AWS vs GCP vs Azure: Cloud Cost Comparison

Comparing cloud provider costs is one of the most requested analyses in any organization using, or considering, multiple clouds. The challenge is that a straightforward price comparison is nearly impossible because each provider uses different pricing models, discount structures, and service names for equivalent capabilities.

AWS, GCP, and Azure all offer compute, database, Kubernetes, storage, and networking at broadly similar price points, but the details matter. GCP offers sustained use discounts automatically, while AWS requires explicit savings plan purchases. Azure provides hybrid benefit for Windows workloads, while AWS and GCP do not. Kubernetes control plane costs differ: GKE offers a free tier, EKS charges $73/month, and AKS is free.

The most useful cost comparison is not “which provider is cheapest per vCPU-hour” but “which provider is cheapest for my specific workload pattern.” A workload running 24/7 on Linux has different optimal pricing than a Windows workload running 10 hours per day. The discount programs, scheduling options, and spot pricing availability all factor in.

This guide keeps the theory short and spends most of its length on what you can actually do. Every recommendation here is one ZopNight can help you execute, starting from a read-only connection.

Compute pricing comparison

On-demand compute pricing is remarkably similar across providers for equivalent instance types: a 4-vCPU, 16 GB instance costs roughly $0.17-0.19/hr across all three. The differences emerge in discount programs.

Database pricing comparison

Managed database pricing varies more than compute. AWS RDS, GCP Cloud SQL, and Azure SQL each have different pricing tiers and discount structures. Multi-AZ/HA configurations roughly double the cost on all providers. The key cost difference for non-production databases is idle cost: all three providers charge full price for idle databases, making scheduling equally valuable across providers. GCP Cloud SQL and Azure SQL Serverless offer auto-pause features that AWS RDS lacks.

Kubernetes pricing comparison

EKS charges $0.10/hr ($73/month) for the control plane. GKE Standard charges the same but offers one free zonal cluster. AKS control plane is free. However, the control plane cost is typically dwarfed by worker node costs. Node pool scheduling, scaling to zero outside business hours, delivers the same percentage savings regardless of provider and is the largest Kubernetes cost optimization lever.

Optimization strategies by provider

AWS: Combine Savings Plans for production with scheduling for non-production. Use Spot for batch workloads. GCP: Leverage sustained use discounts automatically, add CUDs for stable production, and schedule non-production. Azure: Apply Hybrid Benefit for Windows/SQL Server workloads, use reservations for stable Linux VMs, and schedule non-production. Across all providers, scheduling non-production environments delivers the fastest, lowest-risk savings.

Key takeaways

• Discount programs differ significantly: Savings Plans (AWS) vs CUDs (GCP) vs Reserved Instances (Azure).

• The biggest cost difference comes from workload-specific factors, not list prices.

Where ZopNight fits

ZopNight turns this from reading into doing. It ships 490 built-in audit rules across AWS (216), GCP (127), and Azure (147), 124 of those recommendations are wired to act end to end, 28 one-click and 96 guided, and it starts read-only so you can see the opportunity before you act on any of it. The most direct place to begin is scheduling non-production resources to your working hours, which is covered in the FinOps guide and shown concretely for AWS EC2.

How ZopNight schedules non-production resources

The loop that does this is deliberately mechanical, and it starts read-only. You connect your cloud provider with a read-only role, and ZopNight discovers every non-production resources across your regions and accounts. It records a per-action permission verdict for each one, so you can see where it can list a resource but not yet stop it, and you review that inventory, filter it by status or type, and search for the specific resources you care about before anything is scheduled.

Scheduling itself is a cron you write once in plain terms, stop at 7 PM, start at 8 AM on weekdays, pinned to your timezone so the jobs fire at local business hours rather than UTC. A weekly 24-hour grid shows the schedule visually so you catch gaps and overlaps before you save, and an estimate of active versus inactive hours appears before you commit. Resources attach individually or bundle into groups like “dev-cluster” or “staging-db” so a whole environment follows one cadence.

Actions run in dependency order, so a database comes up before the app server that depends on it. When something needs to stay up, an override forces a non-production resources ON or OFF for a defined window, carries a reason so teammates understand why it exists, and expires automatically so nothing is left running by accident. If a start or stop fails, ZopNight retries up to three times and falls back to a dead-letter queue rather than silently dropping the action, and every state change lands in an audit trail that records whether a schedule, an override, or a specific user triggered it.

Beyond the schedule: recommendations, rightsizing, and showback

Scheduling is the fastest lever, but it is one of several. ZopNight ships 490 built-in audit rules across AWS (216), GCP (127), and Azure (147) that flag idle, oversized, and orphaned resources, and each recommendation shows the current monthly cost next to the estimated optimized cost so you act on the largest first. 124 of those recommendations are wired to act end to end, 28 one-click and 96 guided: one-click actions run immediately behind an admin-approval gate, and guided actions add a type-to-confirm review so you check the change before it lands. You mark a recommendation applied once you act, or dismiss the ones that do not fit.

Idle detection reads CPU, network, and connection metrics over a rolling window to separate a genuinely idle resource from one with real but intermittent traffic. Rightsizing is guided and computed from measured utilization over a real window, never a flat 24/7 assumption, so the projected figure matches the bill you actually see. For steady-state fleets, VM autoscaling runs in one of three modes derived from the credential’s permissions: monitor, recommend, or autopilot.

What is left after optimization gets attributed rather than hidden. Showback splits shared cost across owning teams and rolls up by cloud tag, GCP label, or Azure tag, with a Sankey cost-flow view that traces spend across provider, account, type, and team and a savings overlay that points straight at the reclaimable flows. A daily anomaly job writes root-cause markers onto the cost trend, an instance resize, a new resource, a reservation expiry, a failed schedule, so a spike explains itself instead of prompting a manual hunt. And 43 read-only tools expose the same data to an AI assistant over MCP, so you can ask an assistant in Claude, Cursor, or Codex for the same numbers.

Best practices that keep the savings

A few habits separate teams that hold onto the savings from teams that watch them drift back:

• Start with non-production and prove it there. Development, staging, QA, and demo environments carry almost no risk and the largest idle share, so they are the right place to build confidence before anyone considers production.
• Schedule by group, not by hand. Bundling an environment into a group like “staging” means one cadence covers every resource in it, and resources you add later inherit the schedule instead of being quietly forgotten.
• Use overrides instead of disabling schedules. When a late deploy needs a box overnight, a time-boxed override with a written reason keeps the schedule intact and expires on its own, so a one-off exception never becomes a permanent leak.
• Watch the audit trail and notifications. Every start, stop, and failure is logged and can post to Slack, Teams, or Google Chat, so a failed action is visible the moment it happens rather than discovered on the next invoice.
• Treat it as an operating rhythm, not a cleanup. The teams that keep the bill down review recommendations on a cadence and let the automation run continuously, instead of a one-off spring-clean that snaps back the moment attention moves on.

Getting started

Getting started is intentionally low-stakes:

• Connect your cloud provider with a read-only role. Nothing is scheduled or changed at this stage.
• Let ZopNight discover your non-production resources and review exactly what it found, filtered by account, region, and status.
• Create a schedule in your timezone and attach the non-production resources or groups you want it to cover.
• Watch the first cycle run, with Slack, Teams, or Google Chat notifications on every start, stop, and failure, then layer in idle cleanup and guided rightsizing.

Production stays excluded by default throughout, and because discovery and recommendations are read-only, you can prove the value before you enable a single action.