Container Service for Kubernetes (ACK) offers multiple cluster types with distinct features, O&M requirements, and compensation standards to meet your business needs. This topic provides a comparison to help you choose the cluster type that best fits your business needs.
Cluster types
Based on whether the cluster control plane is managed, ACK supports two types of clusters:
ACK managed clusters: Alibaba Cloud fully hosts and maintains the control plane of the managed clusters. The managed version is available in two editions: ACK managed Pro clusters and ACK managed Basic clusters, which differ in control plane availability assurance and advanced custom features.
ACK dedicated clusters: You are responsible for creating and maintaining the control plane of the dedicated clusters.
ImportantThe option of creating new ACK dedicated clusters is no longer available. For more information, see [Product announcement] Creation of new ACK dedicated clusters discontinued.
The following table describes the differences among the cluster types:
Item | ACK managed cluster | ACK dedicated cluster | ||
ACK managed Pro cluster | ACK managed Basic cluster | |||
Cluster size | Each account can manage up to 100 clusters. By default, each cluster can support a maximum of 5,000 worker nodes. To increase this limit, you can request a quota increase in the quota center. | Each account can manage up to two clusters. By default, each cluster can support a maximum of 10 worker nodes. Quota increases are not available. | Each account can manage up to 100 clusters. By default, each cluster can support a maximum of 5,000 worker nodes. To increase this limit, you can request a quota increase in the quota center. | |
Management scope | The auto mode is supported.
| The cluster control plane is fully managed, and you are responsible for maintaining worker nodes. | You are responsible for maintaining both the master and worker nodes, because the control plane is not managed by Alibaba Cloud. | |
Scenarios |
| Limited cluster size, such as personal learning and testing |
| |
Billing methods | You are charged for cluster management based on the number of clusters. You are also charged for Alibaba Cloud services used by worker nodes and some components, such as Simple Log Service (SLS). | Cluster management is free of charge. However, you are charged for Alibaba Cloud services used by worker nodes and some components, such as SLS. | Cluster management is free of charge. However, you are charged for Alibaba Cloud services used by control planes, worker nodes, and some components, such as SLS. | |
SLA | Region-level clusters guarantee a Service-Level Agreement (SLA) for service availability of 99.95%, while zone-level clusters offer a 99.5% SLA. For more information, see Container Service for Kubernetes Service Level Agreement. | No SLA is provided. |
Advantages of ACK managed Pro clusters
The following table compares the capabilities of ACK managed Pro clusters and ACK managed Basic clusters.
The following table uses icons to indicate feature support: indicates supported features, while
indicates features that are not supported.
Feature | ACK managed Pro cluster | ACK managed Basic cluster |
High-frequency cold and hot backups, and geo-disaster recovery of etcd | ||
Support of encrypting Secrets with KMS | ||
Hot migration
Both ACK managed Basic clusters and ACK dedicated clusters support hot migration to ACK managed Pro clusters. For more information, see the following topics:
Auto mode
When creating an ACK managed cluster, you can enable auto mode. This mode allows rapid deployment of Kubernetes clusters compliant with industry best practices through minimal network configuration. Key features include:
Fully managed O&M: The cluster control plane and critical components are fully managed by ACK. A node pool with auto mode enabled is created by default. This node pool dynamically scales the resources based on workloads. ACK is responsible for O&M tasks such as operating system and software upgrades, and vulnerability patches.
Intelligent resource provisioning: This feature automatically recommends the optimal instance specifications, eliminating manual configuration.
Optimized software stack: This feature enhances security protection by using an immutable ContainerOS root filesystem. With minimalized system configuration and tuned kernel parameters, node startup process and hardware utilization are optimized.
We recommend enabling auto mode in the following scenarios:
Dynamic resource scheduling: In environments with fluctuating workload demands, the auto mode rapidly scales computing resources, reducing cluster resource costs.
DevOps and CI/CD pipelines: In continuous integration and continuous deployment (CI /CD) environments, the auto mode automatically adjusts resources based on build and testing requirements, cutting idle resource costs while improving development efficiency.
The auto mode adopts the design concepts of elastic capacity, immutable infrastructure, and maintenance-free operation. For business scenarios that depend on node environment customization and node-local persistent storage, we recommend that you conduct a comprehensive application assessment before migration to identify potential compatibility risk points.
The auto mode aims to provide automated and intelligent management capabilities for Kubernetes clusters. In certain scenarios, you may still need to fulfill some responsibilities. For more information, see Shared responsibility model.
Features
Feature | Description |
Cluster management |
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Nodes and node pools | You can manage the lifecycle of node pools. You can configure different specifications for node pools in a cluster, such as vSwitches, container runtimes, operating systems, and security groups. For more information, see Node and Node pools. |
Application management |
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Storage |
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Network |
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Auto scaling | Automatically scale computing resources to meet business requirements and reduce costs:
For more information, see Auto Scaling Overview. |
Scheduling | ACK provides various scheduling policies that target different types of workloads, such as job scheduling, QoS-aware scheduling, and descheduling. These scheduling policies can improve application performance and resource utilization. For more information, see Scheduling. |
O&M and security |
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Heterogeneous resources |
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Developer Tools |