Argo Workflows vs Azkaban
Argo Workflows
argoproj.github.ioAzkaban
azkaban.github.ioSide by side
| Figure | Argo Workflows | Azkaban |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Workload Automation Software | 8.6 (#5) | 7.6 (#17) |
| Spec score · Workflow Orchestration Software | 8.6 (#9) | 7.7 (#26) |
| Deployment | Argo Workflows is cloud agnostic and can run on any Kubernetes cluster. | The documentation provides instructions for setting up Solo Server and Multi Executor Server deployments. |
| 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. | Documented job-type plugin examples include Hadoop ecosystem components, Pig, Hive, and VoldemortBuildAndPush. |
| Purpose | Argo Workflows is an open-source, container-native engine for orchestrating parallel jobs on Kubernetes. | Azkaban is a distributed workflow manager created at LinkedIn to manage Hadoop job dependencies and run jobs in sequence. |
| Scheduling | Cron-based scheduled workflows are supported. | Users can schedule workflows, and flows can also be launched through the Ajax API. |
| Support | The project directs users with questions to GitHub Discussions and Slack and describes monthly community meetings. | The project directs users to its Google Group for help and lists Gitter and mailing lists as community channels. |
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.