Difference Between EC2 Reserved vs Spot Instances: Key Comparison Guide
Amazon EC2 (Elastic Compute Cloud) provides flexible computing capacity in the cloud. AWS offers multiple pricing models for EC2 instances, among which Reserved Instances (RI) and Spot Instances are the most popular for cost optimization.
In this blog, we will discuss the key differences between EC2 Reserved vs Spot Instances, pricing structure, benefits, limitations, and when to use which for your AWS workloads.
What are EC2 Reserved Instances?
Definition of Reserved Instances
Reserved Instances (RI) are a billing discount offered by AWS for committing to use EC2 instances for a specific term (1 or 3 years). They offer significant savings compared to On-Demand pricing.
Types of Reserved Instances
- Standard RIs
- Convertible RIs
- Scheduled RIs
Benefits of Reserved Instances
- Cost Savings up to 72%
- Predictable Billing
- Ideal for Steady-State Workloads
Limitations
- Long-Term Commitment
- Less Flexibility
- Instance Family & Region Specific
What are EC2 Spot Instances?
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Definition of Spot Instances
Spot Instances allow you to bid and use unused EC2 capacity at up to 90% lower cost than On-Demand pricing. However, AWS can terminate your instances anytime when capacity is no longer available.
Benefits of Spot Instances
- Massive Cost Savings (up to 90%)
- Scalable for Large Workloads
- Best for Fault-Tolerant Applications
Limitations
- Instances Can Be Interrupted
- No Guaranteed Availability
- Not Suitable for Critical Applications
EC2 Reserved vs Spot Instances: Key Differences
Feature | Reserved Instances | Spot Instances |
---|---|---|
Pricing Discount | Up to 72% | Up to 90% |
Commitment | 1 or 3 years | No Commitment |
Flexibility | Low | High |
Suitable For | Steady-State Workloads | Intermittent/Fault-Tolerant Workloads |
Availability | Guaranteed | Not Guaranteed |
When to Use Reserved Instances
- Long-running applications
- Enterprise workloads
- Databases
- Production environments with predictable traffic
When to Use Spot Instances
- Big data processing
- Batch jobs
- CI/CD pipelines
- Develop
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