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Comparison

AWS vs Azure vs Google Cloud

AWS, Azure and Google Cloud all run production workloads at planet scale. The differences are practical: which one fits your team's skills, your enterprise stack and your workload shape.

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At A Glance

Side-by-side comparison.

Criterion AWS Azure Google Cloud
Service catalog breadth Widest (200+ services) Very broad (~200) Narrower but deep
Enterprise stack fit Neutral — works with everything Best for Microsoft shops Best for data + open-source stacks
Pricing transparency Complex but most-documented Enterprise-favoured Most transparent + sustained-use discounts
AI / ML services Bedrock, SageMaker Azure AI + OpenAI exclusive Vertex AI + Gemini
Kubernetes maturity EKS (good) AKS (good) GKE (best-in-class)
Data warehouse Redshift Synapse BigQuery (industry leader)
Talent availability Largest pool Strong in enterprise / .NET Smaller, deeper specialists
Hybrid / on-prem Outposts (good) Arc (best-in-class for hybrid) Anthos (Kubernetes-only)

Cells with a coloured accent show the winner for that row.

Deep Dive

The detail behind each criterion.

Service catalog and maturity

Winner: AWS

AWS still has the widest catalog — there is virtually no service category where AWS does not have a production-grade managed offering. For most general workloads, AWS is the lowest-risk choice precisely because it has been running them at scale longest.

Azure has caught up dramatically. Its catalog is comparable in breadth, with Microsoft-stack integrations (Active Directory, Office 365, Power BI, Dynamics) that no one else can match.

Google Cloud is narrower but extremely deep in its specialisms — Kubernetes (GKE is the reference implementation), BigQuery, Spanner, Pub/Sub and AI/ML platforms. For workloads in those categories, GCP often beats both competitors on developer experience.

AI / ML and generative AI

Winner: Azure

This category flipped fast in 2023–2024. Azure has exclusive cloud access to OpenAI’s GPT-4 and GPT-4o family — for many enterprises that alone is the deciding factor. Azure AI Foundry now provides a unified platform for building and deploying AI agents.

Google Cloud counters with Gemini and Vertex AI — strong models, excellent tooling, deep integration with BigQuery for data-grounded RAG.

AWS has Bedrock (multi-model marketplace including Claude, Llama, Cohere, Mistral) and SageMaker for custom model training. Less of a single hero model, more of a model marketplace.

Pricing and cost optimization

Winner: Google Cloud

Google Cloud is the most transparent of the three. Sustained-use discounts apply automatically without negotiation, and per-second billing is genuinely per-second. BigQuery’s on-demand pricing is uniquely simple compared to alternatives.

Azure’s enterprise customers get the best negotiated rates — through Microsoft Enterprise Agreements, customers with significant Office 365 or Dynamics spend get Azure credits and steep discounts.

AWS pricing is opaque but extensively documented — third-party tools (CloudHealth, Spot.io, AWS’s own Cost Explorer) make it manageable. Reserved Instances and Savings Plans give 30–70% discounts but require upfront commitment.

Decision Guide

When to choose each one.

Choose AWS if

Choose AWS if:

  • You want the broadest service catalog and lowest “service doesn’t exist” risk
  • You have a general-purpose workload mix without a strong tilt toward Microsoft or data-platform specialisms
  • You’re hiring fast and need the largest available talent pool
  • You’re a startup — the AWS Activate credits + ecosystem maturity is hard to match

Choose Azure if

Choose Azure if:

  • You’re a Microsoft shop — heavy Active Directory, Office 365, Dynamics 365, .NET
  • You need OpenAI’s frontier models with enterprise commitments and data-residency controls
  • Hybrid cloud (mix of on-prem + cloud) is a hard requirement — Azure Arc is best-in-class
  • Your sector is government, healthcare or regulated finance — Azure’s compliance footprint is the deepest

Choose Google Cloud if

Choose Google Cloud if:

  • You’re a data-heavy organisation — BigQuery is genuinely best-in-class for analytical workloads
  • You’re building Kubernetes-native — GKE is the reference Kubernetes platform
  • You’re running ML / data-science workloads at scale
  • You value pricing transparency and want default sustained-use discounts
Migration

Moving from one to the other.

Multi-cloud is rarely the right answer for typical companies — it doubles the operational surface and rarely provides the redundancy benefits people expect. Most migrations we run are single-cloud consolidation rather than multi-cloud.

Common migration paths:

  1. On-prem → AWS — the safest default for a first-cloud journey.
  2. AWS → Azure — almost always driven by Microsoft licensing economics or AI/Office365 integration.
  3. AWS → GCP — usually data-engineering teams chasing BigQuery + Vertex AI.

Whatever the move, we plan it with parallel-running, traffic-shifting and a rollback path. Read more on cloud migrations.

Background

The fuller picture.

Most cloud-platform debates are tribal. The actual question is: given the workload shape, the team’s skills and the existing enterprise stack, which provider gives the lowest friction and lowest TCO for the next three to five years?

Ready When You Are

Still not sure which
to pick?

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