Argo Workflows vs Dagu
Argo Workflows
argoproj.github.io
Dagu
dagu.shSide by side
| Figure | Argo Workflows | Dagu |
|---|---|---|
| Price from | Free | $50/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 4 |
| Spec score · Workload Automation Software | 8.6 (#5) | 8.3 (#11) |
| Deployment | Argo Workflows is cloud agnostic and can run on any Kubernetes cluster. | Dagu runs as a single binary locally or across distributed workers and does not require an external database or message broker. |
| Integrations | The project lists Argo Events, Hera, Katib, Kedro, Kubeflow Pipelines, Netflix Metaflow, Piper, Seldon, and SQLFlow among projects that use or rely on Argo Workflows. | Built-in integration actions include HTTP, mail, SSH, SFTP, Git, S3, Redis, LLM, browser, computer, SQL, PostgreSQL, SQLite, and DuckDB. |
| Purpose | Argo Workflows is an open-source, container-native engine for orchestrating parallel jobs on Kubernetes. | Dagu is a self-hosted workflow engine that lets users define DAG workflows in YAML and manage runs, logs, and retries through a built-in web UI. |
| Scheduling | Cron-based scheduled workflows are supported. | Dagu supports cron schedules with timezones, catchup windows, overlap policies, retry policies, zombie detection, lifecycle handlers, and notifications. |
| Support | The project directs users with questions to GitHub Discussions and Slack and describes monthly community meetings. | Team and Pro include standard support, Enterprise includes priority support, and support is handled directly by Dagu's maintainer. |
Plans
In their own words
Argo Workflows
Argo Workflows is a free, open-source engine for orchestrating container-based jobs on Kubernetes. Workflows can be arranged as sequential steps or as tasks linked in a directed acyclic graph, with each step running in a container. The project lists machine learning pipelines, data and batch processing, infrastructure automation, and CI/CD as use cases. Controls include loops, conditionals, retries, timeouts, suspension and resumption, cancellation, and limits on parallel work. Cron scheduling, workflow archiving, a UI for viewing and managing runs, and Prometheus metrics are included. Artifact support covers S3, Artifactory, Alibaba Cloud OSS, Azure Blob Storage, HTTP, Git, GCS, raw artifacts, and plugins. The server provides REST API support over HTTP and gRPC, with client libraries for Java, Go, Python, and TypeScript. Argo Workflows is self-hosted and can run on any Kubernetes cluster. Its listed plan costs nothing, and the official pages list no paid plans or usage limits. Administrators should configure transport security, authentication, roles, and permissions; workflow pods run as root by default unless their security context is set.
Dagu
Dagu is a self-hosted workflow engine for defining DAG workflows in YAML and managing runs, logs and retries through a built-in web interface. It runs as a single binary locally or across distributed workers, without an external database or message broker. Workflows can run shell scripts, Docker containers, SSH commands, SQL queries, HTTP requests, chat completion steps and Agent Harness steps. Scheduling supports cron timezones, catchup windows, overlap and retry policies, zombie detection, lifecycle handlers and notifications. Built-in actions cover HTTP, mail, SSH, SFTP, Git, S3, Redis, LLM, browser, computer, SQL, PostgreSQL, SQLite and DuckDB. Dagu also supports MCP connections from clients including Claude Code, Codex, Cursor, VS Code, Gemini CLI, OpenCode, Zed and Cline. The project is open source under GNU GPL v3.0. Its free Community plan includes unlimited servers and workers, with up to two API keys. Team is $50 a month and Pro is $150 a month; both add SSO, role-based access, audit logging, incident routing and standard support.