Certified - AWS Certified Cloud Practitioner

In this episode, we provide a detailed comparison of On-Demand, Reserved, and Spot Instances, helping you decide which option is best for your AWS workloads. On-Demand Instances are the most flexible and allow you to pay for compute capacity by the hour or second, making them ideal for unpredictable workloads. However, they can be more expensive compared to other options. We’ll explain when to use On-Demand Instances for testing, development, or workloads with unpredictable traffic patterns, and how to manage costs by adjusting your usage.
Next, we’ll explore Reserved Instances, which offer significant savings for long-term, predictable workloads. We’ll discuss how Reserved Instances provide discounts in exchange for a one- or three-year commitment, and how to plan your capacity to maximize savings. Lastly, we’ll cover Spot Instances, which are ideal for flexible, fault-tolerant workloads that can tolerate interruptions. Spot Instances are much cheaper but come with the risk that AWS might reclaim capacity if demand for resources increases. By the end of this episode, you’ll be able to make informed decisions about which EC2 pricing model to use for your specific needs. Produced by BareMetalCyber.com, your trusted resource for expert-driven cybersecurity education.

What is Certified - AWS Certified Cloud Practitioner ?

Ready to earn your AWS Certified Cloud Practitioner credential? Our prepcast is your ultimate guide to mastering the fundamentals of AWS Cloud, including security, cost management, core services, and cloud economics. Whether you're new to IT or looking to expand your cloud knowledge, this series will help you confidently prepare for the exam and take the next step in your career. Produced by BareMetalCyber.com, your trusted resource for expert-driven cybersecurity education.

When thinking about EC2 pricing, it’s best to view the three main purchase models as a trade-off between flexibility, commitment, and discount with risk. On-Demand Instances offer the most flexibility—you pay by the hour or second with no long-term commitment. Reserved Instances (RIs) provide the biggest discounts in exchange for steady commitments. Spot Instances give the deepest savings, but only for workloads that can handle interruptions. The exam doesn’t ask you to calculate dollar amounts; it tests whether you can match workload predictability and tolerance for risk with the right model.
On-Demand is the default choice when launching workloads. The advantages are simplicity, no commitments, and full control over starting and stopping instances. The downside is cost, since On-Demand runs at list price. It’s best used for unpredictable workloads, short-term projects, or when you’re still learning usage patterns. For example, a dev team launching a new service with uncertain traffic should start with On-Demand. On the exam, words like “new,” “unpredictable,” or “spiky” usually map to On-Demand.
Standard Reserved Instances provide the steepest discounts—up to 72 percent—but lock you into specific instance families, Regions, and platforms for one or three years. This lack of flexibility makes them ideal for steady, predictable workloads, like a core fleet of web servers that runs 24×7. Convertible RIs offer some flexibility: you can exchange them for different families or sizes, but the discount is smaller. The exam expects you to know the trade-off: Standard RIs are bigger savings but rigid; Convertible RIs are more flexible but less discounted.
Zonal RIs add another twist: they reserve capacity in a specific Availability Zone. This guarantees instance availability even during shortages, making them a capacity reservation as well as a discount mechanism. Regional RIs, by contrast, apply across all AZs in a Region, giving you more placement flexibility but without hard reservations. On the exam, “guarantee capacity in a single AZ” means Zonal RI, while “discount across AZs in a Region” points to Regional RI.
Savings Plans often show up as a quick contrast. Compute Savings Plans cover EC2, Fargate, and Lambda in exchange for a steady hourly spend commitment, making them more flexible than RIs. Instance Savings Plans are closer to RIs, tied to a family and Region. The exam may not dive deep into this but expects you to recognize that Savings Plans are the more modern, flexible alternative.
Spot Instances give access to spare AWS capacity at discounts of up to 90 percent. The trade-off is that AWS can reclaim them with only a short interruption notice. Workloads must be designed to checkpoint, retry, or tolerate failures. The exam cue is “fault-tolerant,” “batch jobs,” or “rendering.” If it says “must always be available” or “mission-critical,” Spot is never correct.
Spot diversification is a best practice—don’t rely on a single pool of Spot capacity. By spreading requests across multiple instance types, sizes, and AZs, you reduce the chance of losing all capacity at once. For example, an analytics job might request Spot capacity from three different pools, ensuring resiliency even if one pool dries up. The exam may hint at this by mentioning “diversify Spot pools.”
The fit for each model becomes clearer in context: steady workloads fit Reserved or Savings Plans, unpredictable workloads fit On-Demand, and interruption-tolerant workloads fit Spot. Real-world strategies blend them. For example, cover 70 percent of steady usage with RIs or Savings Plans, run 20 percent On-Demand for bursts, and use 10 percent Spot for opportunistic jobs. This portfolio approach balances cost and risk.
Monitoring utilization of commitments is crucial. If you buy RIs and don’t use them, you lose money. AWS provides reporting to track utilization and recommendations to modify Convertible RIs. Tags and ownership metadata help with chargeback, ensuring commitments are tied to the right teams. On the exam, if the scenario mentions “unused RIs,” the correct answer is to modify or track utilization.
Governance practices also apply. RIs and Savings Plans should be tagged with owners, environments, or cost centers to avoid waste. Chargeback models let finance attribute savings and costs to teams. For the exam, if the scenario says “allocate costs by team,” tagging is always part of the answer.
Finally, the exam lens is simple: read cues about workload predictability and risk tolerance. If the workload is unpredictable or short-term, choose On-Demand. If it’s steady and predictable, choose Reserved Instances or Savings Plans. If it’s tolerant of interruptions, choose Spot. The key is to align the pricing model with the workload’s stability and resilience requirements.
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A practical way to decide among purchase models is to follow a decision tree. Start with On-Demand while workloads are new or unpredictable. Once usage patterns stabilize and you can forecast baselines, commit to Reserved Instances or Savings Plans for discounts. For bursty or flexible workloads, add Spot capacity where fault tolerance allows. This staged approach avoids locking in too early and ensures your commitments reflect actual demand. The exam often frames this as “start flexible, commit when stable.”
A blended portfolio works best. Cover steady baselines with Reserved Instances or Savings Plans, handle overflow with On-Demand, and leverage Spot for opportunistic workloads. For example, a retail site might cover its steady daily traffic with Savings Plans, run On-Demand during seasonal peaks, and push nightly analytics to Spot fleets. The exam keyword “baseline” signals commitments, while “bursty” or “batch” points to On-Demand or Spot.
Spot workloads require specific patterns. Queued jobs like rendering, checkpoint-enabled analytics, or stateless application tiers are excellent candidates. Stateless web servers behind a load balancer can tolerate Spot interruptions gracefully, while core databases cannot. Exam cues like “queue,” “checkpoint,” or “stateless” always map to Spot-friendly design.
Commitment choices matter too. One-year terms are common for agility, while three-year terms maximize savings but require long-term confidence. Payment options—no upfront, partial upfront, or all upfront—adjust the discount further. The exam may ask which gives “maximum discount,” and the correct answer is usually a three-year, all-upfront commitment.
Capacity reservations must be distinguished from RIs. Reservations guarantee the ability to launch instances in a given AZ but don’t necessarily include discounts. RIs provide discounts but don’t guarantee capacity unless they’re zonal. If the exam mentions “guarantee availability during shortages,” the answer is capacity reservations, not just RIs.
Unused or underused commitments are a common pitfall. AWS Cost Explorer and Trusted Advisor can identify unused RIs or low-utilization commitments. Convertible RIs can be modified to adjust families. The exam may present this as “a company purchased RIs but isn’t using them,” with the expected fix being to modify or monitor.
Reporting views matter when commitments are spread across accounts. Amortized views in Cost Explorer spread upfront costs over the commitment term, giving a clearer picture of true hourly costs. Dashboards allow finance teams to track utilization and coverage percentages. On the exam, “detailed commitment reporting” points to Cost Explorer or CUR (Cost and Usage Report).
Some scenarios raise security and compliance concerns. Dedicated Instances and Dedicated Hosts provide physical isolation for regulatory requirements, ensuring workloads don’t share hardware. They cost more but may be mandated. The exam cue “compliance or licensing requiring dedicated hardware” points here.
Multi-account environments benefit from AWS Organizations. Consolidated billing shares RI and Savings Plan discounts across accounts, preventing stranded discounts. On the exam, “share discounts across multiple accounts” maps directly to consolidated billing.
Budget alarms and anomaly detection help ensure commitments stay on track. Budgets let you set thresholds for spend or utilization. Anomaly Detection in Cost Explorer highlights unusual spikes. The exam cues are “alert when spend exceeds threshold” (Budgets) and “highlight unexpected usage patterns” (Anomaly Detection).
CI/CD and Auto Scaling interplay with purchase models too. Auto Scaling fleets often run On-Demand for peaks but can include baseline RIs. CI/CD pipelines testing new builds should run On-Demand or Spot, never commitments, since workloads are transient. The exam may hint at this with “test environments” or “scale dynamically,” which map to On-Demand and Spot.
Common pitfalls include relying on a single Spot pool, which risks total interruption, or locking RIs into the wrong family or Region, leaving them unused. The exam may present these pitfalls directly, asking how to avoid them. The correct answers emphasize diversification for Spot and careful planning for RIs.
From an exam perspective, always map the workload’s time horizon and risk tolerance. Steady 24×7? Reserved Instances or Savings Plans. New or unpredictable? On-Demand. Fault-tolerant batch or stateless? Spot. This simple mapping covers nearly all EC2 pricing questions and mirrors real-world best practice.
In conclusion, blending models pragmatically is the winning strategy. Start workloads On-Demand, commit steady baselines to RIs or Savings Plans, and use Spot where interruptions are acceptable. Revisit commitments as workloads evolve, monitor utilization, and keep governance tight with tagging, budgets, and consolidated billing. For the exam, trust the cues—predictability means commit, uncertainty means On-Demand, tolerance means Spot.