AWS EC2 instance types: Spot vs reserved vs on-demand

When managing AWS cloud costs, choosing the right EC2 instance purchasing model can significantly impact your bottom line. With potential savings of up to 90% compared to standard pricing, understanding the differences between spot instances, reserved instances, and on-demand instances is crucial for optimizing your AWS spend.

Understanding AWS EC2 instance purchasing options

AWS offers three primary purchasing models for EC2 instances, each designed for different workload requirements and budget considerations:

On-demand instances

On-demand instances operate on a pay-as-you-go model with no upfront commitments or contracts. You pay for compute capacity by the hour or second, depending on the instance type.

Key characteristics:

No upfront payment required
No minimum commitment
Highest cost per hour compared to other options
Maximum flexibility

Best for:

Short-term workloads
Unpredictable applications
Development and testing environments
Applications that can’t be interrupted

Reserved instances (RIs)

Reserved instances provide significant discounts (up to 75%) compared to on-demand pricing in exchange for a 1 or 3-year commitment.

Key characteristics:

Requires term commitment (1 or 3 years)
Payment options: all upfront, partial upfront, or no upfront
Significant cost savings over on-demand
Available in three types:
- Standard RIs: Offer the highest discount (up to 75%)
- Convertible RIs: Allow changing instance attributes (up to 54% savings)
- Scheduled RIs: Available during specific time windows

Best for:

Steady-state, predictable workloads
Applications with consistent usage patterns
Production environments requiring capacity assurance
Budget-conscious organizations with long-term plans

Spot instances

Spot instances allow you to bid on unused EC2 capacity, offering the deepest discounts (up to 90%) compared to on-demand pricing.

Key characteristics:

Lowest cost option (up to 90% off on-demand)
Variable pricing based on supply and demand
Can be terminated with just two minutes’ notice when AWS needs capacity back
No long-term commitments

Best for:

Fault-tolerant applications
Stateless workloads
Batch processing jobs
Background processing
Optional workloads that can be interrupted

Cost comparison

To illustrate the potential savings, let’s compare the pricing models for a typical workload:

Instance Type	Pricing Model	Relative Cost	Potential Savings
On-demand	Pay-as-you-go	100% (baseline)	0%
Reserved (Standard)	1-3 year commitment	25-40% of on-demand	Up to 75%
Reserved (Convertible)	1-3 year commitment with flexibility	46-54% of on-demand	Up to 54%
Spot	Variable, market-based	10-20% of on-demand	Up to 90%

When to use each instance type

On-demand instances

Use on-demand instances when:

You need maximum flexibility without commitment
Your workloads are unpredictable or short-term
You’re testing or developing applications
Your application cannot tolerate interruptions

Reserved instances

Use reserved instances when:

You have steady, predictable workloads
You can commit to using EC2 for at least one year
You need a balance between cost savings and flexibility
Budget predictability is important for your organization

As noted in our AWS cloud cost management tools, reserved instances can be a key component of your cost optimization strategy. For organizations looking to maximize their RI investment, you can even trade reserved instances on the AWS RI Marketplace.

Spot instances

Use spot instances when:

Your applications are fault-tolerant and can handle interruptions
You have flexible start and end times for your workloads
Your workload is stateless or can save state
Cost optimization is more important than instance availability
You’re running batch processing jobs or background tasks

Best practices for instance selection

1. Analyze your workload patterns

Before selecting an instance purchasing option, analyze your workload patterns to understand:

Predictability of usage
Tolerance for interruptions
Required availability
Budget constraints

2. Implement a hybrid approach

Most organizations benefit from a mixed approach:

Use reserved instances for baseline, predictable workloads
Use on-demand for unpredictable spikes
Use spot instances for fault-tolerant, flexible workloads

This approach is like a financial portfolio – diversifying your instance types helps balance risk and cost-efficiency. Think of reserved instances as your long-term investments, on-demand as your readily available cash, and spot instances as opportunistic investments.

3. Leverage auto scaling

Combine your instance purchasing strategy with auto scaling to automatically adjust capacity based on demand. This is particularly effective when using a mix of reserved instances for baseline capacity and spot or on-demand instances for handling traffic spikes.

For example, an e-commerce platform might use reserved instances to handle normal traffic, then automatically scale up with spot instances during flash sales – potentially saving thousands of dollars compared to provisioning on-demand capacity for peak loads.

4. Monitor and optimize regularly

Cloud costs can drift over time. Regularly review your instance usage and adjust your purchasing strategy accordingly. AWS Cost Explorer and third-party tools can help identify optimization opportunities.

A good practice is to schedule monthly cloud cost reviews where you:

Review usage patterns
Identify idle or underutilized instances
Adjust reserved instance commitments
Update spot instance strategies based on recent pricing trends

5. Consider storage optimization alongside compute

While focusing on EC2 instances, don’t overlook storage optimization. Implementing proper Amazon EBS best practices can further reduce your overall cloud spend.

Mitigating risks with spot instances

While spot instances offer the deepest discounts, they come with the risk of interruption. Here are strategies to mitigate this risk:

Implement checkpointing: Regularly save application state to resume processing after interruption. For example, a machine learning training job could save model weights every few minutes to avoid losing progress if the instance is reclaimed.
Use spot instance fleets: Diversify across multiple instance types and availability zones. This spreads the risk and increases the likelihood that at least some of your capacity will remain available during price fluctuations.
Set up fallback mechanisms: Automatically switch to on-demand instances for critical workloads if spot instances become unavailable. You can configure AWS Auto Scaling groups to maintain capacity by provisioning on-demand instances when spot instances aren’t available.
Design for fault tolerance: Build applications that can handle instance termination gracefully. This might involve using distributed systems like Apache Cassandra or stateless microservices that can easily recover when nodes disappear.

Comparing AWS with other cloud providers

When evaluating cloud costs, it’s worth considering how AWS pricing models compare to other providers. For example, comparing AWS vs GCP prices can reveal different approaches to discounting and instance flexibility.

AWS Reserved Instances differ from Google Cloud’s Committed Use Discounts (CUDs) in several ways:

AWS RIs can be traded in a marketplace; GCP CUDs cannot
AWS offers multiple RI types (Standard, Convertible); GCP offers a simpler model
AWS requires instance family commitments; GCP’s flexible CUDs work across families

If you’re running containerized workloads, understanding the cost implications of orchestration services like ECS and EKS difference is also important for comprehensive cost optimization.

Automating cost optimization

For many organizations, manually managing instance purchasing strategies becomes complex as cloud usage grows. Automation tools can help:

Automated instance selection: Tools that analyze workloads and recommend the optimal purchasing model. These solutions examine historical usage patterns and suggest the most cost-effective mix of instances.
Dynamic allocation: Systems that automatically shift workloads between spot, on-demand, and reserved instances based on availability and cost. For example, batch processing jobs can be configured to run on spot instances when prices are low and fall back to on-demand when spot prices spike.
Continuous optimization: Solutions that constantly monitor usage patterns and adjust purchasing strategies. These might recommend converting on-demand usage to reserved instances when patterns stabilize or suggest breaking up monolithic reserved instances into smaller units for better utilization.

Case study: Financial services firm saves 62% with hybrid instance strategy

A mid-sized financial services company was running their risk analysis platform entirely on on-demand instances, spending approximately $250,000 annually. By implementing a hybrid approach:

They moved 60% of their workload to reserved instances (saving 40%)
They shifted 25% to spot instances (saving 85%)
They kept 15% on on-demand for critical, unpredictable processing

The result was a 62% reduction in their annual EC2 costs ($155,000 saved), without sacrificing performance or reliability. The key was identifying which workloads could tolerate interruption (batch processing) versus those requiring guaranteed availability (customer-facing services).

Conclusion

Selecting the right EC2 instance purchasing model is a critical component of AWS cost management. By understanding the strengths and limitations of spot instances, reserved instances, and on-demand instances, you can create a strategic approach that balances cost savings with your workload requirements.

For most organizations, a hybrid approach works best—using reserved instances for predictable baseline workloads, spot instances for interruptible tasks, and on-demand instances for critical, variable workloads.

Hykell specializes in helping businesses optimize their AWS costs through automated solutions that can reduce cloud spending by up to 40%. Our platform analyzes your usage patterns and recommends the optimal mix of instance purchasing options, helping you maximize savings without compromising performance.