Run Argo workflows serverlessly on Alibaba Cloud Container Service (ACS) and Elastic Container Instance (ECI). The service optimizes the open-source engine and Kubernetes parameters for efficient elastic scheduling at scale, and supports cost optimization through BestEffort and preemptible instances. This topic introduces the workflow cluster and its key advantages.
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Use cases
Argo Workflows is a Cloud Native Computing Foundation (CNCF) graduated project — the highest CNCF maturity tier, signaling broad adoption, security, and ecosystem reach — and the most popular workflow engine for Kubernetes, deployed across autonomous driving, scientific computing, quantitative finance, and digital media.
Workflow clusters are a strong fit for:
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Batch data processing — launch thousands of parallel tasks without pre-allocating compute
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Machine learning pipelines — orchestrate training, evaluation, and deployment as pod workflows
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Infrastructure automation — coordinate multi-step provisioning and teardown jobs reliably
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CI/CD pipelines — build high-throughput pipelines that scale out on demand and release resources when done
Argo Workflows excels at batch task orchestration:
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Cloud-native — designed for Kubernetes, where each task runs as a pod
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Lightweight and scalable — no VM overhead; launches thousands of tasks in parallel with elastic scaling
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Powerful orchestration — supports regular jobs, Spark jobs, Ray jobs, and Tensor jobs in a single workflow
Why use workflow clusters
Workflow clusters are built on open-source Argo Workflows with full API compatibility—existing workflows from ACK or any Kubernetes cluster migrate without modification.
Beyond compatibility, workflow clusters eliminate operational overhead at scale:
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No infrastructure management — clusters are ready immediately with automatic version upgrades.
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Elastic scaling with automatic resource release — resources scale out during execution and release after completion. Pay only for actual compute time.
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Multi-zone reliability — the engine schedules pods across availability zones, keeping workflows running during zone failures.
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Optimized control plane — tuned for performance, efficiency, stability, and observability at scale, ensuring consistent scheduling under heavy load.
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Enhanced OSS artifact management — upload large artifacts, stream data between steps, and configure automatic artifact garbage collection (GC) without manual bucket policy management.
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Community technical support — get expert guidance to optimize pipeline performance and costs.
Architecture
Workflow clusters are serverless workflow engines built on Kubernetes and powered by open-source Argo Workflows.
Network design
Workflow clusters are available in these regions: China (Beijing), China (Hangzhou), China (Shanghai), China (Shenzhen), China (Zhangjiakou), China (Heyuan), China (Guangzhou), China (Hong Kong), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Japan (Tokyo), Germany (Frankfurt), UK (London), and Thailand (Bangkok). For other regions, join DingTalk group 35688562 for expert support.
Before creating a workflow engine, plan your Virtual Private Cloud (VPC) and vSwitch configuration:
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Create a VPC or select an existing one.
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Create vSwitches or select existing ones, following these guidelines:
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Ensure your vSwitch CIDR blocks provide enough IP addresses. Argo workflows may create many pods, each requiring an IP address.
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Create a vSwitch in each availability zone, then specify all vSwitch IDs when creating the workflow engine. The engine schedules ACS pods or elastic container instances in zones with available stock. If all zones are out of stock, workflows cannot run.
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