Technology Comparison

AWS vs Azure / GCP - Which Should You Use?

Comparing AWS and Azure / GCP for your next project. Honest analysis of performance, ecosystem, and suitability for Australian businesses.

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Our Development Capabilities

Custom-built solutions for your specific business needs.

API Development and Integration

RESTful and GraphQL APIs that connect your systems seamlessly. Custom integrations with 150+ business tools.

Data Dashboards and Reporting

Custom analytics dashboards that give your team real-time visibility into business metrics and KPIs.

Performance Engineering

Server-side rendering, code splitting, and caching strategies that deliver sub-second load times at scale.

Progressive Web Apps

Web applications that feel native on mobile devices. Offline support, push notifications, and app-like performance.

Cloud-Native Architecture

Applications designed for scalability and reliability on modern cloud infrastructure. Auto-scaling, CDN, and edge deployment.

Quick Comparison

Side-by-side comparison to help you decide.

Criterion	AWS	Azure
Market share and ecosystem breadth	Largest cloud - most services, most third-party integrations, largest community	Azure: Microsoft ecosystem; GCP: data and ML strength
Regions in Australia	Sydney (ap-southeast-2) + Melbourne (ap-southeast-4) - two AU regions	Azure: Sydney, Melbourne, Canberra. GCP: Sydney + Melbourne
Managed Kubernetes	EKS - mature, battle-tested, complex to configure	AKS (Azure) - good DX. GKE (GCP) - often considered the best-managed K8s
Serverless functions	Lambda - most mature, broadest runtime support, fastest cold starts	Azure Functions (consumption plan). Cloud Functions / Cloud Run (GCP)
ML and data platform	SageMaker - comprehensive but complex; Bedrock for foundation models	Azure: Azure ML, OpenAI Service. GCP: Vertex AI, BigQuery ML - GCP leads here
Cost at startup scale	Competitive; free tier is broad; pricing complexity can lead to bill shock	Azure: credits for Microsoft startups. GCP: generous free tier for data services

Our Verdict

AWS is the default for most Australian businesses: the largest service catalogue, two local regions, the deepest talent pool, and the most third-party integrations. Choose Azure if your team runs Microsoft 365, is building .NET applications, or your organisation has an existing volume licensing agreement. Choose GCP if Google's data platform (BigQuery, Vertex AI) is a core requirement or your engineering team has strong GCP expertise. For mid-market Australian businesses with no pre-existing cloud commitment, AWS in ap-southeast-2 is the lowest-risk starting point.

Choose AWS when:

✓ No pre-existing cloud commitment and you want the deepest service catalogue
✓ Using managed services (RDS, ElastiCache, SQS) where AWS equivalents are most mature
✓ Need two Australian regions for geographic redundancy (Sydney + Melbourne)
✓ Most of your DevOps hires will know AWS (it is the most common certification in AU)

Choose Azure when:

✓ Azure: your organisation runs Microsoft 365 and Azure AD integration is needed
✓ Azure: .NET workloads and Windows Server are in scope
✓ GCP: BigQuery or Vertex AI are core to your data or ML strategy
✓ GCP: you want the cleanest managed Kubernetes (GKE) experience

Frequently Asked Questions

Our core stack is Next.js, React, and TypeScript, but we select technology based on your project needs. We have experience with Python, Node.js, PostgreSQL, and all major cloud platforms.

Yes, our team has experience with both AWS and Azure / GCP. We choose the right tool based on your project needs and recommend accordingly during our free assessment.

Pricing depends on what you need - a simple web app costs less than a complex system with multiple integrations. We provide fixed-price proposals after a free discovery session.

We specialise in integration. Whether it is your CRM, accounting software, or industry-specific tools, we connect your new application to your existing systems.

It depends on your project requirements. AWS and Azure / GCP each have strengths. Our detailed comparison above covers the trade-offs to help you decide.

Timelines depend on scope, but most projects go from kickoff to launch in 4-8 weeks. A working prototype is ready within the first 2 weeks for early feedback.