$ k8scalc compare temporal argo workflows

Temporal vs Argo Workflows

Temporal vs Argo Workflows for Kubernetes workflow orchestration — compare execution model, persistence, language support, long-running workflows, and operational requirements.

FeatureTemporalArgo Workflows

Execution model

Code-based (SDK)—

YAML DAG (containers)—

Languages supported

Go/Python/TS/Java/PHP

Any (container-based)

Persistence layer

PostgreSQL/Cassandra

Kubernetes etcd

Long-running workflows

Native (years)

Limited (pod TTL)

Automatic retries

Built-in with backoff

retryStrategy in YAML

Web UI

Basic

Good (Argo UI)

GitOps compatible

Yes (YAML in Git)

Kubernetes native

No (separate service)

Yes

Saga / compensation

Yes (native patterns)

Manual

Best for

Business logic orchestration—

Data pipelines / CI/CD—

Verdict

Temporal and Argo Workflows are designed for fundamentally different use cases. Temporal is a durable execution engine for code-first workflows — your business logic runs as code (Go, Python, TypeScript) with automatic retry, state persistence, and saga patterns. Argo Workflows is a YAML-defined DAG runner for container-based pipelines — CI/CD, ML training, data processing. For business logic orchestration (order processing, onboarding flows, multi-step transactions), Temporal. For data pipelines, CI/CD, and container-based batch processing, Argo Workflows.

Related Calculators

Temporal Resources K8s Cluster Cost