Calculating Cloud Compute Baseline: RIs vs. Savings Plans
Commitment is scary. In software engineering, we prize agility, the ability to tear down a monolith and replace it with microservices overnight. So, the idea of telling your cloud provider, “I promise to use this specific server for the next three years,” feels like an anti-pattern.
But here is the reality: Agility has a price tag, and it’s usually a 40% premium.
If you run your entire production fleet on On-Demand instances to maintain “maximum flexibility,” you aren’t being agile; you are being inefficient. Every system has a “base load”, a minimum set of resources that are always running, regardless of traffic spikes or architectural shifts.
Identifying that baseline and locking it in is one of the highest-ROI activities an engineer can do. This article provides the framework to do it safely.
1. Finding Your “Waterline”: The Heatmap Analysis
Before you buy a reservation, you need to know what you are actually using. A simple “average CPU usage” metric is misleading because it smoothes over the valleys where you might be scaling down to zero.
You need to find your “Always-On Waterline.”
The best tool for this is a Utilization Heatmap:
- X-Axis: Hours of the day (0-24).
- Y-Axis: Days of the week (Mon-Sun).
- Cell Color: Minimum instance count running during that hour.
How to read it:
Look for the number that never drops. If your auto-scaling group fluctuates between 20 and 50 instances, your “Waterline” is 20. Those 20 instances are effectively static. Paying On-Demand rates for them is a waste.
Industry Benchmark: According to ProsperOps’ 2024 Benchmarks, elite organizations cover ~80-90% of this steady-state usage with commitments. If your coverage is 0%, you are leaving free money on the table.
2. The Commitment Spectrum: A Decision Framework
Once you know your baseline (e.g., “I need 100 vCPUs continuously”), you have to choose the vehicle. This is where engineers get confused. AWS and Azure offer multiple “flavors” of commitment with varying degrees of rigidity.
Think of it as a spectrum from Highest Savings / Lowest Flexibility to Lower Savings / Highest Flexibility.
A. Standard Reserved Instances (The “Marriage”)
- Savings: Up to 72-75%.
- The Deal: You lock in a specific Instance Type (e.g., m5.large) in a specific Region (and sometimes Zone).
- The Trap: If you want to switch from m5 to r5 next month? You can’t.
- Best For: Databases (RDS), cache nodes (ElastiCache), and monolithic core apps that haven’t changed in years.
B. Convertible Reserved Instances (The “Pivot-Ready”)
- Savings: Up to 54%.
- The Deal: You can exchange these for different instance families (e.g., c5 to m5) if your architecture changes.
- The Trap: You often have to “true up” the value (increase commitment) to make the exchange.
- Best For: Core infrastructure that might need a RAM upgrade or a generation shift (e.g., moving to AWS Graviton) during the term.
C. Compute Savings Plans (The “Universal Adapter”)
- Savings: Up to 66%.
- The Deal: You commit to a dollar amount per hour (e.g., “$10/hr”), not a specific instance. It applies automatically to EC2, Fargate, and Lambda.
- The Benefit: This is the most engineer-friendly option. You can refactor from VMs to Serverless containers halfway through your 3-year term, and the discount still applies.
- Adoption Stat: Because of this flexibility, the Flexera 2024 State of the Cloud Report notes that Savings Plans have overtaken RIs, with 50% of organizations now prioritizing them.
3. The “Break-Even” Analysis
Technical leaders often ask: “What if we migrate to a new cloud next year? Is a 3-year term worth the risk?”
You need to calculate the Break-Even Point.
Typically, a 1-year All-Upfront RI breaks even at month 7-9.
- If you are 100% sure your architecture will exist for 9 months, buying a 1-year commitment is safer than staying On-Demand.
- For 3-year commitments, the savings are massive, but the risk is real. A good rule of thumb: Only apply 3-year commitments to the “Waterline” of your Waterline, the absolutely irreducible core of your stack (like your primary database).
Conclusion
Don’t let the fear of architectural lock-in prevent you from optimizing costs. By calculating your baseline and choosing the right vehicle, likely Compute Savings Plans for your stateless apps and Standard RIs for your databases, you can fund your next innovation project with the budget you save.
For more on how these decisions fit into your broader strategy, refer back to our pillar guide on Cloud Billing Models.
