Cloud-Native and Serverless design

July 15, 2025

How to design services that scale predictably on AWS, Azure, or GCP with sensible tradeoffs.

Cloud-native is about operating model

“Cloud-native” usually means you can:

• Deploy frequently without fragile runbooks
• Scale horizontally when traffic changes
• Recover quickly from failures

It’s less about which vendor you pick and more about how you build and run software.

Serverless fits spiky, event-driven workloads

Serverless can be a great default when:

• Work arrives as events (webhooks, queue messages, schedules)
• You want minimal infrastructure management
• You can tolerate cold starts or mitigate them

Common pitfalls are hidden coupling and difficult local debugging. Offset that with good tracing and small functions.

Example (make operational constraints explicit in IaC):

resource "aws_lambda_function" "handler" {
  function_name = "api-handler"
  timeout       = 10
  memory_size   = 512
}

Design for failure, not perfection

Across AWS/Azure/GCP, the same resilience patterns show up:

• Timeouts everywhere (client, server, downstream calls)
• Retries with jitter (but only when operations are safe to retry)
• Circuit breakers and bulkheads for noisy dependencies
• Dead-letter queues for poisoned messages

Treat IAM as part of your application

Security and operability depend on identity and permissions:

• Use least-privilege roles per service
• Prefer short-lived credentials (workload identity)
• Keep secrets in a managed store, not environment files

Optimize cost like performance

Cloud bills are feedback. Make them actionable:

• Attribute cost by service/team (tags/labels)
• Watch top drivers (egress, logs, idle databases)
• Right-size and autoscale with clear SLOs

References

• AWS Well-Architected Framework
• Azure Well-Architected Framework
• Google Cloud Architecture Framework