The honest answer: it rarely matters as much as vendors claim
For a typical business estate, all three major providers offer capable compute, storage, networking and managed database services. Feature comparison spreadsheets tend to overstate the differences, because most gaps close within a year and rarely matter for the workload actually being run.
What actually drives a good decision is narrower: existing licensing relationships, the skills already inside the team, data residency and compliance requirements, and whether a specific managed service is a genuine differentiator for the workload in question.
AWS — the broadest service catalogue
AWS launched first and has the largest number of regions, availability zones and individual services. That breadth is a real advantage when a workload needs a specific, narrow capability — there is usually a managed service for it.
- The largest and most mature partner and marketplace ecosystem, useful when procuring third-party tools through cloud commitments.
- Deep container and serverless options (ECS, EKS, Lambda, Fargate) with the longest production track record of the three.
- The most extensive set of database engines, including purpose-built options such as DynamoDB, Aurora and Redshift.
- A steeper console learning curve than the other two — the breadth of services can slow onboarding for smaller teams.
- Reserved Instances and Savings Plans are flexible but require active management to avoid overcommitting.
Azure — the natural choice with an existing Microsoft estate
Azure is the strongest option where an organisation already runs Windows Server, SQL Server, Active Directory or Microsoft 365 at scale. Licensing benefits (Azure Hybrid Benefit, existing Enterprise Agreements) can materially change the cost comparison in ways a feature list will not show.
- Native integration with Active Directory and Microsoft 365, which simplifies identity for organisations already on that stack.
- Strong hybrid tooling (Azure Arc, Azure Stack) for estates that will keep some workloads on-premise indefinitely.
- Synapse Analytics and Power BI give a reasonably integrated analytics path without introducing a third vendor.
- Regional availability and government cloud options are strong in markets where Microsoft already has a large enterprise presence.
- Some services lag AWS equivalents in maturity, though the gap has narrowed considerably in recent years.
Google Cloud — the strongest data and analytics platform
GCP trails the other two in overall market share, but leads in specific areas: BigQuery is frequently the fastest and simplest route to a working data warehouse, and Google network infrastructure gives consistently strong global network performance.
- BigQuery separates storage from compute cleanly and requires very little operational tuning to get useful performance.
- Kubernetes originated at Google, and GKE remains widely regarded as the most polished managed Kubernetes offering.
- Strong data and machine learning tooling (Vertex AI, Dataflow, Pub/Sub) for teams building analytics or ML-heavy products.
- Smaller partner ecosystem and fewer regions than AWS or Azure, which matters for specific residency or latency requirements.
- Historically the smallest enterprise support organisation of the three, though this has improved.
A decision framework that is not a feature comparison
Work through these questions in order. Most organisations find the answer well before reaching the bottom of the list.
- Existing licensing: significant Microsoft licensing already in place points toward Azure unless there is a strong reason otherwise.
- Team skills: certifying and hiring against an unfamiliar provider is a real, recurring cost — do not discount it against a feature advantage.
- Data and analytics weight: if the workload is data-warehouse or ML heavy, GCP deserves serious consideration purely on BigQuery and Vertex AI.
- Compliance and residency: confirm the specific regions and certifications required exist today, not on a roadmap.
- Existing commitments: an active AWS or Azure enterprise agreement with unspent committed spend is a real cost to walk away from.
- Vendor lock-in tolerance: containerised, Kubernetes-based workloads port between providers far more easily than serverless-first designs.
Should you run more than one provider?
Multi-cloud is frequently proposed as a way to avoid lock-in or improve resilience, and it is rarely the right default. Running two providers well requires duplicated skills, duplicated tooling and duplicated operational processes, and it forfeits the volume discounts a single-provider commitment earns.
Multi-cloud makes sense in narrower cases: a genuine regulatory requirement for provider diversity, an acquisition that inherited a second estate, or a specific workload that is materially better on a second provider (a GCP-based analytics platform alongside an AWS-based application estate is a common, defensible pattern). Choosing multi-cloud as a strategy rather than a consequence usually costs more than it protects against.
What it actually costs to switch later
Providers are not switched lightly, and the framework above should weigh that. The realistic cost of a later move includes re-platforming any provider-specific managed services used, retraining or rehiring the team, renegotiating committed spend, and running a migration project with all the discovery and validation that implies.
This is an argument for designing for portability where the cost is low (containerise, use open table formats, avoid unnecessary proprietary services) rather than an argument for avoiding managed services altogether — a managed database that saves months of operational effort is usually worth the coupling it creates.
Frequently asked questions
None is consistently cheapest across all workload types. List prices are broadly comparable; the real cost differences come from licensing benefits, committed-use discounts, and how well a workload is right-sized and architected on the chosen platform.
By overall market share, yes, and it remains the broadest service catalogue. That does not make it the best fit for every workload — Azure and GCP each lead in specific areas relevant to particular estates.
Free credits are a reasonable tie-breaker when the underlying choice is otherwise close, but they should not override a genuine mismatch in team skills or required services. Credits run out; the architecture built on top does not change with them.
Containerised, Kubernetes-based workloads with open data formats port with moderate effort. Workloads built heavily on provider-specific serverless and managed services are the most expensive to move and should be a deliberate choice, not an accident.
For the common frameworks (SOC 2, ISO 27001, HIPAA, PCI DSS) yes, GCP is broadly comparable. Region availability and specific government or sovereign cloud offerings are narrower than AWS or Azure, so verify the specific requirement against current, not roadmap, availability.
Usually not as a starting position. Depth on one provider delivers more value than shallow multi-cloud coverage for most teams. Build the second-provider skill set only once a genuine multi-cloud need is confirmed.
How we can help
Provider-neutral migration planning, from discovery through cutover.
Solution Design & ArchitectureTechnology selection and a costed target state before you commit to a provider.