AWS: On-Demand vs Reserved vs Spot Cost savings
How to manage your workloads for best cost optimisation
Quick Comparison
On-Demand Instances
On-demand instances allow you to pay as you go with no upfront costs, offering flexibility for unpredictable workloads. They are ideal for development, testing, and short-term projects but come at a higher price compared to other options.
Pricing: Pay-as-you-go; no upfront cost.
Flexibility: No long-term commitment; ideal for unpredictable workloads.
Use Cases: Development, testing, and short-term projects.
Pros: Easy to scale up or down; no upfront payment.
Cons: Highest cost compared to other instance types.
Reserved Instances
Reserved Instances require a 1- or 3-year commitment, providing significant cost savings of up to 75% and reserved capacity for predictable, steady-state workloads. This option is best suited for long-term projects with consistent usage patterns.
Pricing: Up to 75% discount compared to On-Demand; requires upfront payment.
Flexibility: 1 or 3-year commitment; reserved capacity.
Use Cases: Predictable, steady-state workloads.
Pros: Significant cost savings; capacity reservation.
Cons: Less flexibility; requires long-term commitment.
Spot Instances
Spot Instances offer the lowest cost, with up to 90% savings compared to On-Demand, by taking advantage of unused EC2 capacity. They are perfect for fault-tolerant, flexible workloads like batch processing and data analysis, though they come with the risk of interruptions by AWS.
Pricing: Up to 90% discount compared to On-Demand; variable pricing.
Flexibility: Instances can be terminated by AWS with short notice.
Use Cases: Batch processing, data analysis, and flexible applications.
Pros: Lowest cost; great for fault-tolerant and flexible workloads.
Cons: Risk of interruption; not suitable for critical applications.
Workload Management for Best Cost Optimisation
Different types of workloads can be split into On-Demand, Reserved, and Spot to ensure you are optimizing for your compute costs at every step.
On-Demand Instances
Development and testing environments
Short-term projects
Unpredictable workloads
Applications with variable or temporary usage
Reserved Instances
Long-term applications
Predictable, steady-state workloads
Large enterprises with consistent usage patterns
Critical applications requiring reserved capacity
Spot Instances
Batch processing
Data analysis
High-performance computing
Fault-tolerant and flexible applications
Big data workloads
CI/CD pipelines
This is the first stage to start saving costs, next steps are to dynamically migrate workloads on compute and time patterns to save additional costs at a monthly level. We will share more about that soon!
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