The State of the Cloud Market in 2025
The hyperscaler cloud market has stabilised around three dominant players: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Together they hold approximately 65% of the global cloud market — AWS at roughly 33%, Azure at 22%, and GCP at 11%. All three have reached a level of maturity where they offer comparable core services; the differences increasingly lie in ecosystem, pricing models, support quality, and specific service strengths.
The "which cloud?" question has become less about capability gaps (all three can run your workloads) and more about strategic fit: your team's existing skills, your existing vendor relationships, your workload characteristics, and your cost optimisation goals.
AWS: The Breadth Leader
AWS remains the default choice for most greenfield cloud deployments for good reason: it has the broadest service catalogue (200+ services), the most mature ecosystem of third-party integrations and tooling, and the largest community of experienced engineers.
AWS strengths:
- Broadest service catalogue — virtually every workload type has a purpose-built AWS service
- Largest pool of experienced engineers — easier to hire and find community support
- Best-in-class serverless (Lambda, Step Functions, EventBridge)
- Strongest marketplace for third-party software
- Most mature compliance certifications (1,000+ across global regions)
AWS weaknesses:
- Cost complexity — the pricing model is notoriously complex; surprise bills are common without rigorous tagging and monitoring
- Console UX — the AWS console is powerful but dense; onboarding new team members is slower than Azure
- Enterprise support tiers are expensive (Enterprise Support starts at $15,000/month or 10% of monthly spend)
Best for: most workloads; startups and scale-ups without existing cloud commitments; teams with strong AWS skills; serverless-first architectures.
Azure: The Enterprise and Microsoft Stack Choice
Azure's primary advantage is integration with the Microsoft ecosystem. If your business runs on Microsoft 365, Active Directory, Teams, or any Microsoft enterprise software, Azure integrates more seamlessly than competitors.
Azure strengths:
- Best-in-class Active Directory and identity integration (Azure AD / Entra ID)
- Native integration with Microsoft 365, Teams, and Dynamics 365
- Excellent .NET and Windows Server support (better than AWS for legacy Windows workloads)
- Strong enterprise sales support and commitment to enterprise SLAs
- Azure OpenAI Service — exclusive enterprise access to OpenAI models with data residency guarantees
Azure weaknesses:
- Historically slower service releases than AWS, though this gap has narrowed
- Some services are less mature than their AWS equivalents (e.g., Azure ECS vs AWS EKS)
- Documentation quality is inconsistent across services
Best for: enterprises already on Microsoft stack; regulated industries requiring Microsoft compliance frameworks; AI workloads requiring Azure OpenAI Service; Windows-heavy workloads.
Google Cloud: The Data and AI Leader
GCP is the strongest choice for data and AI workloads. Google's infrastructure powers its own AI research, and that capability is available to GCP customers through best-in-class services like BigQuery, Vertex AI, and Google Kubernetes Engine (widely considered the best managed Kubernetes offering).
GCP strengths:
- Best managed Kubernetes (GKE) — battle-tested at Google scale
- BigQuery — serverless data warehouse that outperforms AWS Redshift and Azure Synapse on large analytical workloads
- Vertex AI — comprehensive ML platform with access to Google's latest models (Gemini)
- Network performance — Google's private global fibre network delivers superior cross-region latency
- Sustained use discounts — automatic discounts for long-running VMs without upfront commitment
GCP weaknesses:
- Smallest market share means smaller community and fewer experienced engineers to hire
- History of product discontinuation (Google kills services) creates trust concerns
- Enterprise sales and support have historically been weaker than AWS/Azure
Best for: data-heavy workloads; ML/AI-intensive applications; Kubernetes-first architectures; analytics and data warehouse workloads.
The Multi-Cloud Question
Multi-cloud (using multiple providers simultaneously) is increasingly common and sometimes strategic, but it's also expensive and complex to manage. Our recommendation: unless you have a specific reason (regulatory requirement for redundancy, vendor lock-in risk for a critical service, or distinct workload characteristics that make different providers optimal), start with a single cloud provider. The operational overhead of managing two cloud environments with their own security models, billing, and tooling is significant.
The exception is using cloud-provider-specific AI services — it's completely reasonable to run your primary workloads on AWS while using Azure OpenAI Service for LLM access, or GCP Vertex AI for specific ML pipelines. This doesn't create the management complexity of full multi-cloud.
Migration Approach: Lift-and-Shift vs Cloud-Native
The migration approach matters as much as the provider choice. Lift-and-shift (rehost) is faster and lower-risk: take your existing virtual machines and move them to cloud VMs. You gain cloud economics (pay-as-you-go, no hardware maintenance) but don't gain cloud-native benefits (elasticity, serverless, managed services). Cloud-native refactoring takes longer and costs more upfront but delivers the full value of the cloud. The right choice depends on your timeline pressure and budget for the migration project.