Single service comparison

Cloud Deploy is tightly scoped to Kubernetes (GKE) and Cloud Run delivery, modeling releases as immutable rollouts that progress across declared targets via Skaffold render/deploy stages. Huawei CodeArts Pipeline is a broader orchestration layer for arbitrary CI/CD flows across build, test, and deploy tasks, while CodeArts Deploy provides host- and container-based deployment orchestration. The two Huawei services are not interchangeable: Pipeline drives the flow and gates; Deploy executes target-side deploy actions to CCE or hosts.

Cloud Deploy exposes a declarative delivery-pipeline and target resource model (gcloud, REST API, Terraform) with IAM-gated promotion, parallel multi-cluster rollout, and built-in canary verification tied to Cloud Logging/Monitoring. CodeArts Deploy uses environment and application/templates, deploy tasks, and approval nodes managed through the CodeArts console/API; CodeArts Pipeline uses customizable stages and extension plugins. Neither Huawei service offers native Skaffold-based rendering or Cloud Run equivalent targets, so render and Canary strategies must be reconstructed.

Operating responsibility differs: Cloud Deploy is serverless with no infrastructure to manage and scales automatically, charging a flat per-pipeline management fee plus underlying resource costs. CodeArts Pipeline and Deploy run as managed services but rely on execution resources (deploy hosts, CCE clusters, build agents) you configure and quota; pricing is per-user/per-resource and tied to CodeArts packaging. HA, regional rollout, and audit wiring must be assembled from CodeArts plus CCE, IAM, and LTS rather than inherited from a single CD control plane.

Assess each Cloud Deploy delivery pipeline and target: enumerate pipelines, targets, render strategies, approval gates, automations, and Skaffold/Helm/Kustomize manifests. Map to CodeArts Pipeline for orchestration and gates, and CodeArts Deploy for target execution to CCE (or hosts). Categorize GKE workloads to CCE clusters and Cloud Run workloads to CCE or FunctionGraph, since there is no direct Cloud Run equivalent; treat target model and deploy mechanism as a re-platform, not a lift-and-shift.

Rebuild pipelines as CodeArts Pipeline stages mirroring Cloud Deploy's promote/rollback flow, using CodeArts Build for Skaffold-equivalent build and artifact steps and pushing images to SWR. Recreate deploy templates in CodeArts Deploy for each target environment, wiring approvals, conditions, and notifications. Translate Skaffold/Helm rendering into deploy scripts or built-in deploy actions; recreating canary, parallel, and automated promotion behaviors will require custom logic as these are not feature-equivalent.

Validate control and data planes before cutover: run releases against non-production CCE targets, verify rollout/rollback semantics, RBAC and approvals, audit logs (CTS), and integration with monitoring (CES/AOM). Confirm that regional/multi-target rollouts are emulated through pipeline fan-out, since parallel deploy is not a native CodeArts Deploy primitive. Retain Cloud Deploy during a parallel-run period to compare release outcomes and audit traceability.

Address gaps and cost shifting: Cloud Deploy's per-pipeline billing differs from CodeArts user/package/SWR/CCE and deploy-host pricing, so re-estimate TCO including build minutes, storage, and cross-region traffic. Plan for missing Cloud Run targets, automated canary verification, and Terraform-managed delivery-pipeline resources by adding custom scripts and IaC. Document the reconstructed approval and promotion model so operators understand the change from Cloud Deploy's opinionated single-pane-of-glass experience.