Client type
North American job search service / B2C service platform
Case Study / Anonymized client
Designing AWS deployment paths for a multi-workload service platform
Supporting stable AWS delivery for multiple frontend, backend, and automation workloads.
The point was not to stack AWS services. The point was to establish a clear, maintainable, and evolvable deployment decision system based on how each workload actually runs.
Overview
The platform needed a deployment decision system, not a one-off launch.
The platform had grown beyond a single web app. It now included static frontend pages, dynamic web experiences, backend APIs, and automation scripts. Whether a team uses React, Next.js, Django, or other frontend and backend languages and frameworks, each service has different runtime, caching, scaling, debugging, and operations needs.
Business model
Online service system with multiple application shapes
Region
United States market
Project type
AWS cloud deployment / decoupled frontend-backend architecture
Challenges
One deployment model cannot fit every service architecture.
The difficulty was not whether one service could go live. It was how different runtime shapes could get clear, maintainable, and scalable deployment paths.
01
Frontend services had multiple runtime shapes
SPAs built with React, Vue, Vite, and similar tools, plus SSG pages built with Next.js, Nuxt, and similar frameworks, can become static assets for CDN delivery. SSR applications need runtime infrastructure and server-side request handling. Treating both as the same deployment model adds cost or limits capability.
02
Static and dynamic websites needed different infrastructure
Static sites fit object storage and CDN delivery. Dynamic sites need a runtime for server rendering, request logic, and environment variables. CloudFront can be the unified entry point, but origins should differ.
03
Backend workloads had different complexity levels
Django, FastAPI, Express.js, NestJS, Gin, Spring Boot, and similar backends usually need persistent API handling and service state. Lightweight backend scripts in Python, Node, Go, Java, Rust, and similar languages are better suited for scheduled or event-triggered execution. One server pattern for all backend code would add unnecessary complexity.
04
The architecture needed room to evolve
Early services could ship quickly with Elastic Beanstalk, while future service splitting, containerization, independent scaling, and orchestration needs required a path toward EKS/ECS, avoiding lock-in to a single deployment model.
Architecture solution
Choosing deployment models by service architecture
We established AWS deployment paths by service architecture: static SPA / SSG frontends use S3 + CloudFront, dynamic SSR frontends use Elastic Beanstalk + CloudFront, lightweight backend scripts use Lambda + DynamoDB, lightweight backend services use Elastic Beanstalk + S3 + CloudFront + RDS, and complex backend services use EKS/ECS + ElasticCache + S3 + CloudFront + RDS.
Static SPA / SSG frontend
React / Vue / Vite / Next.js / Nuxt, etc.
S3 + CloudFront
Build output or pre-rendered pages are static assets, which are ideal for CDN delivery with low cost and low maintenance.
Dynamic SSR frontend
Next.js / Nuxt / Node, etc.
Elastic Beanstalk + CloudFront
SSR requires runtime infrastructure and server-side request handling.
Lightweight backend scripts
Python / Node / Go / Java / Rust, etc.
Lambda + DynamoDB
Event or scheduled execution plus DynamoDB for lightweight state avoids maintaining long-running servers for small jobs.
Lightweight backend service
Django / FastAPI / Express.js / NestJS / Gin / Spring Boot, etc.
Elastic Beanstalk + S3 + CloudFront + RDS
A managed runtime, static asset delivery, CDN routing, and relational storage fit a lightweight API backend.
Complex backend service
Any large-scale containerized service
EKS/ECS + ElasticCache + S3 + CloudFront + RDS
Fits large-scale containerized services that need service coordination, caching, object storage, relational data, and independent scaling.
Infrastructure complexity matches workload complexity
The core principle is matching infrastructure complexity to workload complexity: simple services stay simple, and complex services receive more capable infrastructure only when their shape requires it.
Results
From fragmented architecture to deployment governance by service architecture.
Established clear deployment tiers for static SPA / SSG frontends, dynamic SSR frontends, lightweight backend scripts, lightweight backend services, and complex backend services
Matched AWS infrastructure complexity to multiple frontend and backend languages and frameworks
Kept simple services from carrying unnecessary runtime and operations complexity
Preserved an evolution path from Elastic Beanstalk to EKS/ECS for backend services
Created a more maintainable cloud deployment model for long-term iteration
Before / After
Avoid using one deployment model for every kind of service.
| Area | Before | After AWS deployment by service architecture |
|---|---|---|
| Static SPA / SSG frontend | Could be mixed into servers or backend deployments | Deploy independently with S3 + CloudFront |
| Dynamic SSR frontend | Risked being forced into a static-only deployment model | Use Elastic Beanstalk for the required runtime |
| Lightweight backend scripts | Required servers or manual execution | Run lightweight jobs with Lambda + DynamoDB |
| Lightweight backend service | Self-managed server operations | Use Elastic Beanstalk + S3 + CloudFront + RDS for managed runtime delivery |
| Complex backend service | No clear scaling path | Move toward EKS/ECS + ElasticCache + S3 + CloudFront + RDS when orchestration becomes justified |
| Architecture governance | Deployment patterns became fragmented | Use standard deployment models based on service architecture |
Why it worked
The value came from the deployment decision system, not any single AWS service.
Avoided overengineering
Static SPA / SSG frontends and lightweight backend scripts do not need complex infrastructure. S3 + CloudFront and Lambda + DynamoDB keep simple services simple.
Preserved runtime capability
Services that need SSR or persistent APIs still have a managed runtime instead of being incorrectly compressed into static deployment.
Created an evolution path
Regular backend services can ship first with Elastic Beanstalk, S3, CloudFront, and RDS, then move into EKS/ECS, ElasticCache, and fuller orchestration only when complexity justifies it.
Architecture principle
We did not put every service into Kubernetes or force every service into serverless. Static SPA / SSG frontends fit CDN delivery, dynamic SSR frontends need runtime infrastructure, lightweight backend scripts fit Lambda + DynamoDB, lightweight backend services fit Elastic Beanstalk + S3 + CloudFront + RDS, and complex backend services can move into EKS/ECS + ElasticCache + S3 + CloudFront + RDS when justified.
Reusable value
The same pattern fits teams moving from MVP to engineered delivery.
This pattern fits B2B SaaS products, AI application platforms, internal operations systems, data processing tools, customer portals, products that mix static pages with dynamic apps, and startups moving from MVP to engineered delivery.
Frontend pages need fast publishing
Dynamic web apps need SSR or server-side capability
Backend APIs need stable runtime hosting
Lightweight backend scripts need on-demand or scheduled triggers
Growth may require containers and service decomposition
Overseas AWS Deployment
Does your system need a clearer deployment architecture?
If your team maintains static SPA / SSG frontends, dynamic SSR frontends, backend services, and lightweight scripts at the same time, we can help design an AWS architecture that avoids both overengineering and future scaling constraints.