Which practice aligns with the Cloud Native principle of Speed, Efficiency and Cost Saving?

Which practice aligns with the Cloud Native principle of Speed, Efficiency and Cost Saving?

• A. Using autoscaling to scale based on workload, leveraging serverless when appropriate, and implementing proactive cost-saving measures
• B. Maintaining fixed resource allocation regardless of demand
• C. Eliminating automation to reduce upfront costs
• D. Relying solely on manual interventions for scaling decisions

Answer: (A)

Dynamic resource management drives speed and cost efficiency in Cloud Native environments. Autoscaling lets applications respond automatically to changing demand, adding or removing capacity as needed to maintain performance without waste. Leveraging serverless where it fits removes server management overhead and charges you only for actual usage, which keeps costs aligned with demand and speeds up delivery. Proactive cost-saving measures, such as right-sizing resources, choosing appropriate instance types, using cheaper compute when possible, and cleaning up idle resources, further reduce waste while sustaining responsiveness. The other approaches hinge on static or manual processes, which slow down reactions to traffic spikes, lead to over- or under-provisioning, and waste resources. Fixed allocations don’t adapt to demand, automation removal stops optimization, and relying on manual scaling delays decisions and increases cost.

Dynamic resource management drives speed and cost efficiency in Cloud Native environments. Autoscaling lets applications respond automatically to changing demand, adding or removing capacity as needed to maintain performance without waste. Leveraging serverless where it fits removes server management overhead and charges you only for actual usage, which keeps costs aligned with demand and speeds up delivery. Proactive cost-saving measures, such as right-sizing resources, choosing appropriate instance types, using cheaper compute when possible, and cleaning up idle resources, further reduce waste while sustaining responsiveness.

The other approaches hinge on static or manual processes, which slow down reactions to traffic spikes, lead to over- or under-provisioning, and waste resources. Fixed allocations don’t adapt to demand, automation removal stops optimization, and relying on manual scaling delays decisions and increases cost.