Which cloud is cheapest — AWS, Azure, or GCP?

GCP is generally the most aggressive on compute pricing and offers the best always-free tier for experimentation. AWS and Azure are broadly comparable for most workloads, with AWS often cheaper for compute-intensive tasks and Azure cheaper for Microsoft workloads (Hybrid Benefit for existing SQL Server and Windows licenses). At scale, all three reward commitment: Reserved Instances (AWS), Reserved VM Instances (Azure), and Committed Use Discounts (GCP) deliver 40–70% savings vs on-demand. The right answer depends entirely on your specific workload mix — we recommend modelling costs for your actual workloads rather than relying on general benchmarks.

Can we use more than one cloud provider (multi-cloud)?

Multi-cloud is increasingly common for enterprises — 89% of organizations with more than $5M annual cloud spend use two or more providers. The most common pattern is AWS or Azure as the primary platform with GCP for specific workloads: BigQuery analytics, GKE clusters, or Vertex AI. The real costs of multi-cloud are operational: separate toolchains, compliance postures, and engineering expertise. Kubernetes, Terraform, and unified observability tools (Datadog) reduce friction significantly. We recommend multi-cloud when the workload benefit genuinely justifies the operational overhead — not as a default architecture.

What is the market share split between AWS, Azure, and GCP?

As of 2025: AWS holds approximately 31% of the global cloud infrastructure market, Azure around 25%, and GCP approximately 11%. The remaining ~33% is split across other providers. AWS has held the top position since cloud computing emerged; Azure has grown aggressively through enterprise Microsoft agreements; GCP has grown more slowly but has strong traction in data, AI, and startup-to-enterprise segments. Market share matters for ecosystem reasons — tooling, training, certifications, and third-party integrations follow adoption — but it should not be the primary decision factor.

How do we decide between AWS and Azure when both seem appropriate?

The practical tiebreaker is usually one of three things: (1) Your identity infrastructure — if you're an Active Directory shop, Azure Entra ID integration is a genuine operational advantage; (2) Your existing licensing — if you have Microsoft Enterprise Agreement credits or SQL Server/Windows Server licenses, Azure Hybrid Benefit makes Azure materially cheaper; (3) Your engineering team's expertise — cloud migrations are operationally complex, and teams build faster and make fewer expensive mistakes on the platform they know. If none of these apply, AWS is the safer default due to its larger service catalog and talent pool.

What is GCP's biggest weakness compared to AWS and Azure?

GCP's biggest structural weaknesses are talent availability and service breadth. There are significantly fewer GCP-certified engineers available than AWS or Azure professionals, which affects hiring, consulting support, and community knowledge. GCP's service catalog is narrower — there are categories (IoT, edge, enterprise messaging) where AWS and Azure have mature managed services that GCP either lacks or has less polished equivalents. GCP has also historically had a reputation for deprecating services more aggressively than competitors. For organizations without specific GCP workload needs (BigQuery, GKE, Vertex AI), these operational factors often tip the decision toward AWS or Azure.

Cloud Strategy

AWS vs Azure vs GCP: Which Cloud Platform Is Right for You in 2026?

AWS leads on service breadth and operational maturity. Azure wins in Microsoft-heavy enterprises. GCP wins on data analytics, Kubernetes, and AI/ML infrastructure. No single provider is universally best — the right cloud depends on your existing stack, workload type, compliance requirements, and team expertise. This guide compares all three across the dimensions that matter most for enterprise and mid-market decisions.

Halkwinds Verdict—AWS is the safest default for greenfield cloud-native builds. Azure is the clear choice for enterprises deeply invested in the Microsoft ecosystem. GCP is the specialist's pick for data engineering, BigQuery workloads, and Kubernetes-first architectures — and increasingly competitive for AI/ML.

Option A

AWS

The most mature cloud — widest service catalog, deepest managed services, largest global footprint.

Typical Cost

Pay-as-you-go + Savings Plans + Reserved Instances

Timeline

Depends on migration or build scope

Pros

Largest service catalog (200+ services) — a managed service for almost every use case

Best-in-class managed databases: Aurora, DynamoDB, RDS, ElastiCache

Widest global infrastructure — 33 regions, 105 availability zones

Most compliance certifications globally (HIPAA, PCI, FedRAMP, SOC 2, ISO 27001)

Richest AI/ML ecosystem: SageMaker, Bedrock, Rekognition, Comprehend

Largest talent pool — most cloud engineers have AWS experience

Most mature FinOps tooling: Cost Explorer, Compute Optimizer, Savings Plans

Cons

Pricing complexity — optimising AWS costs requires dedicated FinOps expertise

Weaker native Microsoft integration than Azure (Active Directory, Office 365)

Console and CLI can feel more complex for enterprise IT teams used to Microsoft tooling

Smaller free tier compared to GCP for experimentation workloads

Option B

Azure

Microsoft's cloud — the deepest enterprise Microsoft integration and a strong hybrid cloud story.

Typical Cost

Pay-as-you-go + Enterprise Agreements + Hybrid Benefit licensing

Timeline

Depends on migration or build scope

Pros

Native Active Directory and Entra ID integration — seamless identity for Microsoft shops

Best-in-class for .NET, SQL Server, Windows Server, and Office 365 workloads

Azure OpenAI Service — enterprise GPT-4 and Copilot access with data residency controls

Strongest hybrid cloud: Azure Arc manages on-premises and multi-cloud resources natively

Microsoft Enterprise Agreements often bundle Azure credits — effective cost advantage

Strong EU data residency story for European compliance requirements

Cons

Fewer specialized services than AWS in some categories (data streaming, IoT, edge)

Service quality inconsistency — newer services are less mature than AWS equivalents

GCP outperforms Azure on BigQuery-scale analytics and Kubernetes (GKE vs AKS)

More complex pricing model for mixed Microsoft workloads vs clean-sheet AWS deployments

Side-by-Side

Detailed Comparison

Dimension	AWS	Azure	Winner
Service breadth	Largest (200+ services)	Broad; GCP narrower but focused	AWS
Microsoft integration	Third-party connectors	Azure: Native AD, Office \| GCP: weak	Azure
Data & analytics	Redshift, Glue, Athena — strong	Azure Synapse solid; GCP BigQuery best-in-class	Tie
AI / ML platform	SageMaker + Bedrock (best-in-class)	Azure ML + OpenAI; GCP Vertex AI competitive	AWS
Kubernetes (managed)	EKS — mature and feature-rich	AKS solid; GKE (GCP) considered best-in-class	Tie
Compliance certs	Widest globally (HIPAA, FedRAMP, PCI)	Azure strong EU/govt; GCP fewer certs overall	AWS
Hybrid cloud	Outposts (limited on-prem reach)	Azure Arc (best-in-class); GCP Anthos solid	Azure
.NET / Windows workloads	Supported but not optimised	Azure: native; GCP: basic support only	Azure
Networking / CDN	CloudFront, Transit Gateway — excellent	Azure CDN solid; GCP Premium Network fastest	Tie
Serverless	Lambda — most mature and widely adopted	Azure Functions solid; GCP Cloud Run innovative	AWS
Cost optimisation tools	Best FinOps tooling ecosystem	Azure Cost Mgmt decent; GCP tooling catching up	AWS
Talent availability	Largest global pool	Azure strong in enterprise IT; GCP narrower	AWS
Free tier / sandbox	AWS Free Tier (12-month)	Azure: 12-month; GCP: always-free is best	Tie

Decision Framework

When to Choose Each Option

Choose AWS when...

Building cloud-native applications on open-source stacks — Python, Node.js, Go, containers
Your compliance requirements need the widest certification coverage (HIPAA, FedRAMP, PCI, SOC 2)
Scaling an AI/ML platform — SageMaker + Bedrock is the most mature managed ML ecosystem
You need best-in-class managed databases (Aurora, DynamoDB) or event streaming (Kinesis)
Your team's certifications and operational experience are AWS-based
You need the widest global infrastructure coverage (33 regions)

Choose Azure when...

Your enterprise runs on the Microsoft stack: Active Directory, Office 365, SQL Server, .NET — choose Azure
You need enterprise OpenAI (GPT-4) access with data residency guarantees — choose Azure
You have significant on-premises infrastructure requiring hybrid cloud integration — choose Azure
Your workload is BigQuery-scale analytics, data warehousing, or GCP-native ML (Vertex AI) — choose GCP
You're building a Kubernetes-first platform and want the most opinionated, managed experience — consider GKE on GCP
Your EU data sovereignty requirements mandate European cloud infrastructure — Azure Government or GCP Europe regions

Not sure which is right for your project?

We architect on all three platforms. We'll recommend the right provider — or a strategic multi-cloud combination — based on your workload, compliance posture, existing investments, and team's operational skills.

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GCP deserves serious consideration for three specific scenarios: (1) BigQuery-scale data analytics — BigQuery is objectively best-in-class for serverless data warehouse workloads and often significantly cheaper than Redshift or Azure Synapse at scale; (2) Kubernetes — GKE is considered the most polished managed Kubernetes service since Google invented Kubernetes; (3) AI/ML research and infrastructure — Google DeepMind's models, TPU access, and Vertex AI make GCP the preferred platform for serious ML teams. Outside these scenarios, GCP's smaller service catalog and talent pool are real disadvantages. Most enterprises use GCP as a workload-specific choice alongside AWS or Azure rather than as their primary cloud.

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