AWS Savings Plans vs Reserved Instances vs Azure Reservations: A Complete Comparison

Cloud providers reward companies that can commit to predictable usage.

The challenge is deciding what to commit to.

Commit too little and you leave potential savings on the table. Commit too much and you can end up paying for capacity your organization no longer needs.

AWS offers Savings Plans and Reserved Instances. Azure offers Reservations and Savings Plans for eligible workloads.

Although all of these options reduce the cost of predictable cloud usage, they work differently.

The key difference is flexibility.

AWS Savings Plans generally give you more flexibility than traditional Reserved Instances because you commit to a level of spend rather than locking yourself to a specific instance configuration. Azure Reservations are more resource-specific, while Azure Savings Plans use an hourly spend commitment for eligible services and regions.

So which should you choose?

For highly stable workloads, a reservation can provide strong savings. For environments where instance families, sizes, regions, or services may change, a savings plan is generally safer.

The goal is not to find the biggest advertised discount.

It is to find the largest discount you can actually use.

Savings Plans vs Reserved Instances vs Azure Reservations at a Glance

Factor	AWS Savings Plans	AWS Reserved Instances	Azure Reservations
Commitment	$/hour	Specific instance configuration	Specific eligible resource/SKU
Typical term	1 or 3 years	1 or 3 years	1 or 3 years
Flexibility	High	Lower	Moderate
Best for	Changing but predictable compute spend	Stable instance configurations	Stable Azure workloads
Scope	Depends on plan type	Region/configuration dependent	Resource/subscription/shared scope
Instance-size flexibility	Yes	Depends on RI type	Available for eligible VM reservations
Can cover multiple services?	Compute SP can cover EC2, Fargate and Lambda	Generally service-specific	Savings plans provide broader eligible compute coverage
Main risk	Overcommitting hourly spend	Resource/configuration changes	Buying the wrong SKU or scope
Payment options	No upfront, partial, all upfront	No/partial/all upfront depending on RI	Upfront or monthly

AWS currently offers Compute Savings Plans, EC2 Instance Savings Plans, and SageMaker Savings Plans. Compute Savings Plans can apply across EC2, Fargate, and Lambda, while EC2 Instance Savings Plans apply to EC2 usage within a selected instance family and region.

Azure Reservations cover a range of eligible resources, including Virtual Machines, SQL Database compute capacity, Cosmos DB throughput, storage capacity, and others.

What Are AWS Savings Plans?

AWS Savings Plans are commitment-based pricing models.

Instead of committing to a particular EC2 instance, you commit to a consistent amount of eligible usage per hour for one or three years.

AWS then applies discounted rates to eligible usage up to that commitment.

There are two main compute-oriented choices to consider.

Compute Savings Plans

Compute Savings Plans provide the most flexibility.

They can apply to:

• Amazon EC2
• AWS Fargate
• AWS Lambda

They allow changes in instance family, instance size, operating system, tenancy, and region without requiring you to exchange the plan.

This makes them useful for organizations whose infrastructure changes over time.

For example, suppose a company currently runs:

20 m5.xlarge EC2 instances

but expects to migrate some workloads to newer instance families or move workloads between regions.

A Compute Savings Plan can be less restrictive than committing to the exact current configuration.

AWS currently advertises Savings Plans with savings of up to 72% compared with On-Demand pricing, although the actual discount depends on the plan, workload, term, and usage.

EC2 Instance Savings Plans

EC2 Instance Savings Plans provide a higher degree of specificity.

You commit to an instance family in a particular AWS Region.

In exchange, the commitment can apply across instance sizes within that family, and AWS documentation states that the benefit can apply regardless of operating system and tenancy for eligible usage.

This makes them useful when you know that your workload will remain within a particular instance family and region.

The trade-off is simple:

More specificity → potentially stronger savings

More flexibility → easier adaptation to change

What Are AWS Reserved Instances?

Reserved Instances are another commitment-based pricing model for EC2.

AWS describes Reserved Instances as a commitment to a consistent instance configuration, including instance type and Region, for one or three years.

There are two major types:

Standard Reserved Instances

Convertible Reserved Instances

Standard RIs generally provide stronger discounts but less flexibility.

Convertible RIs provide greater flexibility to exchange into different configurations, subject to AWS’s applicable rules.

AWS itself currently recommends Savings Plans over Reserved Instances for many customers because Savings Plans provide more flexibility while offering comparable discounted pricing structures.

That does not make Reserved Instances obsolete.

They can still make sense when the workload is exceptionally predictable.

Savings Plans vs Reserved Instances on AWS

The simplest distinction is:

Savings Plan = commit to spend

Reserved Instance = commit to infrastructure configuration

Consider an organization running a stable production application.

Today:

• 30 EC2 instances
• Same family
• Same Region
• Running 24/7
• Expected to remain stable for three years

An RI may be appropriate.

Now consider another organization:

• Workloads move between instance families.
• Some applications migrate to containers.
• Fargate adoption is increasing.
• Lambda usage is growing.
• Regions may change.

A Compute Savings Plan is likely a better fit because the organization is committing to compute spend rather than one specific infrastructure configuration.

What Are Azure Reservations?

Azure Reservations allow organizations to commit to eligible Azure resources for one or three years in exchange for discounted pricing.

Microsoft says reservations can be paid upfront or monthly, with the total cost being the same between those payment options. The reservation discount automatically applies to matching resources.

For Azure Virtual Machines, the reservation applies to matching usage according to attributes such as SKU, region, and scope.

Azure also provides instance-size flexibility for eligible VM reservations, allowing a reservation to cover different VM sizes within the same flexibility group according to Microsoft’s size ratios.

This makes Azure Reservations more flexible than a simple one-to-one mapping between one purchased reservation and one running VM.

Azure Reservations vs Azure Savings Plans

Azure also offers Savings Plans for eligible compute workloads.

The fundamental difference is similar to the AWS distinction.

Azure Reservation → commit to specific eligible resource usage

Azure Savings Plan → commit to a consistent hourly spend

Microsoft describes Savings Plans as a good option when you need flexibility across services and regions, while reservations are more appropriate for predictable, stable workloads.

This makes the decision easier:

If you know exactly what infrastructure you will run, consider a reservation.

If you know how much compute you will consume but expect the underlying resources to change, consider a savings plan.

The Most Important Question: How Stable Is Your Workload?

Before purchasing any commitment, look at historical utilization.

Do not ask:

“How much compute are we running today?”

Ask:

“How much eligible compute are we confident we will continue using throughout the commitment period?”

This distinction prevents one of the most common FinOps mistakes: purchasing commitments based on peak usage.

Suppose your organization normally uses $10/hour of eligible compute but occasionally reaches $15/hour.

Buying a $15/hour commitment because of the peak can leave $5/hour underutilized for much of the year.

A better strategy may be to commit closer to the stable baseline and leave variable usage at On-Demand rates.

Baseline vs Variable Cloud Usage

A useful way to think about commitments is to split your infrastructure into two categories.

Baseline usage

This is infrastructure that runs consistently.

Examples:

• Production application servers
• Core databases
• Always-on APIs
• Persistent application workloads
• Stable internal systems

This is the best candidate for commitments.

Variable usage

This changes significantly over time.

Examples:

• Development environments
• Seasonal applications
• Batch processing
• Temporary projects
• Migration workloads
• New applications
• Experimental workloads

These workloads are generally less suitable for long-term commitments.

The strategy becomes:

Commit to the baseline.

Keep variable demand flexible.

This is often safer than trying to discount 100% of your cloud compute.

Why Utilization Matters More Than the Discount Percentage

Imagine two companies.

Company A receives a 40% discount but uses only 70% of its commitment.

Company B receives a 30% discount and uses 98% of its commitment.

Company B may achieve the better effective savings.

The advertised discount does not tell you the full story.

You need to consider:

Effective savings = discount benefit − cost of unused commitment

This is why commitment utilization should be one of the most important FinOps metrics you monitor after purchasing.

A Simple Example

Suppose a company has predictable compute spending of:

$10/hour

Its On-Demand cost is approximately:

$10 × 730 = $7,300/month

Now assume a commitment reduces the effective compute rate by 30%.

The committed usage would cost approximately:

$7 × 730 = $5,110/month

The theoretical monthly saving is:

$2,190

But suppose actual usage falls to $7/hour.

The company is still committed to $10/hour.

The unused $3/hour represents approximately:

$3 × 730 = $2,190/month

The organization may therefore lose much of the expected economic benefit through underutilization.

This is why commitment sizing is as important as commitment selection.

AWS vs Azure: How the Commitment Models Differ

Decision	AWS	Azure
Want maximum flexibility	Compute Savings Plan	Savings Plan
Know the instance family	EC2 Instance Savings Plan	VM Reservation
Know exact infrastructure	Reserved Instance	Reservation
Predictable spend	Savings Plan	Savings Plan
Predictable resource configuration	RI	Reservation
Need to adapt to changing compute	Savings Plan	Savings Plan

The terminology differs, but the underlying principle is similar.

Stable resource → reservation

Stable spend with changing resources → savings plan

Scope Matters

Commitment discounts only help when they can actually be applied to eligible usage.

AWS Savings Plans have specific applicability rules depending on the plan type. AWS Compute Savings Plans provide broader EC2 flexibility, while EC2 Instance Savings Plans are tied to an instance family and Region.

Azure Reservations can be scoped to a resource group, subscription, or shared scope depending on the purchase and billing context.

Azure also applies reservation benefits automatically to matching eligible resources.

This means FinOps teams should review organizational structure before buying commitments.

A reservation scoped too narrowly may have low utilization even though another subscription has matching workloads.

What Happens When Your Infrastructure Changes?

This is where commitment flexibility becomes important.

Cloud environments rarely remain static for three years.

You might:

• Resize instances
• Change instance families
• Move regions
• Migrate to containers
• Move workloads to serverless
• Shut down applications
• Acquire another company
• Change cloud providers
• Modernize legacy applications

A commitment that looked perfect when purchased may become inefficient later.

AWS Savings Plans are designed to accommodate many infrastructure changes without requiring manual exchange or modification.

Azure Reservations also provide flexibility features, including instance-size flexibility for eligible VMs and the ability to change scope.

However, Azure is changing its reservation exchange policy.

Microsoft currently states that reservations purchased after February 1, 2027, for services covered by Savings Plans will no longer be eligible for exchange, while reservations purchased before that date retain one final exchange opportunity.

That makes it particularly important to evaluate whether a new Azure commitment should be a Reservation or a Savings Plan.

When Should You Choose AWS Savings Plans?

Choose an AWS Savings Plan when:

• Your compute spend is predictable.
• Your infrastructure may change.
• You use multiple EC2 instance families.
• You may change instance sizes.
• You use Fargate.
• You use Lambda.
• You want to reduce On-Demand costs without locking into one exact configuration.

For maximum flexibility, Compute Savings Plans are generally the strongest option.

For predictable EC2 usage within a particular family and Region, an EC2 Instance Savings Plan can provide a more targeted commitment.

When Should You Choose AWS Reserved Instances?

Consider Reserved Instances when:

• Your EC2 configuration is highly stable.
• You know the instance family and Region.
• You expect the workload to remain for the commitment period.
• The workload does not need the broader flexibility of a Savings Plan.
• The economics of the specific RI are better for your workload.

Standard RIs are most appropriate when the configuration is unlikely to change.

Convertible RIs make more sense when you need some ability to change configurations.

AWS currently positions Savings Plans as the preferred option for many use cases because of their flexibility.

When Should You Choose Azure Reservations?

Azure Reservations are appropriate when:

• Your Azure workload is stable.
• You know the resource/SKU requirements.
• Your usage will remain predictable.
• You want a commitment-based discount.
• VM instance-size flexibility can accommodate expected changes.

Reservations can also be useful for eligible Azure services beyond Virtual Machines, including SQL Database compute and storage-related offerings.

Before purchasing, however, check Microsoft’s current exchange and cancellation policies because they can affect the risk of a long-term commitment.

When Should You Choose Azure Savings Plans?

Consider an Azure Savings Plan when:

• Your compute spending is predictable.
• The underlying VM or compute resources may change.
• You need flexibility across eligible services or regions.
• Your environment is undergoing modernization.
• You do not want to commit to a specific SKU.

Microsoft specifically positions Savings Plans as a commitment-based option for evolving or dynamic workloads.

A Practical Decision Tree

Use this simple framework.

Is the workload predictable?

No → Stay On-Demand or use more flexible pricing options.

Yes → Continue.

Will the infrastructure configuration remain stable?

Yes → Consider a Reservation/Reserved Instance.

No → Continue.

Can you commit to a stable hourly spend?

Yes → Consider a Savings Plan.

No → Stay flexible until usage becomes predictable.

This is much safer than purchasing commitments simply because a provider advertises a large discount.

What Not to Commit

Avoid committing aggressively to:

• Temporary migration infrastructure
• Development environments with unpredictable schedules
• Newly launched applications
• Seasonal workloads
• Projects scheduled for retirement
• Workloads being migrated to another platform
• Infrastructure undergoing major modernization

These workloads can disappear before the commitment expires.

A 40% discount on infrastructure you no longer use is still wasted money.

How Much of Your Cloud Usage Should You Commit?

There is no universal percentage.

A better approach is to identify your stable baseline.

For example:

Usage category	Monthly spend	Commitment strategy
Stable production	$30,000	Strong candidate
Stable database workloads	$10,000	Strong candidate
Development	$5,000	Partial/limited
Seasonal workloads	$8,000	Keep flexible
New projects	$7,000	Avoid long commitment

In this example, the organization might commit primarily against the $40,000 stable baseline rather than the entire $60,000 monthly spend.

Even then, historical utilization should be reviewed before deciding the exact commitment level.

Monitor Your Commitments After Purchase

Buying a commitment is not the end of FinOps.

Track:

Utilization

How much of the purchased commitment is actually being used?

Coverage

What percentage of eligible usage is receiving discounted pricing?

Effective savings

How much are you actually saving compared with On-Demand?

Unused commitment

How much purchased capacity or spend is going unused?

Expiration

When do commitments expire?

Workload changes

Are upcoming migrations or modernization projects likely to reduce usage?

AWS and Azure both provide mechanisms to monitor reservation/commitment usage and utilization. Azure, for example, exposes reservation utilization and usage information through the Azure portal and APIs.

The FinOps Strategy That Usually Works Best

A mature commitment strategy generally follows three stages.

Stage 1: Observe

Run workloads without aggressive commitments long enough to understand:

• Baseline usage
• Growth
• Seasonality
• Instance changes
• Migration plans
• Business-critical workloads

Stage 2: Commit

Commit only against usage that is sufficiently predictable.

Start with the strongest baseline rather than trying to maximize coverage immediately.

Stage 3: Optimize

Review commitments regularly.

If utilization is low:

• Investigate why.
• Adjust future purchases.
• Change scope where possible.
• Consolidate eligible usage.
• Consider exchanges or other supported flexibility mechanisms.
• Avoid repeating the same overcommitment.

This turns commitment purchasing into a continuous FinOps process rather than a one-time procurement exercise.

AWS Savings Plans vs RIs vs Azure Reservations: Final Comparison

If your situation is…	Best starting point
AWS compute spend is stable but infrastructure may change	Compute Savings Plan
AWS EC2 family and Region are highly stable	EC2 Instance Savings Plan
AWS EC2 configuration is extremely predictable	Reserved Instance
Azure compute spend is stable but resources may change	Azure Savings Plan
Azure VM configuration is stable	Azure Reservation
Workload is temporary or highly unpredictable	On-Demand/flexible pricing
Workload is being migrated soon	Avoid long commitments
You don’t understand your usage baseline yet	Collect usage data first

Bottom Line

AWS Savings Plans, AWS Reserved Instances, and Azure Reservations all solve the same fundamental problem:

Why pay On-Demand prices for infrastructure you know you will continue using?

But they solve it with different levels of commitment.

AWS Savings Plans are generally the better choice when flexibility matters. AWS Reserved Instances can still make sense when an EC2 configuration is highly predictable.

Azure Reservations are well suited to stable Azure resources, while Azure Savings Plans are better suited to eligible compute spending that may move between supported resources or regions.

The most important rule is simple:

Do not optimize for the largest discount. Optimize for the highest discount you can reliably use.

A smaller commitment with high utilization is usually better than a larger commitment with unused capacity.

Before purchasing a one- or three-year commitment, analyze at least 6–12 months of usage, remove temporary workloads, account for planned migrations, identify your stable baseline, and calculate the effective savings after considering utilization.

That is how commitment-based pricing becomes a genuine cloud cost optimization strategy rather than another source of wasted spend.