Single service comparison

CCE is the closest AKS analogue: a CNCF-certified, community-version-compatible managed Kubernetes with a managed control plane and customer-owned nodes (ECS/BMS), offered in Standard, Turbo (flattened VPC/container network, Kata isolation), and Autopilot (serverless, fully hosted nodes) variants. AKS exposes a single free control-plane SKU plus optional SLA and policy tiers, whereas CCE splits the model across cluster tiers with materially different networking, isolation, and node-management boundaries. Both stay current with upstream Kubernetes, but CCE's Turbo/Autopilot flattening and Volcano scheduler have no exact AKS equivalent. CCI is a separate serverless container runtime (no cluster to own) for burst-to-second workloads, not a managed Kubernetes.

APIs and data model diverge meaningfully despite shared Kubernetes compatibility. AKS relies on Azure Resource Manager for cluster, node pool, and identity resources and defaults to Azure CNI with Azure AD-backed RBAC; CCE is managed through Huawei cloud APIs/IAM with VPC-overlay or flattened ENI networking, Kubernetes RBAC, and Huawei-specific add-ons (AOM, SWR, ELB, Volcano). Cluster size quotas differ: CCE Standard/Turbo cap at 50/200/1000/2000 nodes per tier, while AKS node-pool limits and upgrade channels differ. Neither provider guarantees feature-for-feature parity for operators, ingress controllers, autoscaler behavior, or storage classes, so each manifest and Helm chart must be re-validated on the target.

Operational responsibility and integration surface differ. On AKS, monitoring, registry, ingress, and secrets tie into Azure Monitor, ACR, Key Vault, and Azure Load Balancer, with Microsoft patching the control plane and you patching node images. On CCE you own node OS patching and lifecycle, integrate with SWR for images, ELB for load balancing, AOM/APM for observability, and IAM for identity, and you choose scheduling and scaling policies via Volcano or cluster autoscaler. CCI shifts more responsibility to Huawei (no node management) but narrows what you control, making it a complement for burst capacity rather than a wholesale AKS replacement.

Assess workload shape first to pick the right target: stateful long-running services and full-cluster control map to CCE (Standard or Turbo); serverless, per-second-billed burst pods map to CCI; many estates compose both, using CCE for steady load and CCI for spikes. Inventory AKS node pools, OS images, storage classes, ingress, autoscaler policies, network plugins, Azure AD bindings, and managed identities, then validate Kubernetes version and add-on parity against the target CCE tier and region before any move. Do not assume a one-click upgrade path exists.

For application and config migration, render manifests/Helm charts from AKS and retarget image references to SWR, replacing Azure Container Registry references. Re-express Azure-specific integrations: Azure Key Vault to Huawei secrets or KMS, Azure Monitor to AOM/APM, Azure Load Balancer/AGIC to ELB, and Azure AD workload identity to Huawei IAM. Migrate stateful data (disks via EVS, file via SFS, object via OBS) with matching access modes; retest storage classes and PVC behavior because performance characteristics are provider-specific and not guaranteed equivalent.

Validate and cut over in waves: stand up a parallel CCE cluster, run canary workloads, and compare autoscaler, scheduling, network, and observability behavior under representative load. Use the documented practice of migrating Kubernetes workloads to CCE as the reference path, falling back to manual manifest redeploy where automated tooling expectations don't match. Cut over traffic via ELB/ DNS after smoke and load validation, keeping the AKS cluster as rollback until stability is confirmed.

Account for cost-model and governance shifts. AKS bills a free control plane plus node compute, storage, network, and optional SLA/policy add-ons; CCE bills underlying ECS/BMS/EVS, bandwidth or traffic, and cluster add-ons, while CCI bills per-second by pod specification. Recompute TCO using peak and steady load, request volume, retention, and cross-region/interconnect traffic, and re-baseline quotas, region availability, and SLA terms, since managed-feature tiers and add-on coverage are not identical across providers.