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ApsaraDB RDS:Overview

Last Updated:Mar 29, 2023

This topic describes the features, architecture, and benefits of serverless ApsaraDB RDS for SQL Server instances. This topic also provides a list of common scenarios and common operations on the serverless RDS instances.

Official launch

Serverless RDS instances are available for commercial use from March 20, 2023. You can create a serverless RDS instance in the ApsaraDB RDS console. The following list provides more information about serverless RDS instances:

  • Serverless RDS instances are available only in the Singapore region.

  • You can create a serverless RDS instance that runs SQL Server 2016 SE, SQL Server 2017 SE, or SQL Server 2019 SE on RDS High-availability Edition.

  • If you use a RAM user to create a serverless RDS instance, the RAM user must have the AliyunRDSFullAccess permission. For more information, see Use RAM for resource authorization.

  • Service level agreement (SLA) commitments are not applicable to your serverless RDS instance and to the availability and liability of any relevant services. Alibaba Cloud shall not be liable for the adverse impact of your use of serverless RDS instances and relevant services.


Serverless RDS instances allow you to quickly and independently scale computing resources to adapt to fluctuating workloads. These benefits simplify rightsizing your RDS instances, helping you reduce costs and improve efficiency.

The following figure compares the rate of resource utilization between a regular RDS instance and a serverless RDS instance with fluctuating workloads.


Referring to the preceding figure, we can obtain the following conclusions:

  • Regular RDS instance: Low resource utilization during off-peak hours translate into wasted costs, while insufficient resources during peak hours affect service performance.

  • Serverless RDS instance:

    • Resources are scaled in response to changes in the workload. This minimizes the amount of idle resources and maintains the resource utilization rate.

    • Resources are scaled to match the workload requirements during peak hours, which ensures performance and service stability.

    • You are charged based on the actual volume of resources that are used to run your workloads. This significantly reduces costs.

    • No human intervention is required. This improves O&M efficiency and reduces costs for O&M administrator and developers.

    • The serverless RDS instance supports auto scaling and is optimized for high-throughput write operations and high-concurrency processing operations. This is suitable for scenarios in which large amounts of data and large traffic fluctuations are involved.




  • Low cost: Serverless RDS instances do not rely on other infrastructure and services and can provide stable and efficient data storage and access services. This deployment model is ideal for startups and scenarios that want to run workloads immediately after resources are created. You are charged only for the resources that you use based on the pay-as-you-go billing method.

  • Auto scaling of computing resources: The computing resources that are required for read and write operations can be automatically scaled without human intervention. This greatly reduces O&M costs and risks of human errors.

  • Fully managed and maintenance-free services: Serverless RDS instances are fully managed by Alibaba Cloud, allowing you to focus on developing your application instead of O&M operations, such as system deployment, scaling, and alert handling. The O&M operations are performed in the background and are transparent at the service layer.

  • High availability: The serverless RDS instance runs in high-availability mode.

Billing rules

For more information, see Pricing of a serverless ApsaraDB RDS for SQL Server instance.


  • Scenarios in which off-peak hours are significant, such as development and testing environments

  • Software as a service (SaaS) scenarios, such as website building of small and medium-sized enterprises

  • Educational scenarios, such as teaching and student experiments

  • Scenarios that handle inconsistent and unpredictable workloads, such as IoT and edge computing

  • Scenarios in which services are changing or unpredictable

  • Scenarios in which intermittent scheduled tasks are involved

  • Individual developers