and tasks.
• tektoncd/pipeline — source.

Verified against Tekton docs, May 2026.