Frontend Dfrontend deployment platforms vseployment Platforms vs AWS, Azure, and Google Cloud: Complete Architecture Guide

Modern web deployment is no longer a simple choice between uploading files to a server and renting a virtual machine.

Development teams can now deploy applications through specialized frontend platforms, managed cloud application services, serverless infrastructure, containers, Kubernetes clusters, or fully customized cloud architectures.

This creates an important architectural question:

Should you deploy a modern frontend application through a dedicated frontend platform, or build the deployment directly on AWS, Microsoft Azure, or Google Cloud?

The answer depends on much more than hosting price.

A production deployment decision can affect performance, global network architecture, scalability, CI/CD, security, observability, developer productivity, infrastructure control, vendor lock-in, and long-term operating cost.

A simplified frontend platform workflow often looks like this:

Developer
    │
    ▼
Git Repository
    │
    ▼
Build Pipeline
    │
    ▼
Frontend Deployment Platform
    │
    ├── CDN
    ├── Edge Runtime
    ├── Serverless Functions
    ├── TLS
    └── Preview Deployments
    │
    ▼
Global Users

A direct hyperscale-cloud architecture may expose considerably more infrastructure:

Developer
    │
    ▼
Git Repository
    │
    ▼
CI/CD Pipeline
    │
    ▼
Cloud Infrastructure
    │
    ├── Object Storage
    ├── Containers / Compute
    ├── Load Balancer
    ├── WAF
    ├── CDN
    ├── IAM
    ├── DNS
    └── Monitoring
    │
    ▼
Global Users

Neither architecture is automatically superior.

The fundamental difference is how much infrastructure the platform abstracts away from the engineering team.

This guide examines that difference from an architecture and DevOps perspective and explains where specialized frontend platforms, AWS, Azure, and Google Cloud fit into a modern application stack.

If you first want to understand how the three hyperscale providers differ below the application layer, read our Cloud Network Architecture: AWS vs Azure vs Google Cloud guide.

What Is a Frontend Deployment Platform?

A frontend deployment platform is a managed environment designed to take an application from source code to production while hiding much of the infrastructure traditionally required to serve it.

The workflow commonly begins with Git:

GitHub / GitLab
       │
       ▼
   Git Push
       │
       ▼
Automatic Build
       │
       ▼
Preview or Production Deployment
       │
       ▼
CDN / Edge Network
       │
       ▼
     Users

Platforms such as Vercel, Netlify, and Cloudflare Pages are examples of services built around this developer-oriented deployment model.

Instead of manually assembling a CDN, TLS configuration, deployment pipeline, build environment, and hosting layer, developers interact primarily with the application and repository.

The platform handles much of the remaining delivery architecture.

This represents a major shift in responsibility:

Traditional Hosting

Developer
   │
   ├── Server
   ├── OS
   ├── Web Server
   ├── TLS
   ├── Deployment
   ├── Scaling
   └── Monitoring

becomes:

Managed Frontend Platform

Developer
   │
   ├── Application
   ├── Repository
   └── Configuration

Platform
   │
   ├── Build
   ├── Deployment
   ├── CDN
   ├── TLS
   └── Scaling Infrastructure

The most important product being sold is therefore not simply hosting.

It is abstraction.

Dedicated Frontend Platform vs Cloud Provider

The distinction is easier to understand when viewed through layers.

A dedicated platform generally presents an application-first abstraction:

Application
     ↓
Framework
     ↓
Platform
     ↓
Infrastructure

A hyperscale provider gives engineers much deeper access to the underlying stack:

Application
     ↓
Runtime
     ↓
Compute
     ↓
Load Balancing
     ↓
Networking
     ↓
Storage
     ↓
Physical Cloud Infrastructure

This difference influences nearly every other architectural decision.

A frontend platform attempts to answer:

How can developers deploy applications with the least infrastructure management?

A general-purpose cloud platform asks a different question:

Which infrastructure components do you need to build the system?

That distinction explains why comparing only hosting prices gives an incomplete picture.

The Hyperscalers Also Offer Managed Frontend Services

The distinction between “frontend platform” and “cloud provider” is not absolute.

AWS, Microsoft Azure, and Google Cloud increasingly provide higher-level managed application services of their own.

For example, AWS Amplify Hosting provides Git-oriented deployment for modern web applications, including CI/CD workflows, branch deployments, and preview workflows. AWS documentation describes Amplify Hosting as a continuous delivery and hosting service for web applications.

Microsoft provides Azure Static Web Apps, which can build and deploy applications based on changes to a connected code repository and integrates with GitHub or Azure DevOps.

This creates an architectural spectrum rather than two completely separate categories:

Maximum Abstraction
        │
        ▼
Dedicated Frontend Platform
        │
        ▼
Managed Cloud Application Platform
        │
        ▼
Serverless
        │
        ▼
Containers
        │
        ▼
Kubernetes
        │
        ▼
Virtual Machines
        │
        ▼
Maximum Infrastructure Control

The real decision is therefore:

How much of the infrastructure stack should your team own?

Architecture of a Dedicated Frontend Deployment Platform

Consider a Next.js, React, Vue, Svelte, or similar application stored in a Git repository.

A managed workflow may be:

Developer
    │
    ▼
git push
    │
    ▼
Git Provider
    │
    ▼
Build Environment
    │
    ├── Install Dependencies
    ├── Run Build
    ├── Generate Assets
    └── Create Functions
    │
    ▼
Deployment Platform
    │
    ├── Static Assets → CDN
    ├── Dynamic Logic → Runtime
    └── Edge Logic → Edge Network
    │
    ▼
Users

The engineering team does not necessarily create individual virtual machines, load balancers, certificate automation systems, CDN distributions, or deployment servers.

The platform transforms source code into deployed infrastructure.

This is particularly valuable when infrastructure itself is not a competitive advantage for the organization.

Architecture of a Direct AWS Deployment

AWS gives architects several ways to deploy the same frontend.

A static application could follow:

User
  │
  ▼
Amazon CloudFront
  │
  ▼
Amazon S3

A more dynamic application could use:

Internet
   │
   ▼
CloudFront
   │
   ▼
Application Load Balancer
   │
   ▼
ECS / EKS / EC2
   │
   ▼
Application
   │
   ▼
Database / AWS Services

A serverless architecture could instead introduce API Gateway and Lambda.

Or a team can use AWS Amplify and move much closer to the managed frontend-platform experience.

AWS Amplify Hosting supports common SSR frameworks, SPA frameworks, and static site generators, while AWS documentation describes Git-based continuous deployment and deployment to AWS’s CDN infrastructure.

AWS therefore supports both ends of the spectrum:

Amplify
   ↓
Serverless
   ↓
Containers
   ↓
EKS
   ↓
EC2

The deeper you move down the stack, the more infrastructure decisions become your responsibility.

Architecture of a Direct Azure Deployment

Microsoft Azure provides a similarly broad range of deployment models.

At the highly managed end, Azure Static Web Apps can connect directly to GitHub or Azure DevOps and automatically build and deploy applications when code changes.

A simple architecture might look like:

Repository
    │
    ▼
Azure Static Web Apps
    │
    ├── Frontend
    └── Serverless API
    │
    ▼
Users

Microsoft’s documentation also supports staging environments, authentication and authorization, serverless API endpoints, and Infrastructure as Code workflows around the service.

Larger applications may instead use:

Internet
   │
   ▼
Azure Front Door
   │
   ▼
Application Layer
   │
   ├── App Service
   ├── Containers
   ├── AKS
   └── Virtual Machines
   │
   ▼
Private Azure Services

This becomes increasingly important when applications require enterprise networking, private endpoints, hybrid connectivity, or sophisticated identity controls.

Architecture of a Google Cloud Deployment

Google Cloud also lets engineering teams build at several abstraction levels.

A typical architecture can conceptually follow:

Users
  │
  ▼
Global Load Balancing / CDN
  │
  ▼
Application Runtime
  │
  ├── Serverless
  ├── Containers
  ├── Kubernetes
  └── Compute
  │
  ▼
Backend Services

The important architectural point is not that every application should use all these components.

It is that general-purpose cloud providers allow teams to decide which components should exist.

That flexibility becomes especially useful for applications requiring complex networking, multi-region infrastructure, specialized compute, private connectivity, or integration with existing enterprise systems.

Developer Experience: Where the Difference Becomes Obvious

Consider the work required to publish a simple application.

A specialized platform can reduce deployment to:

Connect Repository
       ↓
Detect Framework
       ↓
Configure Variables
       ↓
Deploy

A custom cloud architecture could require:

Create Cloud Account
       ↓
Configure IAM
       ↓
Design Network
       ↓
Configure Compute
       ↓
Configure Storage
       ↓
Configure Build Pipeline
       ↓
Configure CDN
       ↓
Configure DNS
       ↓
Configure TLS
       ↓
Configure Monitoring
       ↓
Deploy

That second workflow is clearly more complex.

But complexity is not necessarily wasted work.

Each additional layer represents additional control.

The architectural question is whether the application benefits from that control.

Git-Based Continuous Deployment

Modern frontend deployment increasingly treats Git as the deployment control plane.

A typical workflow is:

Developer
    │
    ▼
Feature Branch
    │
    ▼
Pull Request
    │
    ▼
Automated Build
    │
    ▼
Preview Environment
    │
    ▼
Review
    │
    ▼
Merge
    │
    ▼
Production

This changes deployment from a separate operational event into part of the software-development lifecycle.

AWS Amplify, for example, supports Git-based deployment workflows and feature-branch deployment patterns.

Azure Static Web Apps similarly builds and deploys applications based on repository changes.

This demonstrates an important industry trend:

The hyperscale clouds are increasingly adopting developer-experience patterns popularized by specialized deployment platforms.

Preview Deployments Are More Important Than They Look

Preview deployments are one of the most valuable capabilities of modern frontend platforms.

Imagine a team building a new checkout interface.

Without preview environments:

Developer
   ↓
Run Locally
   ↓
Screenshot
   ↓
Send to Designer

With preview environments:

Developer
   ↓
Pull Request
   ↓
Automatic Preview
   ↓
Unique URL
   ↓
Designer + QA + Product Team

Now everyone can inspect the real application before the code reaches production.

Preview deployments can improve:

• UI review;
• accessibility testing;
• QA;
• stakeholder approval;
• responsive-design validation;
• integration testing.

A custom AWS, Azure, or Google Cloud architecture can reproduce this behavior, but doing so may require additional CI/CD and Infrastructure as Code automation.

Framework Awareness

Traditional hosting generally sees an application as files or processes.

Modern frontend platforms increasingly understand the framework itself.

A modern application may contain:

Static Pages
     +
Server-Rendered Pages
     +
Client Components
     +
API Endpoints
     +
Background Logic
     +
Edge Middleware

The deployment platform may map these components to different execution environments.

Conceptually:

Application Build
       │
       ├── Static Assets ─────► CDN
       │
       ├── Dynamic Routes ────► Server Runtime
       │
       ├── API Routes ────────► Functions
       │
       └── Edge Logic ────────► Edge Runtime

This is significantly more sophisticated than copying HTML files to a server.

It also explains why framework-specific deployment platforms can provide an unusually smooth developer experience.

CDN Architecture and Global Performance

One of the most important infrastructure components in frontend delivery is the Content Delivery Network.

Without distributed caching:

User in Europe
       │
       │ Long Network Path
       ▼
Origin in North America

With a CDN:

                 Origin
                    │
              Global CDN
          ┌─────────┼─────────┐
          ▼         ▼         ▼
       Europe      Asia     America
          │         │         │
        Users     Users     Users

Static assets can be served much closer to users.

But a critical mistake is assuming:

CDN = fast application.

A CDN only solves part of the performance problem.

The Real End-to-End Request Path

Consider this application:

User
 │
 ▼
Nearby CDN
 │
 ▼
Frontend
 │
 ▼
API
 │
 ▼
Database

Suppose the frontend is served in 30 ms.

That sounds excellent.

But if the API request takes 350 ms and the database operation requires another 150 ms, the user’s experience is not a 30 ms application.

The actual path matters:

DNS
 +
TCP / QUIC
 +
TLS
 +
CDN
 +
Application Runtime
 +
API
 +
Database
 +
Response

This is why web performance and network architecture cannot be treated as completely separate subjects.

The provider backbone can influence how traffic travels between regions and services. Our Cloud Network Architecture: AWS vs Azure vs Google Cloud article examines this infrastructure layer in greater detail.

Edge Computing Changes the Traditional Model

Traditional application architecture often looks like:

User
  │
  ▼
Central Application Region

Edge architectures attempt to move selected computation closer to users:

              Central Backend
                    │
        ┌───────────┼───────────┐
        ▼           ▼           ▼
   Edge Europe   Edge Asia   Edge America
        │           │           │
      Users       Users       Users

Edge logic can potentially handle:

• redirects;
• authentication checks;
• request transformation;
• personalization;
• routing decisions;
• lightweight API operations.

However, moving code toward the edge creates new constraints.

Edge runtimes may differ from conventional server environments in areas such as supported APIs, execution time, memory, libraries, and network access.

Therefore:

Running closer to the user does not automatically mean every workload belongs at the edge.

Frontend Performance Is a Systems Problem

A useful way to think about application performance is:

User Experience
      =
Network Latency
      +
Server Processing
      +
Data Access
      +
Asset Delivery
      +
Browser Rendering

Optimizing only one component can produce diminishing returns.

For example, moving static assets closer to users may provide limited benefit if every page still waits for a distant database.

Similarly, placing compute at the edge may not solve latency if that compute must synchronously query a single-region backend.

Good frontend architecture therefore requires understanding the complete request graph.

Backend Connectivity Can Completely Change the Decision

Consider two applications.

Application A

Browser
   │
   ▼
Frontend Platform
   │
   ▼
Public SaaS API

This is relatively simple.

Now consider Application B:

Browser
   │
   ▼
Frontend
   │
   ▼
Private API
   │
   ▼
Microservices
   │
   ▼
Private Database
   │
   ▼
Corporate Data Center

The second application introduces an entirely different class of infrastructure problems:

• private addressing;
• routing;
• IAM;
• firewalls;
• service discovery;
• network segmentation;
• hybrid connectivity;
• transit architecture;
• observability.

At this point, cloud networking is no longer an implementation detail.

It becomes part of application architecture.

Hybrid Cloud Applications

Enterprise applications often span more than one environment.

For example:

Users
  │
  ▼
Frontend Platform
  │
  ▼
Cloud API
  │
  ▼
Cloud Transit Network
  │
  ▼
VPN / Dedicated Connection
  │
  ▼
Corporate Data Center
  │
  ▼
Legacy ERP

This is where AWS, Azure, and Google Cloud provide capabilities that go far beyond basic frontend hosting.

Large environments may require centralized connectivity architectures.

CorgiNetwork examines these designs in AWS Transit Gateway vs Azure Virtual WAN vs Google Network Connectivity Center.

As networks grow further, dynamic routing may become essential. Our BGP in Cloud Networking and Hybrid Routing guide explains how route exchange participates in hybrid and cloud architectures.

Infrastructure as Code Changes Cloud Operations

One argument against direct cloud deployment is the amount of infrastructure that must be configured.

But mature DevOps teams rarely manage complex production infrastructure entirely through manual console clicks.

Instead:

Infrastructure Definition
        │
        ▼
Git Repository
        │
        ▼
Code Review
        │
        ▼
CI/CD
        │
        ▼
Infrastructure Deployment

A network can therefore become code:

VPC
Subnets
Routes
Firewalls
Load Balancers
Compute
DNS
Monitoring

This improves:

• reproducibility;
• reviewability;
• disaster recovery;
• consistency;
• automation;
• environment creation.

Our Infrastructure as Code Explained guide covers the underlying model in more depth.

Infrastructure as Code does not eliminate cloud complexity.

It makes complexity manageable and repeatable.

Containers Introduce a Different Abstraction Layer

There is another option between highly managed frontend platforms and manually managed virtual machines:

containers.

Instead of deploying source code directly:

Source Code
    │
    ▼
Container Build
    │
    ▼
Container Image
    │
    ▼
Container Runtime

A containerized application can potentially run across multiple environments:

              Container Image
                    │
        ┌───────────┼───────────┐
        ▼           ▼           ▼
       AWS        Azure     Google Cloud

Containers can reduce runtime differences between environments.

However, they do not eliminate cloud dependencies.

The application may still depend on provider-specific:

• databases;
• IAM;
• storage;
• messaging;
• networking;
• secrets management;
• observability services.

Portability therefore exists on several layers.

Kubernetes Pushes Infrastructure Control Further

Kubernetes provides a powerful orchestration layer for containerized applications.

A simplified architecture could be:

Internet
   │
   ▼
Load Balancer
   │
   ▼
Ingress
   │
   ▼
Kubernetes Cluster
   │
   ├── Frontend Pods
   ├── API Pods
   ├── Worker Pods
   └── Internal Services

This architecture provides considerably more control than a dedicated frontend deployment platform.

It also creates considerably more operational responsibility.

Teams must consider:

• cluster upgrades;
• networking;
• autoscaling;
• ingress;
• service discovery;
• secrets;
• monitoring;
• resource requests;
• workload security.

Running a simple marketing site on Kubernetes would often introduce unnecessary complexity.

Running dozens of interconnected services may justify that complexity.

Architecture should match workload requirements.

Scaling: Managed Abstraction vs Explicit Control

A frontend platform often presents scaling as:

Traffic Increases
       │
       ▼
Platform Absorbs Demand

Direct cloud infrastructure exposes more of the mechanism:

Traffic
   │
   ▼
Load Balancer
   │
   ▼
Scaling Metric
   │
   ▼
Auto Scaling Policy
   │
   ▼
Additional Capacity

Engineers may control:

• minimum capacity;
• maximum capacity;
• CPU thresholds;
• request-based metrics;
• queue depth;
• scaling intervals;
• instance sizes;
• geographic distribution.

That additional control can be essential for some workloads and unnecessary for others.

For a deeper explanation, see Cloud Auto Scaling Explained.

Security Responsibility

Managed platforms can reduce the infrastructure security surface that application teams manage directly.

But they do not remove security responsibility.

Developers remain responsible for areas such as:

Authentication
Authorization
Application Vulnerabilities
Secrets
Dependencies
API Security
Data Protection

Direct cloud infrastructure adds another layer:

IAM
Network ACLs
Security Groups
Firewall Policies
Private Networks
Load Balancers
TLS Policies
Logging
Cloud Permissions

This produces an important trade-off.

More infrastructure control can allow sophisticated security architecture.

But every additional configuration surface also creates opportunities for misconfiguration.

Observability: Application View vs Infrastructure View

Production systems eventually fail.

The important question is whether engineers can determine where they failed.

A managed platform may expose:

Build Logs
Request Logs
Function Logs
Deployment History
Performance Metrics

A full cloud environment may provide visibility deeper into:

Application
    ↓
Container
    ↓
Load Balancer
    ↓
Firewall
    ↓
Network
    ↓
Route
    ↓
VPN
    ↓
Database

Consider a timeout.

The visible symptom is:

HTTP 504

But the actual problem might be:

Frontend
   │
   ✓
API
   │
   ✓
Route
   │
   ✗
Private Backend

For enterprise applications, infrastructure-level telemetry can be extremely valuable.

Vendor Lock-In Is More Complicated Than It Appears

Frontend platforms are sometimes criticized for vendor lock-in.

That concern is legitimate—but it also applies to hyperscale clouds.

Suppose an application depends heavily on:

Provider-Specific Runtime
Provider-Specific Database
Provider-Specific Queue
Provider-Specific IAM
Provider-Specific Edge APIs
Provider-Specific Observability

Moving the application may require substantial engineering work.

The relevant question is not:

Are we using a cloud provider?

It is:

How deeply does the application’s architecture depend on proprietary behavior?

Portability can be considered at several layers:

Source Code Portability
        ↓
Runtime Portability
        ↓
Data Portability
        ↓
Infrastructure Portability
        ↓
Network Portability

An application can be portable at one layer and highly locked-in at another.

Cost: The Most Misunderstood Comparison

A common mistake is comparing:

Frontend Platform Plan
vs
Virtual Machine Price

These are not equivalent products.

A managed platform may bundle:

Build Infrastructure
+
Hosting
+
CDN
+
TLS
+
CI/CD
+
Preview Environments
+
Deployment Automation
+
Observability

A custom cloud deployment may charge separately for:

Compute
+
Storage
+
Load Balancer
+
CDN
+
Requests
+
Build Minutes
+
DNS
+
Logging
+
Data Transfer

A meaningful comparison must evaluate the complete production architecture.

Engineering Time Is Infrastructure Cost

There is another cost that rarely appears on cloud pricing calculators:

engineering time.

The real equation is closer to:

Total Cost
    =
Infrastructure Bill
    +
Engineering Time
    +
Operational Complexity
    +
Incident Response

Imagine Platform A costs $300 more per month but saves 25 engineering hours.

Now imagine a large company already has:

Platform Engineering Team
Terraform Modules
CI/CD Templates
Centralized Monitoring
Cloud Networking
IAM Standards
Security Automation

For that organization, building directly on AWS, Azure, or Google Cloud may require relatively little additional operational effort.

The same architecture can therefore have completely different economics for two companies.

Data Transfer Architecture

Network topology also affects cost.

Consider:

Frontend Platform A
       │
       ▼
API in Cloud B
       │
       ▼
Database in Cloud C

Every request can potentially cross provider boundaries.

That may introduce:

• additional latency;
• data-transfer charges;
• operational complexity;
• more failure domains.

A theoretically flexible multicloud architecture can therefore become:

User
 ↓
Provider A
 ↓
Internet
 ↓
Provider B
 ↓
Internet
 ↓
Provider C

Multicloud is not automatically resilient or cost-effective.

It should solve a specific architectural requirement.

Failure Domains Matter

Architecture is not only about what happens when everything works.

Consider:

Git Provider
     ↓
Build Platform
     ↓
Deployment Platform
     ↓
DNS Provider
     ↓
API Provider
     ↓
Database Provider

Every dependency represents a possible failure domain.

A more distributed architecture can reduce dependency on one provider, but it can also increase the number of external systems required for the application to operate.

Resilience therefore requires understanding dependency chains rather than simply counting cloud providers.

When a Dedicated Frontend Platform Makes Sense

A specialized frontend platform can be especially effective for:

• marketing sites;
• documentation;
• blogs;
• ecommerce frontends;
• SaaS dashboards;
• prototypes;
• frontend-heavy applications;
• small engineering teams;
• applications relying on public APIs;
• projects requiring frequent preview deployments.

The ideal architecture may remain beautifully simple:

Developer
    ↓
Git
    ↓
Platform
    ↓
Global Delivery
    ↓
Users

Reducing unnecessary infrastructure can be a significant architectural advantage.

When Direct AWS, Azure, or Google Cloud Deployment Makes Sense

Direct cloud infrastructure becomes increasingly attractive when applications require:

• complex private networks;
• hybrid cloud connectivity;
• enterprise IAM;
• advanced firewall policy;
• private databases;
• custom routing;
• Kubernetes;
• specialized compute;
• high infrastructure customization;
• regulatory controls;
• centralized cloud governance;
• complex backend systems.

A production architecture may resemble:

Internet
   │
   ▼
DNS
   │
   ▼
CDN
   │
   ▼
WAF
   │
   ▼
Load Balancer
   │
   ▼
Application
   │
   ▼
Private Services
   │
   ▼
Database

with additional connections to corporate networks and other regions.

For these workloads, infrastructure is no longer merely a place where the application runs.

Infrastructure becomes part of the application design.

The Hybrid Model: Often the Most Interesting Architecture

The choice does not have to be:

Frontend Platform
OR
Cloud Provider

A modern architecture can use both:

             Users
               │
               ▼
      Frontend Platform
               │
               ▼
           API Layer
               │
               ▼
       AWS / Azure / GCP
               │
        ┌──────┼──────┐
        ▼      ▼      ▼
     Database Queue  Services

This allows frontend teams to retain a streamlined deployment workflow while backend and infrastructure teams retain deeper cloud control.

A more sophisticated enterprise model could be:

Users
   │
   ▼
Frontend Platform
   │
   ▼
Public API Gateway
   │
   ▼
Private Cloud Services
   │
   ▼
Transit Network
   │
   ▼
Corporate Data Center

This pattern combines developer experience with enterprise infrastructure.

The challenge shifts from deployment to integration architecture.

Frontend Platform vs Direct Cloud Deployment Comparison

Architecture Area	Dedicated Frontend Platform	Direct Cloud Infrastructure
Initial setup	Usually low complexity	Can require substantial design
Git integration	Usually central to workflow	Available through CI/CD tooling
Preview deployments	Often built in	Usually requires configuration
CDN	Commonly integrated	Explicitly configured
TLS	Highly automated	Managed but configurable
Compute control	Limited	Extensive
Network control	Limited	Extensive
Private networking	Platform-dependent	Extensive
Hybrid networking	Usually limited	Strong
Custom routing	Limited	Strong
Containers	Platform-dependent	Strong
Kubernetes	Not usually core	Native option
Infrastructure as Code	Varies	Mature ecosystem
Scaling	Highly abstracted	Highly configurable
Operational burden	Generally lower	Generally higher
Architecture flexibility	Opinionated	Extensive
Developer onboarding	Often fast	Depends on architecture
Enterprise integration	Varies	Extensive

The table should not be read as a scorecard.

Each architecture optimizes for different requirements.

A Better Decision Framework

Instead of asking:

Which hosting platform is best?

ask the following questions.

1. Is the application primarily frontend?

If most of the workload is static or frontend-oriented, specialized deployment infrastructure can eliminate unnecessary operational complexity.

2. Does the application require private networking?

If the application must communicate extensively with private services, enterprise networks, or on-premises systems, deeper cloud networking capabilities may become important.

3. Does your organization already have a cloud platform team?

Existing Terraform modules, CI/CD templates, IAM policies, Kubernetes platforms, and monitoring systems can significantly reduce the cost of direct cloud deployment.

4. How important are preview environments?

Teams shipping frequent UI changes may gain significant productivity from automatic preview deployments.

5. Is portability a real requirement?

Identify exactly which components must remain portable.

Do not treat “multicloud” as a goal without defining the business or technical problem it solves.

6. Does the workload require specialized compute?

GPU workloads, complex data processing, advanced networking, and custom compute configurations generally favor broader cloud infrastructure.

7. How much infrastructure does the team actually want to operate?

This may be the most important question.

Common Architectural Mistakes

Overengineering a Simple Website

A simple site does not necessarily need:

Kubernetes
+
Service Mesh
+
Multiregion Database
+
Custom Transit Network

Complexity should solve a problem.

It should not exist merely because the technology is available.

Underengineering a Complex Enterprise Application

The opposite mistake is forcing a growing enterprise application into an abstraction layer that no longer provides sufficient control over networking, security, or backend infrastructure.

Architecture should evolve as requirements evolve.

Comparing Only Monthly Hosting Prices

A $20 hosting service and a $20 virtual machine do not necessarily represent the same operational capability.

Always calculate the full architecture.

Ignoring Network Topology

Application diagrams frequently show:

Frontend → API → Database

as though the arrows were free and instantaneous.

They are not.

Each arrow represents a network path with:

• latency;
• routing;
• security policy;
• availability;
• potential transfer cost.

The network is part of the application.

Adopting Multicloud Without a Defined Requirement

Deploying components across multiple providers can increase flexibility.

It can also multiply:

IAM Systems
Networking Models
Billing Models
Monitoring Systems
Operational Skills
Failure Modes

Multicloud should therefore be an architectural response to a requirement—not an objective by itself.

Frequently Asked Questions

What is a frontend deployment platform?

A frontend deployment platform provides managed infrastructure for building, deploying, and delivering web applications, often integrating Git workflows, CI/CD, CDN distribution, TLS, preview deployments, and application runtimes.

Is Vercel a cloud provider?

Vercel provides a cloud application platform focused strongly on web development and frontend workloads. Its abstraction and service scope differ from general-purpose hyperscale platforms such as AWS, Microsoft Azure, and Google Cloud.

Can I deploy a frontend directly on AWS?

Yes. Depending on the architecture, frontend workloads can use services ranging from managed AWS Amplify Hosting to object storage, CDN infrastructure, serverless services, containers, Kubernetes, and virtual machines. AWS documents Amplify Hosting as a Git-based continuous-deployment platform for web applications.

Does Azure provide frontend hosting?

Yes. Azure Static Web Apps provides repository-driven deployment for modern web applications and supports integrations including GitHub and Azure DevOps.

Can I combine a frontend platform with AWS or Azure?

Yes. This is a common architectural pattern. The frontend can use a specialized deployment platform while APIs, databases, identity systems, and other backend services run in a hyperscale cloud.

Which approach provides more networking control?

Direct AWS, Azure, or Google Cloud infrastructure generally exposes much deeper control over private networks, routing, VPN connectivity, dedicated connectivity, transit networking, firewall policy, and hybrid architectures.

Are dedicated frontend platforms always easier?

They generally abstract more infrastructure, which can simplify common deployment workflows. However, application complexity can reappear when private networking, backend integration, compliance requirements, or specialized runtime behavior becomes necessary.

Is direct cloud deployment cheaper?

Not necessarily. Cost depends on workload architecture, traffic, data transfer, managed services, engineering time, and operational requirements. Comparing only the price of one compute instance with a managed-platform subscription is usually incomplete.

Final Architecture Perspective

The most important difference between dedicated frontend deployment platforms and direct deployment on AWS, Azure, or Google Cloud is not simply ease of use.

It is where the responsibility boundary sits.

A highly managed frontend platform attempts to transform:

Application
+
Build System
+
Deployment Pipeline
+
CDN
+
TLS
+
Scaling
+
Infrastructure

into:

Code
 ↓
Git
 ↓
Deploy

A general-purpose cloud platform allows engineering teams to expose and control more of the stack:

Application
      ↓
Runtime
      ↓
Compute
      ↓
Load Balancing
      ↓
Security
      ↓
Networking
      ↓
Storage
      ↓
Global Infrastructure

Neither model is universally correct.

For a small development team shipping a frontend-focused application, operating a custom cloud network and compute stack may add cost without creating meaningful value.

For an enterprise application connected to private APIs, multiple regions, corporate networks, regulated data, and dozens of backend services, infrastructure control may be fundamental to the design.

There is also an increasingly important middle ground.

AWS Amplify, Azure Static Web Apps, serverless platforms, containers, and similar managed services allow organizations to choose different abstraction levels without treating infrastructure as an all-or-nothing decision.

The better architectural question is therefore not:

Which cloud platform is best?

It is:

Which infrastructure responsibilities should our engineering team own, and which should the platform own?

Once that boundary is clear, the deployment decision becomes much easier.

For the next layer of the architecture, continue with Cloud Network Architecture: AWS vs Azure vs Google Cloud to understand how the hyperscale networks differ beneath application deployment.

From there, AWS Transit Gateway vs Azure Virtual WAN vs Google Network Connectivity Center explains centralized cloud connectivity, while BGP in Cloud Networking and Hybrid Routing moves deeper into dynamic route exchange.

For the automation layer, Infrastructure as Code Explained provides the foundation for repeatable infrastructure, and Cloud Auto Scaling Explained explains how modern environments adjust capacity as demand changes.

Together, these guides form a logical CorgiNetwork learning path:

Frontend Deployment → Cloud Architecture → Cloud Networking → Transit → BGP → Infrastructure as Code → Auto Scaling.