Google Cloud CUD vs AWS Savings Plans
Full Enterprise Comparison 2026

How Google Cloud CUDs Work

Google Cloud Committed Use Discounts come in two flavours: resource-based and spend-based. Resource-based CUDs lock you into a specific vCPU and memory configuration per region, offering discounts up to 55% on standard compute and a remarkable 70% on memory-optimised instances for three-year commitments. Spend-based CUDs work differently — you commit to a dollar spend floor in a specific region and service family (compute, BigQuery, Cloud SQL, etc.), earning a smaller but more flexible 25% discount on three-year terms. This distinction matters enormously for your effective savings rate. If your workload footprint is stable and predictable, resource-based CUDs win. If you're still optimising or scaling unevenly across machine types, spend-based offers safer optionality.

The key advantage that separates GCP from AWS is automatic sustained-use discounts. Google applies these automatically — up to 30% off — for any compute instance running consistently, with no commitment required. This is not an opt-in; it's automatic. Most enterprise buyers overlook this when comparing gross CUD rates to AWS Savings Plans, but the cumulative benefit is substantial. A 3-year resource-based CUD at 55% stacks with zero additional friction. You don't negotiate sustained-use discounts; you simply benefit from them.

One structural constraint: CUDs are per-project and per-region. If you run the same workload across five GCP projects or two regions, you need separate CUD commitments for each. This lack of flexibility is a real cost driver — many enterprises overshoot commitment sizes because they can't pool. You can layer these constraints into a Google Cloud CUD negotiation conversation with your account team to explore whether they'll offer flexibility on regional portability, though the answer is usually no. Explore what's possible with a Private Pricing Agreement to see if multi-project consolidation is negotiable.

How AWS Savings Plans Work

AWS offers three commitment vehicles: Compute Savings Plans (up to 66% discount, most flexible), EC2 Instance Savings Plans (up to 72% discount, instance-family locked), and Reserved Instances (RIs). Compute Savings Plans apply across instance families, operating systems, and regions within a family — this is AWS's most buyer-friendly option because you can shift workload mix and still capture the discount. EC2 Instance Savings Plans go deeper (72% ceiling) but demand instance-family specificity; if you buy m6i commitments and shift to m7i, you lose the coverage. Reserved Instances are an older vehicle with similar structures, and convertible RIs let you change families mid-term (down to 66% discount) for that optionality.

The critical trap: AWS's shortfall risk. When you commit to a Compute Savings Plan, you're committing to an hourly spend. If your actual spend falls below that commitment, you still pay the shortfall at a gap rate (typically 10–15% of On-Demand pricing). This is not how Google CUDs work. With GCP, you commit to specific vCPUs and memory allocations; you bill per resource, not per spend floor. The risk geometry is fundamentally different. AWS has trained enterprise buyers to expect shortfall risk as normal; it is not. Redress typically sees 8–12% shortfall waste on first-time AWS commitment deals, and many enterprises remain unaware of the risk until the true-up report arrives.

AWS also allows Marketplace purchases to offset EDP and PPA commitments by up to 25%, which opens a secondary negotiation pathway if your vendor stack includes SaaS or third-party tools running on AWS. Multi-cloud stack considerations make this relevant — factor Marketplace offsetting into your GCP vs AWS cost model if you're building a hybrid footprint.

Side-by-Side: The Key Differences That Matter for Enterprise Buyers

On raw discount ceiling, AWS edges ahead: 72% on EC2 Instance Savings Plans versus GCP's 70% on memory-optimised CUDs. But this comparison ignores GCP's automatic sustained-use discounts. If your baseline workload qualifies for sustained-use, your effective rate climbs — 70% CUD plus 30% sustained-use (non-stacking, so you get whichever is higher at any point) means you're often seeing 70%+ effective savings without additional commitment negotiation. AWS does not offer an equivalent automatic discount; you must earn Savings Plans or RIs explicitly.

Flexibility is where AWS Compute Savings Plans dominate. You can flex across regions, instance families, and operating systems within a Compute family. GCP resource-based CUDs are per-region, per-vCPU configuration, per-project. If your architecture drifts — common in enterprise cloud transformation — you may find yourself overhauling CUD commitments. For multi-cloud buyers, this rigidity is a dealbreaker. GCP spend-based CUDs offer more play, but at only 25% discount, they're often less attractive than resource-based.

Stackability is GCP's sleeper advantage. Google's PPAs (Private Pricing Agreements) stack on top of CUDs. An enterprise that locks in a 40% PPA for non-committed usage can layer CUDs on committed workloads for cumulative benefits. AWS typically does not work this way — an EDP or PPA replaces a Savings Plan rather than stacking. For multi-cloud benchmarking, this stackability property means GCP's effective floor is often higher than AWS's, even if AWS's ceiling is slightly higher.

Data transfer and egress costs are invisible in most Savings Plan comparisons, but they shouldn't be. AWS is the most expensive major cloud for egress — customers regularly report egress costs adding 8–15% to effective total cost of ownership. GCP's egress pricing is approximately 40% lower. When you model total cloud cost, not just compute, this spread widens significantly. Factor egress modelling into your AWS vs GCP comparison; it often tips the business case toward GCP even if raw compute Savings Plan rates look better.

For GenAI workloads and Workspace deployments, GCP's commitment discounts often extend across service families in a PPA negotiation. AWS's Savings Plans are compute-focused and don't flex into SaaS or managed AI services the same way.

Which Delivers More Savings for Your Enterprise?

The honest answer: it depends entirely on your workload stability, region footprint, and negotiation leverage. If 80% or more of your public cloud spend is Google Cloud and your compute footprint is stable (same instances, same regions, same project structure for 12+ months), resource-based CUDs are your path. You can realistically hit 40–54% effective spend reduction rate (ESR) when you layer in sustained-use discounts, stack them with a PPA for overages, and clean up any regional arbitrage. This is what world-class GCP buyers achieve.

If your estate is split between AWS and GCP — say 60% AWS, 40% GCP — model both clouds independently. This is critical: do not let AWS account teams tell you that switching to GCP will "lose" you existing Savings Plan commitments and therefore isn't worth considering. Each cloud's math should stand on its own. Your AWS business case should show what Compute Savings Plans and potential Marketplace offsets yield. Your GCP case should show resource-based CUDs plus sustained-use plus PPA ceiling. Then compare the net costs, not the gross rates.

The biggest mistake we see: enterprises treating CUDs and Savings Plans as interchangeable levers, when they're structurally different. One is per-resource with automatic stackable discounts. The other is per-spend with shortfall risk and less flexibility. The better choice isn't determined by comparing the headline percentages. It's determined by how stable your workload is, whether you can afford multi-year lock-in, and whether your vendor (Google or AWS) will negotiate additional commercial terms like PPAs or flexible regional pooling.

Redress's standard recommendation: benchmark both clouds independently before you sign anything. Most enterprise buyers — even sophisticated FinOps teams — underestimate what's negotiable on both sides. Google will discuss regional flexibility, project consolidation, and stackability if you have serious volume. AWS will discuss Savings Plan term structure, Marketplace integration, and support plan bundling. Go in informed, modelled, and benchmarked.