A production-grade AWS cloud infrastructure diagram. CloudFront, ECS, RDS, Lambda, SQS — the full stack, ready to customize with AI.
Copy this Mermaid code into Cybewave Studio or any Mermaid-compatible editor.
graph TD
Users[Users] --> CF[CloudFront CDN]
CF --> ALB[Application Load Balancer]
ALB --> ECS[ECS / EKS Cluster]
subgraph VPC
ECS --> AppSvc[App Service]
ECS --> WorkerSvc[Worker Service]
AppSvc --> RDS[(RDS PostgreSQL)]
AppSvc --> EC[(ElastiCache Redis)]
AppSvc --> S3[(S3 Bucket)]
WorkerSvc --> SQS[SQS Queue]
SQS --> Lambda[Lambda Functions]
Lambda --> DDB[(DynamoDB)]
end
AppSvc --> SES[SES Email]
AppSvc --> SNS[SNS Notifications]
Lambda --> CW[CloudWatch Logs]
RDS --> RDSReplica[(RDS Read Replica)]
S3 --> Glacier[(S3 Glacier Archive)]A battle-tested AWS production stack you can adapt in minutes.
CloudFront CDN, ALB, ECS containers, RDS with read replicas — a proven AWS production topology.
SQS + Lambda for async workloads alongside containerized services for maximum flexibility.
ElastiCache Redis for session storage, query caching, and rate limiting.
S3 lifecycle rules to Glacier, read replicas for query offloading, and spot instances for workers.
CloudFront (CDN), Application Load Balancer, ECS/EKS containers, RDS PostgreSQL with read replicas, ElastiCache Redis, S3 with Glacier archiving, SQS queues, Lambda functions, DynamoDB, SES email, SNS notifications, and CloudWatch.
Yes. Open the template in Cybewave Studio and ask the AI to adapt it for GCP (Cloud Run, Cloud SQL) or Azure (AKS, Azure SQL). The AI remaps equivalent services.
Absolutely. This template covers a scalable production architecture. You can simplify it by removing read replicas or Glacier for early-stage and ask the AI to adjust.
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