Teams expanding to Google Cloud or migrating workloads from Amazon Web Services (AWS) ask the same question about cost: Where are the Reserved Instances?
The Google Cloud platform doesn’t have them, at least not by that name. It has Committed Use Discounts (CUDs).
While they achieve the same goal (lower hourly rates in exchange for a usage commitment), the mechanics are different enough that the AWS RI mental model can lead you astray.
Many teams treat CUDs like RIs, applying the same flexibility assumptions, coverage logic, and management cadence to a different type of pricing model. They end up overcommitting to resource-based CUDs when spend-based is a better fit, or undercommitting if the mapping doesn’t feel clean. Sometimes both happen within the same fiscal year.
Let’s break down how CUDs work, where they differ from AWS Reserved Instances, how to measure whether your strategy is delivering savings, and where autonomous commitment management fits once you have the fundamentals down.
Key takeaways
- Google Cloud doesn’t offer Reserved Instances by name, but committed use discounts serve a similar purpose: lowering rates in exchange for a usage commitment.
- Treating Google Cloud commitments as a direct copy of AWS Reserved Instances leads to weaker decisions around flexibility, coverage, and commitment risk.
- Spend-based and resource-based committed use discounts support different optimization goals, so the right option depends on workload predictability and governance needs.
- Visibility and budgeting are foundational, but they don’t improve the Effective Savings Rate on their own without an execution strategy for discount instruments.
- Manual commitment management gets harder as environments change, which is why automation matters for maintaining savings and reducing risk.
- ProsperOps autonomously manages commitment-based discount instruments, fitting into the optimization stack after visibility and governance are in place.
What Google Cloud uses instead of Reserved Instances
AWS Reserved Instances apply a billing discount when you commit to a specific instance type or family for one or three years. Standard RIs offer up to 72% off on-demand rates with less flexibility; Convertible RIs trade some of that discount for the ability to exchange across instance types, families, and operating systems.
Google Cloud’s committed use discounts operate on the same basic premise: commit to a level of usage, get a lower rate. But the structure differs. CUDs reduce your per-unit cost for eligible compute resources (vCPUs, memory, specific machine types) based on the type of commitment you buy. You pay monthly at the discounted rate, with no upfront payment.
CUDs are billing discounts, not capacity reservations. Guaranteed resource availability in a specific data center zone requires a separate reservation. This is a minor detail for most use cases, but for stateful apps with low latency requirements, capacity planning helps mitigate that risk.
(For a broader comparison of how these platforms approach cost and architecture, see Google Cloudvs. AWS.)
| AWS Reserved Instances | Google Cloud CUDs | |
| Commitment types | Standard, Convertible | Resource-based, Spend-based |
| Commitment lengths | 1 or 3 years | 1 or 3 years |
| Payment structure | No upfront, partial, or full upfront | No upfront; monthly billing |
| Capacity reservation | Zonal RIs guarantee capacity | No guarantee by default; requires separate reservation |
| Discount depth | Up to 72% (Standard RI, varies by instance family and term) | Up to 70% (memory-optimized machine types) |
| Scope | Instance family, region, or AZ | Machine series and region (resource-based); global (spend-based) |
How Google Cloud’s committed use discounts work
When you buy a CUD, you’re committing to a minimum level of resource usage, measured in vCPUs, memory, or spend, for the duration of the term. Google applies the discount automatically to eligible usage in your billing account or project. You don’t select instances ahead of time or reserve capacity.
Choosing a CUD type is less about technical specs and more about your confidence in the workload’s long-term behavior:
- Have a workload running on a specific machine family in a specific region for 12+ months with no planned migration or instance type change? Choose resource-based CUD.
- Is the DevOps or engineering team actively modernizing, experimenting with machine types, or moving workloads between regions? Choose spend-based CUD.
- Have a steady-state baseline with growth on top? Choose resource-based for the baseline; choose spend-based for the variable layer.
- Expecting architecture uncertainty in the next 12 months? Choose spend-based and wait until things stabilize before going deeper.
Committed use discounts vs. AWS Reserved Instances
| AWS Reserved Instances | Google Cloud CUDs | |
| Commitment model | Instance-based or family-based | Resource-based or spend-based |
| Flexibility | Convertible RIs allow exchange without same family | Spend-based CUDs apply across machine series and regions |
| Payment options | No upfront, partial, or full upfront | No upfront only |
| Capacity implications | Zonal RIs include capacity reservation | No capacity guarantee; requires separate reservation |
| Ideal use cases | Stable compute with known instance requirements | Stable workloads (resource-based); evolving architecture (spend-based) |
Google Cloud’s model offers more flexibility in certain dimensions. Spend-based CUDs float across machine series and regions, unlike Convertible RIs.
Greater flexibility still carries commitment risk. You can still overcommit or undercommit; the mechanism just looks different. A spend-based CUD purchased at the wrong coverage level, or right before a migration that drops your eligible usage, still creates waste.
What are the types of committed use discounts?
| Spend-Based CUDs | Resource-Based CUDs | |
| Discount depth | ~28% (1-yr)/~46% (3-yr) for eligible Compute Engine | Up to 57% for most machine types; up to 70% for memory-optimized |
| Scope | Global across eligible cloud services (Compute Engine, GKE, Cloud Run) | Specific machine family and region |
| Commitment risk | Lower: applies across broader usage | Higher: tied to a specific resource group |
| Best for | Evolving workloads, multi-region footprints, mixed service usage | Stable, predictable compute baselines |
Your resource-based CUDs are your bedrock, and spend-based CUDs are your agility layer. Resource-based commitments offer the deepest discounts, but they’re tied to specific machine families in specific regions. Any meaningful change to that workload creates a mismatch problem.
Spend-based commitments give up discount depth in exchange for the ability to float across services and regions, which is worth it when your architecture is still in motion.
Spend-based CUDs
Spend-based CUDs commit to a minimum hourly spend amount on eligible services rather than a specific resource configuration. They apply at the Cloud Billing account level with no restriction to a single region or machine series, which means a workload migration or machine type change doesn’t automatically strand your commitment.
A one-year spend-based CUD usually delivers around 28% off eligible usage; a three-year lands around 46%.
- If the workload is stable enough to hold a resource-based commitment, you’re leaving a discount on the table by defaulting to spend-based.
- If your engineering team is mid-modernization or shifting workloads, spend-based reduces your risk of buying the wrong thing.
Resource-based CUDs
Resource-based CUDs are tied to specific vCPU and memory configurations within a defined machine series and region. Discount depth is much higher: up to 57% for most machine types, and up to 70% for memory-optimized machine types like M2. That specificity creates greater mismatch risk if anything changes.
- If an N2 workload in us-central1 has been stable for a year with no planned changes, it’s a strong candidate for a resource-based CUD you can purchase via the Google Cloud console or API.
- If there’s a node pool migration on the roadmap, a planned move to a different machine series, or any active modernization work, the better call is to wait until you’ve completed the work and are committing to a stable new baseline.
How to measure whether your commitment strategy is working
Effective Savings Rate (ESR) measures the overall discount achieved across your total committed cloud spend, not the advertised rate of any single discount instrument across cloud providers. You can buy a resource-based CUD at 70% off and still deliver a weak ESR if coverage is low, utilization is poor, or a significant chunk of eligible spend is running on-demand.
ESR accounts for the full picture: coverage of your eligible spend and the utilization of your commitment. It’s a more actionable metric than headline discounts for benchmarking cloud computing cost efficiency or evaluating whether a strategy impacts your bottom line.
For context: According to ProsperOps’ ESR benchmarking data across approximately $3 billion in AWS compute spend, the median organization achieves an ESR of around 15%, and only the top 2% exceed 40%. Google Cloud optimization presents similar dynamics. The gap between median and top-percentile performance isn’t discount availability, but the consistency of execution as environments change.
The over-commitment vs. under-commitment trade-off
Overcommitting creates waste. Undercommitting means eligible usage runs at on-demand prices. A balance between discount coverage and flexibility matters more than maximum commitment.
Many teams default to one of two postures: high coverage with high lock-in risk (buy 3-year CUDs and hope the workload holds) or low coverage to avoid risk (stay mostly on-demand and pay the premium). Neither fits an actively changing environment.
Quantifying that tradeoff requires a risk metric alongside ESR. Commitment Lock-In Risk (CLR) measures the maximum weighted average duration of your active commitment portfolio in months, or essentially, how long you’re financially exposed to your current commitment strategy.
A portfolio of three-year resource-based CUDs might deliver a high ESR today, but carries three times the CLR of an equivalent portfolio built on one-year commitments. If a migration, architecture change, or workload retirement makes those commitments obsolete mid-term, CLR tells you exactly how long you’ll be carrying that exposure.
The goal is to find the highest ESR achievable at an acceptable CLR for your organization’s pace of change. That balance looks different for a stable SaaS company than for a team mid-migration or scaling through a hypergrowth event.
Commitment optimization is ongoing, not a one-time decision
A commitment portfolio calibrated in January can be off by Q3. This doesn’t always mean something went wrong operationally: businesses grow, architecture decisions change the footprint, and migrations shift usage to different services. Teams that maintain high ESR over time treat commitment management as a continuous process. The ones that don’t face the music at renewal time.
Why manual commitment management breaks down
Buying commitments is the easy part. Maintaining the right level of commitment as workloads shift is much harder. And these problems get worse during the business events most likely to drive a Google Cloud investment.
Forecasting is imperfect in dynamic environments
All FinOps teams, including experienced ones, struggle to predict usage closely enough to manage commitments by hand. A company migrating workloads to Google Cloud, scaling infrastructure to support new demand, or absorbing new infrastructure through an acquisition has an unstable usage baseline.
Commitments purchased before a major migration cutover may reflect an outdated footprint. Commitments sized during a scaling event can fail to cover a baseline that grows by the next review cycle. This is a structural limitation of point-in-time forecasting applied to an environment that never stands still.
Recommendations are not the same as execution
Cloud cost management tools provide recommendations, but they don’t act on them. Teams that review commitments monthly are optimizing for the previous month; by the time a recommendation is prioritized, reviewed, and executed, the underlying usage patterns may have already shifted.
Execution speed and consistency determine whether you can capture savings opportunities. Manual processes don’t hold up when workloads change.
When continuous management replaces periodic reviews
The structural limitation of manual commitment management isn’t effort, it’s cadence. Monthly or quarterly reviews optimize for the state of your environment at review time, not at execution time.
In a Google Cloud environment where workloads shift between machine families, regions, and services, the gap between review cycles is where coverage decays and commitment risk accumulates.
Autonomous optimization closes that gap by managing commitment portfolios by continuously adjusting coverage as usage changes rather than waiting for a human review cycle to catch drift after the fact. The commitment portfolio stays calibrated to the actual workload, not to a snapshot taken last quarter.
Where ProsperOps fits in the Google Cloud optimization workflow
Once you have visibility, budgeting, and governance in place, you still have to tackle execution. That means adjusting commitments as workloads evolve. Autonomous optimization addresses that execution layer by managing commitment-based discount instruments continuously, freeing engineering and finance teams to focus on strategic decisions.
ProsperOps operates at that layer, continuously managing the commitment portfolio rather than making a recommendation and pausing for approval. It monitors usage signals, including instances spinning up or down, workload shifts, and service mix changes, and adjusts the commitment portfolio in one-to-two-hour windows.
ProsperOps manages this through Autonomous Discount Management for Cloud SQL on Google Cloud, specifically an adaptive laddering strategy that constructs a continuously rebalanced mix of short- and long-term commitments calibrated to real-time usage signals.
Short-duration commitments cover the dynamic layer of your workload; longer-duration commitments cover the stable baseline. When usage changes, the short-duration layer absorbs the shift without stranding long-term exposure. The result is high ESR paired with actively managed CLR rather than a static portfolio that looks correct at purchase time and drifts from there.
ProsperOps is available on the Google Cloud Marketplace, enabling procurement through existing GCP spend commitments.
Turn Google Cloud commitments into a savings strategy
Getting a great discount rate on Google Cloud is a solid start, but it’s a vanity metric if your coverage and utilization are slipping.
A commitment portfolio is a living document, not a purchasing decision. The Google Cloud environments that consistently achieve top-percentile ESR aren’t the ones that bought the right CUDs in January, they’re the ones that kept their portfolios aligned with reality in March, June, and September as workloads shifted underneath them.
Manual management can hold up in a stable environment. The question worth asking honestly is whether your Google Cloud environment will still look the same in six months. Migrations, scaling events, machine family changes, and architecture modernization all create commitment drift, and drift is expensive.
If you’re managing GCP commitments today and want to see what continuous autonomous optimization changes about your ESR and CLR, request a free Savings Analysis from ProsperOps.
FAQs
Does Google Cloud have Reserved Instances?
Google Cloud does not use the term Reserved Instances in the same way AWS does.
Instead, it offers committed use discounts, or CUDs, which provide lower rates in exchange for a usage commitment over time. They serve a similar cost optimization purpose, but the pricing model, commitment terms, and flexibility differ.
What is the difference between Google Cloud CUDs and AWS Reserved Instances?
Both reduce costs by rewarding predictable usage, but they’re structured differently. AWS Reserved Instances are tied to AWS pricing constructs, while Google Cloud committed use discounts are based on Google Cloud’s commitment models, including spend-based and resource-based options.
Google Cloud’s model offers more flexibility in certain dimensions. Spend-based CUDs float across machine series and regions, unlike Convertible RIs.
Are Google Cloud committed use discounts risky?
They can be if teams commit without understanding workload stability and future usage patterns. The main risk is paying for commitments that no longer match actual consumption.
That’s why FinOps teams evaluate coverage, utilization, and Effective Savings Rate instead of focusing only on headline discount percentages.
What is Effective Savings Rate in cloud cost optimization?
Effective Savings Rate measures the overall discount a team achieves across its committed cloud spend. It’s a more practical metric than looking at advertised discount percentages alone because it reflects how well a commitment strategy performs in the real world, including utilization and coverage outcomes.
Do budgeting and visibility tools optimize committed use discounts?
Not by themselves. Visibility and budgeting tools help teams understand spending, assign accountability, and forecast costs, but they don’t usually manage commitment-based discount instruments automatically. Optimization still requires execution, whether that’s done manually by a team or autonomously through a platform focused on rate optimization.
