Picking the cheapest cloud provider sounds like a simple question. It isn’t. The answer depends on your workload type, your usage patterns, your negotiating leverage, and — critically — how you plan to get data out.

Here’s a practical comparison of AWS, Azure, and GCP pricing in 2026 across compute, storage, and the thing that often matters most: egress.

Compute: Who’s Cheapest for VMs?

Comparing compute across clouds is tricky because pricing changes frequently, committed use discounts are substantial, and instance types don’t map perfectly across providers. With those caveats:

General-purpose compute (on-demand, 4 vCPU / 16GB RAM equivalent):

• AWS m7i.xlarge: ~$0.202/hr in us-east-1
• Azure D4s v5: ~$0.192/hr in East US
• GCP n2-standard-4: ~$0.194/hr in us-central1

On-demand prices are broadly similar. The differentiation happens at commit levels:

• AWS: 1-year Savings Plan saves ~38%, 3-year saves ~57%
• Azure: 1-year Reserved VM saves ~35%, 3-year saves ~55%
• GCP: 1-year Committed Use Discount saves ~37%, 3-year saves ~55%

Verdict on compute: Remarkably similar at on-demand prices. GCP’s sustained use discounts (automatic discounts applied to instances running most of the month, no commitment needed) make it attractive for variable but sustained workloads. AWS has more instance variety. Azure pricing tends to be slightly more opaque.

Storage: Where GCP Wins

Object storage pricing follows a fairly predictable pattern:

Standard storage (per GB/month):

• AWS S3 Standard: $0.023/GB
• Azure Blob Storage (Hot): $0.018/GB
• GCP Cloud Storage (Standard): $0.020/GB

Archive/cold storage (per GB/month):

• AWS S3 Glacier Deep Archive: $0.00099/GB
• Azure Archive: $0.00099/GB
• GCP Archive: $0.0012/GB

Azure wins on hot storage price. AWS and Azure tie on archive. But storage unit price is rarely the dominant cost driver — retrieval operations and egress fees matter more.

For managed database storage, pricing diverges significantly based on engine and configuration. PostgreSQL on RDS vs Azure Database vs Cloud SQL can vary 2–3x for the same workload depending on instance sizing and multi-AZ configuration.

Egress: The Hidden Budget Killer

This is where cloud pricing gets painful. All three providers charge for data leaving their network, and the rates are high enough to materially affect architecture decisions.

Egress to internet (first 10TB/month):

• AWS: $0.09/GB (after 10TB, $0.085/GB)
• Azure: $0.087/GB (after 10TB, $0.083/GB)
• GCP: $0.08/GB (after 10TB, reduces further)

These prices are similar enough that they shouldn’t drive your architecture — but they do, because they compound. An application serving 50TB of data per month pays $4,000–$4,500 in egress fees alone, regardless of the underlying compute and storage costs.

Inter-region transfer (within same provider):

• AWS: $0.01–$0.02/GB
• Azure: $0.01–$0.05/GB (varies significantly by region pair)
• GCP: $0.01–$0.08/GB

This is the cost that bites multi-region architectures unexpectedly. A cross-region replication setup that nobody modelled at design time can silently add thousands to the monthly bill.

Cross-provider transfer: All providers charge their standard egress rate. If you’re running a microservice on AWS that calls an API on GCP, you’re paying egress on both sides. This is why vendor lock-in arguments have real financial weight, not just philosophical ones.

Which Cloud Is Cheapest for Which Workload?

This is genuinely workload-dependent, but some patterns hold:

Data analytics and ML: GCP tends to win. BigQuery’s pricing model (pay per query for on-demand, or flat rate) and TPU availability make it cost-effective for data-heavy workloads. Vertex AI pricing is competitive with SageMaker.

Windows workloads: Azure. Microsoft’s licensing advantage for Windows Server means Azure VMs running Windows are often 20–30% cheaper than AWS or GCP equivalents once you factor in bring-your-own-license options for Azure Hybrid Benefit.

Startup workloads (AWS-first): AWS’s breadth of managed services means less custom engineering, which often translates to lower total cost of ownership even if unit compute prices aren’t the lowest. The ecosystem depth matters.

Kubernetes/containers: Surprisingly similar once you look at total cost. AWS EKS management fee ($0.10/hr per cluster) vs Azure AKS (free control plane) vs GCP GKE Autopilot (per pod pricing) requires careful analysis for your specific cluster usage.

Why Most Companies End Up Multi-Cloud Anyway

In theory, committing to a single cloud provider should be cheaper: you negotiate better discounts, your team has deeper expertise, and you avoid cross-provider egress. In practice, most companies above a certain size end up multi-cloud for reasons that have nothing to do with price:

• Acquisitions: A company you acquire already runs on Azure
• Data residency requirements: Customer contracts require data in regions where your primary provider doesn’t have coverage
• Best-of-breed services: Snowflake, Databricks, Confluent — they run on multiple clouds and you consume them separately from your primary provider
• Disaster recovery: Some teams use a second cloud as a warm DR target
• Team preferences: An ML team hired from DeepMind defaults to GCP; the infrastructure team runs AWS

The practical result is that “which cloud is cheapest” often matters less than “how do we get visibility across all the clouds we’re already using.”

How to Track All Three

If you’re running workloads on multiple clouds, the cost comparison question eventually becomes a monitoring and attribution question: how do you see total cloud spend in one place, allocate it by team or product, and catch anomalies across all three environments?

The answer isn’t three separate tools. It’s unified billing visibility with a common data model — so that “compute spend” means the same thing whether it’s EC2, Azure VMs, or GCP Compute Engine instances.

That’s exactly what Xplorr is built to do: aggregate and normalise billing data from all three providers so you can make coherent decisions about where your money is going and where it should go.

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Xplorr gives you unified cost visibility across AWS, Azure, and GCP with anomaly detection and actionable recommendations. Request beta access — free for early teams.