Reactive Scaling vs Predictive 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 meets developers should learn and use predictive scaling when managing applications with predictable, cyclical workloads (e. Here's our take.

Reactive Scaling

Nice Pick

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

Predictive Scaling

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

The Verdict

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

Use Predictive Scaling if: You prioritize g over what Reactive Scaling offers.

The Bottom Line

Reactive Scaling wins

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