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# Designing a Scalable, Secure & AI-Powered Property Management Platform on AWS

**[Medium](https://daily.dev/sources/medium_js)** · 5 min read · 0 upvotes · 0 comments

## Summary

A walkthrough of designing a cloud-native, AI-powered property management platform on AWS following the Well-Architected Framework. The architecture uses a four-layer approach: S3+CloudFront for the frontend, ECS Fargate microservices for the backend, RDS MySQL+ElastiCache for data, and SQS+Lambda+Bedrock for async AI workloads. Key decisions covered include choosing ECS Fargate over EKS for reduced operational complexity, a three-tier VPC design to minimize attack surface, edge security via WAF and Shield, auto-scaling strategies, and cost optimization using Fargate Spot and CloudFront caching. Terraform snippets illustrate S3 bucket setup, ECS task definitions, service configuration, and auto-scaling targets.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@saileshkhadka1/designing-a-scalable-secure-ai-powered-property-management-platform-on-aws-c26adc33ed93>

## Questions this post answers

### Should I use ECS Fargate or EKS for a microservices backend on AWS?

ECS Fargate is a reasonable choice when the priority is reducing operational complexity and avoiding Kubernetes cluster management. It removes the need to manage server patching, AMIs, and capacity planning, letting developers define only the Docker image, CPU, memory, networking, and scaling rules, while EKS remains a viable option when more Kubernetes-native control is required.

_Comparing container platforms for a new backend is easier with real-world tradeoffs surfaced on daily.dev._

### How can I reduce AWS costs for a containerized backend with AI workloads?

Cost reduction can come from running only the containers needed via ECS auto scaling, using Fargate Spot for AI workers and background processing, caching frequently accessed content with CloudFront and Redis to cut backend and database load, and applying S3 lifecycle policies to move older data to cheaper storage tiers.

_Engineers tuning cloud spend can track cost optimization patterns like these on daily.dev._

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---

Tags: [#aws](https://daily.dev/tags/aws), [#microservices](https://daily.dev/tags/microservices), [#terraform](https://daily.dev/tags/terraform), [#iac](https://daily.dev/tags/iac)

[View this post on daily.dev](https://daily.dev/posts/designing-a-scalable-secure-ai-powered-property-management-platform-on-aws-sdu6ieozs)

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