AWS vs Azure vs Google Cloud is the hyperscaler comparison. AWS leads on service breadth (most services, biggest ecosystem). Azure leads on enterprise integration (Active Directory, hybrid with on-prem, Microsoft shops). Google Cloud leads on data, ML, and Kubernetes (Google’s heritage, BigQuery, GKE). The team that picks based on existing IT investments (Microsoft shop -> Azure, data/ML -> GCP, general -> AWS) has the right default.
Table of contents
- Market share and ecosystem
- Pricing comparison
- Compute services
- Storage services
- Networking and global reach
- Strengths by use case
- FAQ
Market share and ecosystem
As of 2026:
- AWS: ~31% market share, launched 2006, ~200+ services.
- Azure: ~25% market share, launched 2010, ~200+ services.
- Google Cloud: ~11% market share, launched 2008, ~100+ services.
- Alibaba Cloud, Oracle Cloud, IBM Cloud, Tencent Cloud: 5% combined.
AWS has the largest community, most third-party tools, and most hiring. Azure has the deepest enterprise integration. GCP is the smallest of the three but has the strongest data and ML story.
Pricing comparison
Pricing is hard to compare directly because every cloud has different SKUs, discount programs, and bundling. General patterns:
- AWS: most expensive for steady-state, but biggest discount programs (Savings Plans, Reserved Instances up to 72% off).
- Azure: similar to AWS for compute, often cheaper for Windows workloads (Microsoft licensing benefits).
- GCP: often the cheapest for sustained use, automatic sustained-use discounts (no upfront commitment), custom VMs with better price/perf.
The team that runs steady-state workloads uses Reserved Instances or Savings Plans on AWS, sustained-use on GCP, or Azure Hybrid Benefit if Microsoft-licensed. The team that runs bursty workloads uses spot/preemptible instances.
Compute services
- EC2 (AWS) vs Azure VMs vs Compute Engine (GCP): all three offer similar VMs with various instance types, sizes, and pricing.
- Lambda (AWS) vs Azure Functions vs Cloud Functions (GCP): serverless functions, similar.
- ECS/Fargate (AWS) vs Container Instances (Azure) vs Cloud Run (GCP): containers, GCP’s Cloud Run is the easiest to use.
- EKS (AWS) vs AKS (Azure) vs GKE (GCP): managed K8s, GKE is the most managed.
The team that runs general compute picks based on the rest of the stack. The team that runs pure serverless picks GCP’s Cloud Run for ease or Lambda for AWS-native integration.
Storage services
- S3 (AWS) vs Blob Storage (Azure) vs Cloud Storage (GCP): object storage, all similar.
- EBS (AWS) vs Managed Disks (Azure) vs Persistent Disk (GCP): block storage.
- EFS (AWS) vs Azure Files vs Filestore (GCP): shared file storage.
- RDS (AWS) vs Azure Database vs Cloud SQL (GCP): managed relational DBs.
- DynamoDB (AWS) vs Cosmos DB (Azure) vs Firestore/Bigtable (GCP): managed NoSQL.
The team that needs object storage picks S3 (most mature). The team that needs managed Postgres picks the matching cloud’s offering for lowest latency.
Networking and global reach
- AWS: 30+ regions, 400+ edge locations.
- Azure: 60+ regions (most of any cloud).
- GCP: 40+ regions, premium-tier network.
GCP’s premium-tier network is faster between regions but more expensive. AWS’s global accelerator and CloudFront are mature. Azure’s front door and CDN are good.
The team that needs global low-latency picks the cloud with edge locations closest to users. The team that runs in one region picks based on cost and integration.
Strengths by use case
- AWS: general workloads, startups, broad service catalog.
- Azure: Microsoft shops, hybrid (Azure Arc), Windows workloads, enterprise compliance.
- GCP: data analytics (BigQuery), ML (Vertex AI), Kubernetes (GKE), sustained-use pricing.
The team that is a Microsoft enterprise picks Azure. The team that runs BigQuery-equivalent workloads picks GCP. The team that runs everything else picks AWS (default).
FAQ
Which cloud is cheapest?
Depends. GCP for sustained workloads with automatic discounts. AWS with Reserved Instances for committed use. Azure Hybrid Benefit for Microsoft-licensed workloads. The team that runs the same workload on all three with the same utilization usually finds GCP 5-15% cheaper for steady-state, AWS competitive with reservations, Azure cheapest for Microsoft stacks.
Which cloud is best for ML?
GCP. Vertex AI, BigQuery ML, and the underlying TPUs are Google’s heritage. The team that runs serious ML picks GCP or uses specialized providers (Lambda Labs, CoreWeave) for GPU-heavy workloads.
Can I use multiple clouds?
Yes - multi-cloud is real. The team that runs multi-cloud needs: a unified IAM (Auth0, Okta), a multi-cloud Kubernetes (CAPI, Crossplane), consistent observability (Datadog, Grafana Cloud), and a cloud-agnostic storage layer (or accepts per-cloud storage). The complexity is significant.
What about lock-in?
Real but manageable. K8s abstracts compute. Terraform abstracts IaC. S3-API-compatible object storage is available on all three. The team that uses these abstractions reduces lock-in but does not eliminate it (each cloud’s managed services have proprietary APIs).
Should I migrate to a different cloud?
The team that runs into specific limitations (cost, performance, compliance, missing service) migrates. The team that is happy with their current cloud does not. Cloud migrations are expensive and risky - the bar for moving should be real value, not theoretical flexibility.
If you are sizing the infrastructure for the kind of project this post covers, the RunxBuild hosting calculator is the right place to model the line items. The compute, the memory, the storage, the bandwidth, the database - each one is a separate number, and the team’s mental model for the platform is the sum of those numbers. The RunxBuild dashboard is where the team sees the actual usage in one place.
Useful related references: