AWS offers two primary ways to save money on compute costs: Reserved Instances (RIs) and Savings Plans. Both can deliver significant savings, but which is right for your organization? This comprehensive guide breaks down the differences, benefits, and best use cases for each option.
Reserved Instances provide capacity reservations and billing discounts for specific instance types in specific Availability Zones. You commit to using a particular instance type for 1 or 3 years in exchange for significant discounts.
Savings Plans offer flexible pricing models that provide savings on compute usage. You commit to a consistent amount of usage (measured in $/hour) for 1 or 3 years.
Feature | Reserved Instances | Savings Plans |
---|---|---|
Max Savings | Up to 75% | Up to 72% |
Flexibility | Low | High |
Capacity Reservation | Yes | No |
Best For | Stable workloads | Dynamic workloads |
The most flexible option, providing savings across:
More restrictive but higher savings:
RIs are ideal when you have:
Start with Standard RIs for your most stable workloads, then use Convertible RIs for workloads that might change. You can always convert Standard to Convertible later.
Savings Plans work best for:
Scenario: Consistent baseline traffic with seasonal spikes
Current: 20 m5.large instances running 24/7, 50 additional instances during peak seasons
Recommendation: RIs for baseline + Savings Plans for peak capacity
Savings: $18,000/year (43% reduction)
Scenario: Mix of EC2, Fargate, and Lambda with changing requirements
Current: $50,000/month across multiple services and regions
Recommendation: Compute Savings Plans for maximum flexibility
Savings: $180,000/year (30% reduction)
For most organizations, a hybrid approach works best: use Reserved Instances for your most stable, predictable workloads where you need capacity guarantees, and Savings Plans for everything else. This combination typically delivers 35-50% overall savings while maintaining operational flexibility.
Use our Reserved Instances ROI Calculator to model different commitment scenarios and find the optimal strategy for your workloads: