Predictive Scaling vs Reactive Scaling

Developers should learn and use predictive scaling when managing applications with predictable, cyclical workloads (e meets developers should learn and use reactive scaling when building cloud-native applications, microservices, or distributed systems that experience unpredictable traffic patterns, such as e-commerce platforms, streaming services, or iot applications. Here's our take.

Predictive Scaling

Nice Pick

Developers should learn and use predictive scaling when managing applications with predictable, cyclical workloads (e

Pros

• g
• Related to: auto-scaling, cloud-computing

Cons

• Specific tradeoffs depend on your use case

Reactive Scaling

Developers should learn and use Reactive Scaling when building cloud-native applications, microservices, or distributed systems that experience unpredictable traffic patterns, such as e-commerce platforms, streaming services, or IoT applications

Pros

• It helps prevent over-provisioning of resources during low demand and avoids performance degradation during spikes, ensuring high availability and cost-effectiveness in environments like AWS, Azure, or Kubernetes
• Related to: reactive-programming, microservices-architecture

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Predictive Scaling if: You want g and can live with specific tradeoffs depend on your use case.

Use Reactive Scaling if: You prioritize it helps prevent over-provisioning of resources during low demand and avoids performance degradation during spikes, ensuring high availability and cost-effectiveness in environments like aws, azure, or kubernetes over what Predictive Scaling offers.

The Bottom Line

Predictive Scaling wins

Developers should learn and use predictive scaling when managing applications with predictable, cyclical workloads (e