In our previous post, we talked about what happens when your cloud bill is on fire: you buy a savings plan, the bill drops, everyone celebrates, and nothing actually gets fixed.
But we didn't fully answer the question: why do savings plans exist? And more importantly, when do they actually make sense?
Most engineering leaders view cloud savings plans as a cost optimization tool. They're not. They're a risk transfer mechanism that benefits the cloud provider far more than it benefits you. But that doesn't mean they're always wrong.
Understanding this distinction changes everything about how you approach commitment pricing. Let's break down the economics so you can make informed decisions rather than reactive ones.
1. Cloud pricing is designed for provider stability, not customer efficiency
Cloud providers operate one of the most capital-intensive businesses in technology. AWS, Azure, and Google Cloud spend billions on data centers, servers, networking equipment, and power infrastructure. These are fixed costs that don't care about your usage patterns.
Revenue predictability matters more than you think. When AWS reports earnings, Wall Street doesn't want to hear "usage was volatile this quarter." They want predictable, recurring revenue. Your savings plan commitment gives them exactly that: guaranteed cash flow regardless of whether you actually use the capacity.
The capital intensity of infrastructure creates natural volatility. Cloud providers spend billions on data centers that must be paid for whether you use the capacity or not. Commitment pricing transfers utilization risk from them to you. They get guaranteed revenue, you get guaranteed costs.
Why volatility is expensive for providers. Cloud providers need to maintain enough capacity to handle peak demand, but most customers don't use that capacity consistently. This creates expensive idle resources. Commitment pricing solves this for them: you pay for capacity whether you use it or not, thereby transforming their variable utilization problem into your fixed-cost problem.
From their perspective, AWS Savings Plans, Azure Reserved Instances, and Google Cloud Committed Use Discounts aren't discounts. They're revenue-stabilization tools that also come with a price reduction.
2. The rise of commitment-based cloud pricing
Cloud pricing hasn't always worked this way. Early AWS was purely pay-as-you-go. But as the business matured, the economics demanded more predictability.
Reserved Instances came first. Commit to a specific instance type in a specific region for 1-3 years, and get up to 72% off. Simple. Rigid. Effective at locking in revenue.
Savings Plans evolved as a more flexible model. Instead of committing to a specific instance type, you commit to a per-hour rate. AWS Compute Savings Plans offer up to 66% off with maximum flexibility. EC2 Instance Savings Plans offer up to 72% off with some restrictions. The flexibility is real, but the commitment is still absolute.
Spot markets exist as the counterbalance. Spot instances are the cloud provider's way of monetizing excess capacity. You can save up to 90% off on On-Demand pricing, but you're buying preemptible capacity. This is genuine price discovery: supply and demand in real time. But most enterprises can't architect for this volatility, so they end up in the commitment model instead.
The pattern is clear: cloud providers want predictability. They'll offer steep discounts to get it. The question is whether that trade makes sense for you.
3. The psychology of "instant savings"
Remember the scenario from our last post? Your cloud bill spikes 40% in a single month. Panic sets in. Someone pulls up a savings plan calculator, sees potential savings of 30%, and within a week, you've made a commitment.
This happens thousands of times a day across engineering organizations. Let's talk about why.
Budget panic cycles are real. When your CFO asks why infrastructure costs jumped, "we're working on it" isn't good enough. A savings plan gives you something concrete to report: "We locked in a 30% discount on compute." Crisis averted.
Executive optics matter more than substance. From a board perspective, purchasing a savings plan appears to be a decisive action. The bill drops. Leadership feels progress. No one asks whether the underlying usage was justified.
Why commitments feel like progress. There's a psychological satisfaction in seeing a number go down. Your $100K monthly compute bill becomes $70K. You "saved" $30K. The fact that you might only need $50K of that compute? That question doesn't get asked because everyone's celebrating the discount.
This is why savings plans sell so well, even though they are often suboptimal. They provide immediate relief from budget pressure without requiring the hard work of actually understanding where your money goes.
4. Commitments vs utilization reality
Here's the uncomfortable truth: buying a savings plan doesn't eliminate overprovisioning. It just changes who bears the cost of inefficiency.
Overprovisioning doesn't disappear. As we explored in Kubernetes as an economic system, engineers overprovision because incentives reward it. The career risk of an outage is immediate and high. The cost of wasted capacity is delayed and hidden. A savings plan doesn't change these incentives. It just makes you pay upfront for the waste.
Waste becomes prepaid. When you commit to $50K/month in compute and only use $35K, you're not "wasting" $15K in the traditional sense. You already paid for it. The waste is baked into your fixed costs. Azure's documentation is explicit: reservation discounts are "use-it-or-lose-it." Unused capacity in any hour is lost forever.
Risk moves to the customer. With On-Demand pricing, the cloud provider bears utilization risk. If you don't use capacity, they don't get paid. With commitments, that risk transfers to you. If your architecture changes, if your product pivots, if your usage patterns shift—you're still on the hook for the commitment. The provider gets paid regardless.
This is why understanding which workloads waste the most matters so much. You need to know your actual utilization before committing to pay for theoretical capacity.
5. Cloud mirrors every other infrastructure market
Cloud pricing isn't unique. It follows the same economic patterns as every other infrastructure market.
Energy contracts work identically. Large industrial customers commit to minimum electricity usage in exchange for lower rates. The utility gets predictable revenue. The customer gets predictable costs. But if your factory shuts down? You still pay.
Telecom operates the same way. Buy a long-term bandwidth contract, and get discounted rates. But unused bandwidth doesn't roll over. The infrastructure is built whether you max it out or not.
Transportation capacity follows this model. Airlines commit to leasing aircraft for years at fixed rates. Shipping companies lock in vessel capacity. The asset (plane, ship, server) has fixed costs regardless of utilization.
Cloud as an economic system. The pattern is universal: capital-intensive infrastructure businesses use commitment pricing to stabilize revenue and reduce risk. The cloud is simply applying centuries-old economic principles to compute resources. Understanding this helps you see savings plans for what they are: a capital markets tool, not an optimization strategy.
6. When Savings Plans actually make sense
So if savings plans are a risk transfer mechanism that mostly benefits cloud providers, why would you ever use them?
Because sometimes the trade makes sense. The problem isn't savings plans themselves. It's treating them as a substitute for optimization rather than a complement to it.
Here's when they make sense:
Stable, predictable workloads. Your production database runs 24/7. Your core API services have consistent traffic. These workloads aren't going anywhere. For this baseline capacity, commitment pricing is rational. You know you'll use it, so you might as well pay less.
Mature capacity planning. If you've been running at scale for years, you understand your usage patterns. You know your baseline. You can predict growth. You've already optimized. At this point, committing to your baseline makes sense, so you can lock in predictable costs for predictable usage.
Clear visibility into future demand. Some businesses have this. SaaS companies with annual contracts. Enterprises with multi-year roadmaps. If you can confidently predict your compute needs 12-36 months out, commitments reduce costs without increasing risk.
The key is the word "predictable." Commitments work when the future looks like the past. For infrastructure at EKS scale, GKE scale, or AKS scale, this requires sophisticated observability and capacity planning.
7. When they don't
This is the situation most engineering leaders find themselves in when they're reaching for a savings plan. The bill is on fire, and the savings plan appears to be the answer. But here's when it's actually dangerous:
Rapid growth. If you're scaling 3x this year, committing to current usage patterns is a gamble. You might grow into different instance types. You might move workloads. You might make architectural changes. Flexibility matters more than discount percentages.
Architectural change. Migrating to Kubernetes? Moving to serverless? Adopting containers? Any significant architectural shift makes commitments risky. What made sense for your EC2-heavy architecture might not make sense after you've containerized everything.
Poor workload visibility. If you can't explain where your $100K compute bill goes, committing to it is insane. You need to understand actual utilization before committing to pay for theoretical capacity. Without visibility, you're committing to waste.
Teams are already struggling with waste. If you're seeing 60% CPU overprovisioning and resources sitting idle, a savings plan doesn't fix this. It just means you've prepaid for inefficiency. Fix the waste first, then optimize pricing.
The danger is using commitments as a substitute for optimization. They're not. They're a pricing model that works after you've optimized, not instead of optimizing.
8. The better long-term approach
We gave you a 6-step playbook for when your bill is on fire. Steps 1-2 were "buy the savings plan, but don't stop there." Now let's talk about what "don't stop there" actually means.
Here's the approach that actually works:
Understand usage behavior first. Before you commit to anything, know where your money goes. What workloads drive costs? What's actually running 24/7? What's periodic? What's waste? You need answers before you can make intelligent commitments. This means properly instrumenting your infrastructure and gaining real-time visibility into costs.
Reduce variability. The more predictable your baseline, the more sense commitments make. This means right-sizing workloads, eliminating idle resources, and fixing overprovisioning. Get your actual usage closer to your provisioned capacity. Then commitments become lower risk.
Then decide how much commitment makes sense. Once you understand your true baseline, commit to that—not to your current inflated usage. If you're using $100K of compute but 40% is waste, fix the waste first. Then commit to the $60K you actually need. That's the real savings: lower baseline costs and lower unit costs.
This is the strategy we see work: optimize first, commit second. Not because commitments are bad, but because committing to waste is expensive even at a discount.
Closing: Reframing "cost optimization"
Most organizations think cost optimization means reducing their cloud bill. It doesn't. Cost optimization means getting predictable, controlled infrastructure spending aligned with business value.
This is not about cheaper bills. It's about knowing what you're paying for and why. It's about making infrastructure costs a first-class operational concern, not a quarterly fire drill.
It's about control, predictability, and economic alignment. Control means you decide where resources go, not gravity and organizational inertia. Predictability means you can forecast with confidence. Economic alignment means that those making allocation decisions see the cost of their decisions.
Savings plans can be part of this. They provide cost predictability for baseline workloads. They reduce cash outflow. They simplify forecasting. These are real benefits.
But they're not a substitute for understanding your infrastructure economics. They don't fix overprovisioning. They don't eliminate waste. They don't change the incentives that created the inefficiency in the first place.
From tactical relief to strategic control
The question isn't "should we buy a savings plan?" The question is, "Do we understand our infrastructure well enough to know what we should commit to?"
For most engineering organizations, the honest answer is no. And until the answer is yes, commitments are just prepaying for problems you haven't solved yet.
Here's the path forward:
If you're in crisis mode right now: Follow the playbook from our previous post. Buy the savings plan to get immediate relief. But don't stop there—use the breathing room to do the real work.
If you have time to be strategic, start with visibility. Understand your usage patterns. Identify waste. Fix structural inefficiency. Then you can use commitment pricing to lock in lower costs for what you actually need.
If you're already optimized: Commitments are your friend. Lock in predictable pricing for predictable workloads. You've earned the right to take the discount without the risk.
The difference between these scenarios is understanding. Do you know where your money goes? Do you know what you actually need versus what you're currently using? Do you know which workloads are stable and which are volatile?
If you can answer these questions with data, not guesses, you're ready to use commitments strategically. If you can't, you're gambling with prepaid waste.
That's the path from chaos to control. Savings plans are the last mile, not the first.
