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Building a Serverless Microservices Production Stack on AWS

How to replace a classic microservices stack with fully managed AWS serverless services.

Author

Houssem BEN SLAMA
August 21, 2025

Introduction

Traditionally, deploying microservices into production meant setting up Kubernetes clusters, running Consul or Eureka for service discovery, wiring up ELK for logging, and managing Prometheus + Grafana for monitoring. Powerful, yes — but also heavy and complex.

With AWS serverless services, you can build a production-ready microservices stack without running a single server. Every piece of the puzzle — API routing, service discovery, caching, messaging, observability — exists as a fully managed building block.

The result? An architecture that’s scalable, secure, cost-efficient, and lightweight to operate. Let’s walk through how each part of a traditional microservices stack maps to AWS serverless.

1. API Gateway – The Single Entry Point

AWS Service: Amazon API Gateway

All client requests flow through API Gateway. It routes traffic, applies rate limiting, transforms requests, and integrates seamlessly with Lambda. It replaces load balancers and ingress controllers while supporting REST, WebSocket, and even GraphQL (via AppSync).

2. Service Registry – Discoverability

AWS Service: AWS Cloud Map 

Instead of running Consul or Eureka, AWS Cloud Map provides dynamic service discovery. Microservices register themselves and can be discovered by name or tags. API Gateway and Lambdas can query Cloud Map directly.

3. Service Layer – Business Logic

AWS Service: AWS Lambda (with Step Functions for orchestration)

Each microservice is just a set of Lambda functions. They scale automatically, are deployed independently, and you pay only per execution. For multi-step workflows, AWS Step Functions orchestrate Lambdas reliably.

4. Authorization Server – Security Layer

AWS Service: Amazon Cognito

Cognito replaces custom OAuth servers. It handles sign-up, sign-in, identity federation, and issues JWT tokens. API Gateway validates tokens before invoking Lambdas, offloading authentication logic from microservices.

5. Data Storage – Service-Owned Databases

AWS Services:

Each microservice owns its data store, keeping with the principle of database per service.

6. Distributed Caching – Performance First

AWS Service: Amazon ElastiCache for Redis (or DynamoDB Accelerator – DAX for DynamoDB-specific caching).

Caching reduces database load and accelerates reads. ElastiCache integrates well with serverless apps and supports pub/sub patterns for real-time updates.

7. Async Communication – Decoupling Services

AWS Services:

This trio covers all asynchronous messaging needs: point-to-point, broadcast, and cross-service event buses.

8. Metrics Visualization – Observability

AWS Services:

Together, they provide real-time monitoring, alerting, and visualization — all without running custom monitoring stacks.

9. Log Aggregation – Centralized Logging

AWS Services:

Logs flow from API Gateway, Lambda, and other services into CloudWatch, and can be streamed to OpenSearch for visualization and troubleshooting.

Conclusion

A serverless production stack on AWS delivers all the core features of a traditional microservices platform — but without the overhead of running servers or clusters.

This isn’t just swapping tools. It’s a shift in philosophy:

  • Auto-scaling by default — no cluster capacity planning.

  • Pay-per-use pricing — cost tracks actual usage.

  • Built-in resilience — managed failover, retries, and high availability.

  • Faster delivery — teams focus on business logic instead of managing VMs or Kubernetes.

For teams, that means less time babysitting infrastructure and more time building features that matter. In short: serverless makes microservices finally live up to their promise.