Google Cloud committed use discounts reach up to 70 percent on three-year resource-based commitments, while AWS Compute Savings Plans top out near 66 percent but apply across any instance family, region, and operating system. GCP also adds sustained use discounts of up to 30 percent automatically with no commitment. The comparison turns on whether you value GCP's deeper locked discount and free baseline, or AWS's consistent portability, and for most enterprises running both clouds the real question is how to commit on each without over-committing across the portfolio.
Two different commitment philosophies
AWS and Google Cloud solve the commitment-discount problem differently, and the difference is philosophical, not just numerical. AWS Compute Savings Plans commit to a dollar-per-hour spend and apply automatically across EC2, Fargate, and Lambda regardless of configuration. AWS makes flexibility the default and charges a small discount premium for it. Google Cloud offers two models: resource-based CUDs that lock to a machine family and region for the deepest discount, and spend-based (flexible) CUDs that behave more like Savings Plans across families and regions.
The structural difference is that AWS makes portability the standard product and prices the lock as the exception, while Google offers a deeper discount for accepting the lock and a shallower one for flexibility. Google also gives a sustained use discount for free, applied automatically to long-running Compute Engine instances, which AWS has no direct equivalent for. The GCP side is detailed in our Google Cloud licensing guide and the AWS side in AWS Savings Plans.
Discount depth compared
| Instrument | Max 3-year discount | Flexibility | Commitment unit |
|---|---|---|---|
| GCP resource-based CUD | up to 70% (memory-optimized) | Locked family and region | vCPU and memory quantity |
| GCP spend-based (flexible) CUD | up to 46% | Across families and regions | Dollar-per-hour spend |
| GCP sustained use | up to 30% (automatic) | No commitment | None |
| AWS Compute Savings Plan | up to 66% | Any family, region, OS | Dollar-per-hour spend |
| AWS EC2 Instance Savings Plan | up to 72% | Within a family and region | Dollar-per-hour spend |
The headline numbers are not directly comparable until you net out GCP sustained use, which a workload earns for free before any commitment. A GCP resource-based CUD must beat the sustained use discount the workload already receives to deliver net value, so its effective incremental discount is smaller than the headline 70 percent suggests. The honest comparison is incremental discount over the no-commitment baseline, not list-versus-list.
To make the comparison fair, model the effective rate, not the headline. A continuously running GCP general-purpose instance already earns roughly 20 to 30 percent through sustained use. A resource-based CUD on that instance raises the discount further, but the incremental gain over the free baseline is what the commitment actually buys. On AWS there is no free baseline, so the full Savings Plan discount is incremental from on-demand. Comparing GCP's incremental CUD gain against AWS's full Savings Plan discount is the only apples-to-apples view.
Flexibility and stranded commitment
AWS Compute Savings Plans and GCP flexible CUDs both follow the workload across configurations, eliminating stranded commitment for compute. GCP resource-based CUDs and AWS EC2 Instance Savings Plans both lock to a family, carrying stranded-commitment risk if the workload re-architects. The pattern is identical across clouds: the deepest discount requires the tightest lock, and the tightest lock carries the highest stranding risk.
The portability difference that matters at the margin is regional. AWS Compute Savings Plans apply across regions automatically, while GCP flexible CUDs apply across regions but resource-based CUDs are region-bound. For a multi-region estate that shifts load geographically, the AWS model and the GCP flexible model both preserve coverage that a GCP resource-based commitment would strand. The same locked-versus-flexible trade-off appears within a single cloud in our AWS Savings Plan vs Reserved Instances comparison.
Neither cloud allows cancellation of a commitment, so over-committing a baseline that later shrinks strands spend on both. The discipline is identical regardless of cloud: commit only the floor you are confident persists, cover the variable layer with the most flexible instrument, and revisit coverage as the workload changes.
How each handles coverage and waste
AWS Savings Plans apply automatically to the highest-rate eligible usage first and cascade down, so a single spend commitment covers whatever runs, maximizing utilization without manual allocation. Unused Savings Plan commitment in an hour is simply lost for that hour, but because the discount floats to wherever usage is, well-sized commitments rarely waste. The buyer manages a dollar-per-hour number, not a fleet of reservations.
GCP resource-based CUDs behave differently. A resource-based CUD applies to matching usage in its region and machine family; if the matching usage drops below the committed quantity, the unused commitment still bills. This makes GCP resource-based CUDs more like AWS Standard Reserved Instances in their waste profile, rewarding precise sizing and punishing over-commitment. GCP flexible CUDs and AWS Savings Plans share the more forgiving spend-based profile. The lesson is that the waste characteristics track the lock characteristics: the deeper, more locked instrument on either cloud demands more precise sizing.
Beyond compute: services and AI
Neither comparison stops at compute. GCP CUDs extend beyond Compute Engine to services such as Cloud SQL, and spend-based CUDs can cover broader service categories, while AWS Savings Plans cover EC2, Fargate, and Lambda but not the full service catalog. For an estate whose spend concentrates in managed databases, analytics, or AI inference, the question of which services each instrument covers can matter more than the headline compute discount.
AI workloads sharpen this. Both clouds now push committable AI consumption, Vertex AI on Google and Bedrock and SageMaker on AWS, and both will fold qualifying AI spend into the enterprise discount or a service-specific commitment. A buyer with growing, predictable model-inference spend should treat it as committable rather than leaving it at on-demand, on whichever cloud it runs. The commitment instruments compared here are the compute floor; the AI and managed-service layers are negotiated alongside them under the enterprise agreement.
Management overhead compared
Operationally, AWS Savings Plans and GCP flexible CUDs are low-maintenance: commit a spend number, let the discount float, review coverage periodically. GCP resource-based CUDs and AWS Standard Reserved Instances demand more, because the commitment is tied to a configuration that the estate must keep matching. For a small platform team, the lower-maintenance spend-based instruments are often worth their slightly shallower discount simply because they are harder to get wrong.
The cross-cloud reality is that most FinOps teams standardize on the spend-based instrument as the default on both clouds, reserving the locked, resource-based commitments for the narrow set of large, stable, well-understood workloads where the extra discount justifies the extra management. That single policy, applied consistently across AWS and GCP, captures most of the available discount while keeping the commitment portfolio manageable.
The multi-cloud reality
Most enterprises run both clouds, so the practical question is not which model is better in the abstract but how to commit on each without over-committing across the portfolio. The risk in a multi-cloud estate is double-committing a workload that could move between clouds, leaving stranded commitment on the cloud it leaves. A workload mid-migration from AWS to GCP should not carry a fresh three-year commitment on the cloud it is leaving, yet sales pressure on both sides pushes exactly that outcome.
The buyer-side approach sizes commitments per cloud against the workload genuinely anchored there for the full term, treats workloads in motion as on-demand until they settle, and keeps a portfolio view so the sum of commitments never exceeds the floor the business will actually run. This portfolio discipline, covered alongside the pricing comparison in AWS vs Azure vs GCP pricing, is what separates a multi-cloud estate that captures discount from one that strands it.
The verdict: GCP CUD or AWS Savings Plan
Favor GCP committed use discounts when your workload is anchored on Google Cloud, runs on stable machine families in known regions, and uses expensive specialized instances where the resource-based discount reaches its deepest. The free sustained use discount also makes GCP attractive for continuously running workloads that earn the automatic reduction before any commitment, and the flexible CUD covers the changing remainder.
Favor AWS Compute Savings Plans when your workload is anchored on AWS, your estate is changing or multi-region, and you value consistent portability across families, regions, and operating systems. For the locked AWS baseline, EC2 Instance Savings Plans match GCP resource-based depth. In both clouds, the rule holds: commit the stable baseline deep, cover the variable middle flexibly, net out any free baseline before comparing rates, and size every commitment against real utilization, the discipline in our cloud contract negotiation practice.
For more, see the Google Cloud licensing guide, GCP committed use discounts, AWS Savings Plans, AWS Savings Plan vs Reserved Instances, and the Google Cloud vendor hub.
The bottom line on cloud commitments
The GCP CUD versus AWS Savings Plan comparison resolves to a small set of consistent rules that hold on both clouds. The deepest discount always requires the tightest lock, and the tightest lock always carries the highest stranding risk, so the locked, resource-based instruments belong only on the large, stable, well-understood workloads that justify their precise sizing. Everything else belongs on the flexible, spend-based instrument, where a slightly shallower rate buys portability and forgiveness.
The honest comparison is never list-versus-list. On GCP the free sustained use discount must be netted out before a commitment is valued, because the commitment only buys the incremental gain over a baseline the workload already earns. On AWS the full Savings Plan discount is incremental from on-demand. Comparing the two on headline numbers, without that adjustment, systematically overstates the GCP commitment's value and leads buyers to over-commit on the locked instrument.
For the enterprise running both clouds, the discipline is portfolio-level: size each commitment to the workload genuinely anchored on that cloud for the full term, treat anything in motion as on-demand until it settles, and never let the sum of commitments exceed the floor the business will actually run. Layer the per-cloud enterprise discount on top, negotiated against a credible alternative, and the result captures the deepest safe discount on each cloud without stranding spend across them.
That portfolio view is what most internal teams miss, because commitments are bought cloud by cloud, often team by team, with no single owner reconciling the total against the business floor. An independent review that builds the one consumption forecast underneath both clouds, then sizes commitments and negotiates both enterprise discounts against it, routinely finds both uncovered on-demand spend and stranded commitment in the same estate, and corrects both at once.
One last practical note: commitment instruments on both clouds are bought, not granted, so the buyer controls the timing. Aligning a commitment purchase with the enterprise-agreement negotiation, rather than buying it reactively when an on-demand bill spikes, keeps the whole discount stack in one conversation and prevents the piecemeal commitments that quietly accumulate into a stranded portfolio.