AWS Savings Plans and Reserved Instances look like straightforward financial tools. They promise predictable discounts in exchange for predictable usage. But beneath that simplicity lies one of the most misunderstood and financially sensitive subjects of cloud management.
With autoscaling, spot usage, and frequent deployments, modern cloud environments don’t behave predictably. Usage shifts daily, architectures evolve, and engineering teams adopt new compute types long before old commitments expire. That volatility turns multi-year AWS commitments into a forecasting exercise most organizations can’t reliably perform.
This mismatch is the root cause of stranded Reserved Instances, underutilized Savings Plans, and millions in avoidable waste. It’s not that teams don’t want savings; it’s that AWS makes savings dependent on predictions no one can reliably make.
This article breaks down why the standard SP vs RI debate misses the real point and how a Flex Commit + Cashback model introduces a smarter, safer way to capture discounts without taking on commitment risk.
An AWS Savings Plan is a commitment to spend a fixed amount (e.g., $20/hour) on compute for 1 or 3 years in exchange for discounted pricing. Unlike Reserved Instances, the commitment is tied to spend, not to a specific EC2 machine.
Key characteristics:
When AWS Savings Plans make sense:
Dynamic environments, containerized workloads, serverless adoption, frequent instance-family upgrades, or multi-account architectures.
Reserved Instances (RIs) are a commitment to use specific EC2 instance attributes for 1 or 3 years in exchange for lower hourly rates. The discount applies only when your running instances match all of the RI’s defined attributes, like family, size, region and tenancy.
Key characteristics:
When RIs make sense:
Long-lived workloads, legacy monoliths, stable databases, predictable EC2 fleets.
Reserved Instances (RIs) were AWS’s original answer to cloud cost reduction long before Savings Plans or Flex Commitments existed. They offer some of the deepest possible discounts on EC2 compute, often higher than Savings Plans when fully utilized.
When you purchase an RI, you’re making a long-term commitment to AWS: “We will run this specific EC2 instance for the next 1 or 3 years.”
That commitment includes:
But this model assumes your environment will stay predictable for years at a time, and most engineering teams simply don’t operate that way. Right-sizing, moving to new instance families, shifting regions, or adopting containers can break RI alignment instantly. Even small architectural changes can leave an RI unused, which is why they carry the highest utilization risk of any AWS commitment.
Reserved Instances come in two types: Standard and Convertible.
Standard RIs offer AWS’s absolute best EC2 discounts, but they do it by locking you into a very specific instance setup for one to three years. You can’t change families, you can’t resize, and you can’t shift regions. They only make sense when you know the environment will stay exactly the same for a long time.
Convertible RIs sound like a more flexible alternative, and in some ways they are. You can switch instance families or configurations, but only if the new option costs the same or more. That caveat turns many conversions into an upsell rather than a true adjustment.
For sophisticated FinOps teams, Convertible RIs can still be a powerful instrument and are often the closest AWS-native competitor to Savings Plans. The trade-off is the manual overhead of tracking coverage and executing exchanges, which makes them operationally expensive compared to more automated approaches like Flex Commitments.
Moving on, AWS also offers two very different behaviors: one designed for flexibility across an entire region, and the other for capacity guarantees in a single Availability Zone.
The difference between Regional and Zonal Reserved Instances determines how easily your RI coverage follows your workloads as they scale, shift, or evolve.
Regional RIs apply across an entire AWS region, which means the discount automatically follows your workloads no matter which Availability Zone they run in. This makes them ideal for modern architectures that lean on autoscaling groups, multi-AZ deployments, or Kubernetes clusters that rebalance workloads dynamically. Because they aren’t tied to a specific zone, Regional RIs adapt naturally to the way resilient, distributed systems operate.
Zonal RIs, by contrast, tie the commitment to one specific Availability Zone and include a built-in capacity reservation. That reservation can be helpful in rare cases where you must guarantee EC2 capacity in a single zone, such as certain high-throughput legacy systems or regulated workloads.
Reserved Instances may be rigid, but they still deliver meaningful value when the environment is stable enough to support them. In the right conditions, RIs are straightforward, predictable, and capable of outperforming Savings Plans, especially when workloads barely change over time.
When fully utilized, Standard 3-year RIs offer AWS’s strongest EC2 discounts. If you already know exactly which instance family, size, and region you'll need for years, RIs can beat Savings Plans on pure savings potential alone.
Some workloads genuinely don’t move around much. These long-lived systems are where RIs shine:
If your team knows the instance family, size, region, and architecture will hold steady for years, reserved capacity becomes a reliable way to lock in savings without daily recalibration.
RI's assume a level of compute stability that is increasingly rare. Modern engineering teams move fast, and even small architectural shifts can cause an RI to stop applying.
RI's fall apart when:
Every one of these events breaks alignment. When alignment breaks, the RI stops covering usage, leaving you to pay for a commitment you’re no longer using.
The biggest hidden cost of RIs is its underutilization, because AWS offers zero protection when RIs go unused.
Here’s the uncomfortable math:
This is why many companies either:
RIs reward perfect forecasting. Unfortunately, real engineering environments rarely behave perfectly.
Savings Plans were created to solve the fundamental limitation of Reserved Instances: cloud evolves faster than configuration-locked commitments can handle.
Instead of committing to a specific EC2 instance family, size, and region for years, Savings Plans let you commit to a level of hourly spend, delivering strong discounts while accommodating the reality of architectural change.
Savings Plans allow customers to commit to a specific dollar-per-hour spend, for example, $10/hour or $20/hour for a 1- or 3-year term. As long as your usage meets or exceeds that hourly commitment, AWS applies discounted rates automatically.
This spend-based model frees teams from the rigid EC2 configuration constraints that define Reserved Instances.
AWS offers two Savings Plan types, each intentionally designed with a different flexibility profile:
Savings Plans solve many of the operational bottlenecks associated with Reserved Instances, making them well-suited for fast-moving engineering teams.
This includes the ability to change instance families, instance sizes, and underlying architectures without invalidating the discount. This can be a requirement in environments that routinely adopt new compute generations or shift across hardware types.
Coverage spans EC2, Fargate, and Lambda (for Compute SPs), giving organizations the ability to mix serverless, containerized, and VM workloads without managing multiple commitment models.
They eliminate the need for conversions, instance-type tracking, and configuration-specific governance workflows, which significantly reduces coordination across FinOps, platform teams, and engineering.
As teams migrate from x86 to ARM-based architectures, Savings Plans follow those changes automatically, preventing discount loss during transitional periods.
Workloads that regularly shift between us-east-1, us-west-2, and eu-central-1 remain covered under Compute SPs, greatly simplifying planning for global deployments.
Even with significantly better flexibility, Savings Plans still fall short in several important areas.
If spend declines because of right-sizing, optimization, or unexpected downtime, the commitment continues billing at the pledged hourly rate, creating the same type of underutilization risk as RIs.
Teams with large traffic swings, such as retail, travel, or streaming may be locked into high hourly commitments during periods of low activity.
If an SP is unused, AWS does not reimburse customers in any form; the financial risk remains entirely on the buyer.
They require a 1- or 3-year commitment, which may not align with modern agile or growth-stage infrastructure planning.
Right-sizing, moving workloads to spot, or shifting to a new architecture may drop spend enough to create underutilization.
Savings Plans eliminate much of the rigidity that makes Reserved Instances difficult to maintain, but they still require accurate forecasting and carry the same financial risk when usage dips below the committed spend level.
This is why many organizations evaluating aws savings plans vs reserved instances struggle to determine which option delivers better long-term value. Below is a practical comparison:
Savings Plans commit you to a dollar-per-hour spend level, not a specific machine. This means that if you commit to $20/hour, AWS applies discounted rates to compute usage until your hourly bill reaches that commitment. This model is inherently resilient to architectural change.
Reserved Instances commit you to a specific machine shape, including the instance family (e.g., m6i), instance size (e.g., large), region (e.g., us-east-1), and tenancy. Because these attributes are hard-locked, even small engineering changes can reduce or eliminate RI alignment. This makes forecasting accuracy essential and often unrealistic.
Savings Plans provide high flexibility because they follow your compute strategy wherever it goes. Teams can upgrade to new generations, right-size workloads, adopt Graviton, or deploy across multiple sizes and families without losing discount coverage (Compute SP).
Reserved Instances are low-flexibility instruments. They require workloads to remain consistent for years. If engineering moves from m5.xlarge to m6i.xlarge, or from large to medium, or from us-east-1 to us-west-2, RI coverage may break entirely, leaving companies paying for unused commitments.
Savings Plans cover EC2, Fargate, and Lambda (Compute SP). This is increasingly important as organizations adopt Kubernetes on Fargate, serverless architectures, or a mix of compute surfaces.
Reserved Instances cover only EC2. They cannot discount Lambda, Fargate, or any containerized workloads that move execution layers outside pure EC2.
Savings Plans offer up to ~66% savings, depending on payment structure and term. These discounts are strong but optimized for flexibility over depth.
Reserved Instances can offer up to ~72% savings, especially for 3-year Standard RIs with all-upfront payment. These deeper discounts reflect the rigidity AWS asks customers to accept. But depth only matters if you can maintain high utilization which is the core problem RIs present.
Savings Plans tolerate architectural drift exceptionally well. Changes in instance families (m5 → m6i), sizes (large → xlarge), or compute types (EC2 → Lambda) don’t affect coverage. SPs continue working even when engineering re-architects services, migrates to containers, or modernizes infrastructure.
Reserved Instances tolerate drift poorly. The rigid configuration requirements make RIs highly vulnerable to engineering decisions made outside FinOps. Even small shifts in architecture can eliminate RI coverage and turn commitments into sunk cost.
Savings Plans handle autoscaling patterns perfectly, because they discount the spend, not the machine. Whether your ASG shifts between medium, large, or xlarge nodes throughout the day, SP discounts remain applied.
Reserved Instances are unreliable for autoscaling. If workloads scale into new sizes or if instance types fluctuate, RIs will only apply during the exact window when usage matches the RI configuration. This makes RIs impractical for highly elastic workloads.
Savings Plans require no modifications. There are no conversions, recalibration, or rebalancing. Once purchased, SPs simply apply.
Reserved Instances require ongoing maintenance. Convertible RIs, while more adaptable, still require engineering-heavy calculations and must always be converted into equal or greater value footprints. Standard RIs have minimal modification options and often become obsolete if environments shift.
Savings Plans are ideal for dynamic, evolving environments, including Kubernetes clusters, serverless workloads, multi-account environments, and organizations undergoing modernization.
Reserved Instances are ideal for stable, long-lived workloads, such as legacy monolith servers, static databases, or predictable, unchanging compute shapes. If you know your EC2 footprint will be identical for multiple years, RIs can be attractive.
Savings Plans carry medium underutilization risk, because risk is tied to usage dropping below the committed hourly spend. SPs protect against configuration drift but not usage volatility.
Reserved Instances carry high underutilization risk, because both configuration drift and usage reduction can cause commitments to go unused. This dual-risk profile is why many teams under-buy RIs.
Savings Plans rely on AWS recommendation models that look back over several weeks of usage. Recommendations are generated daily, but they’re based on those longer historical windows, which can make them slow to react in very fast-changing environments.
Reserved Instances face the same issue, since the AWS recommendation engine uses the same multi-week historical data to drive both RI and SP suggestions.
Savings Plans and Reserved Instances differ in design, but both share a critical weakness:
AWS will not protect you if your usage drops.
If your commitments go unused:
Flex Commitments are Usage.ai’s alternative approach to AWS-native commitments. They are designed to provide Savings Plan or Reserved Instance–level discounts while removing the long-term lock-in and underutilization risk associated with AWS’s 1–3 year commitments.
Instead of committing to a specific instance family (RIs) or a fixed hourly spend (SPs), Flex Commitments adjust dynamically based on actual usage and include built-in financial protection.
Flex Commitments are useful in environments with:
Learn more about Usage.ai’s Flex Commitment Program
Mature FinOps teams evaluate AWS commitments using structured, risk-aware criteria that consider forecasting accuracy, engineering velocity, modernization efforts, and organizational constraints.
The following dimensions are commonly applied when selecting between Savings Plans and Reserved Instances.
Forecasting consistency is the single largest factor in commitment choice.
Frequent architectural change reduces RI effectiveness. Examples include:
RIs align only when underlying compute shapes remain stable. Savings Plans tolerate more change but still depend on consistent spend.
Modernization initiatives introduce configuration drift that affects commitment alignment:
RI's generally break during modernization. Savings Plans adapt better, but still carry usage risk if spend drops.
Seasonal spikes, batch workloads, and shifting demand often lead to underutilized commitments.
Teams with substantial usage variability must manage risk carefully, as AWS does not reimburse unused commitments.
Modern workloads span multiple compute types:
RIs apply only to EC2 and require alignment. Savings Plans cover multiple surfaces (Compute SP), but still rely on consistent overall spend.
Some organizations require:
Neither SPs nor RIs include native approval workflows, so governance must be managed externally.
Commitment selection often depends on willingness to accept financial exposure:
Flex Commitments address many of the risks noted above by removing AWS’s long-term lock-in, refreshing recommendations daily, and offering cashback for unused commitment. They are often evaluated when teams require commitment-level savings but cannot ensure stable configurations, predictable usage, or conservative forecasting windows
Learn more: Which Commitments are Eligible Under the 'Flex-Commit Program'?
There are smarter ways to cut AWS costs than locking yourself into rigid one- or three-year commitments. Usage.ai helps you save more and with far less risk by combining intelligent commitment automation with real cashback protection.
With Usage.ai, cloud cost management becomes refreshingly simple. The platform automatically analyzes your environment, refreshes recommendations every 24 hours, and gives you SP/RI-level discounts without the AWS lock-in. And if your usage dips? You’re protected. Flex Commitments reimburse unused commitment in real cashback, giving FinOps teams the confidence to commit more aggressively without worrying about the downside.
Usage.ai also gives you the kind of financial clarity every team wants. Insights update daily, recommendations are always current, and everything runs through an approval workflow, meaning engineering, finance, and FinOps finally get a tool they all can trust.
Want to see how much more your team could save when commitment risk disappears? Usage.ai makes managing AWS commitments feel easy, lightweight, and actually enjoyable.
Interested in running a POC? We’d love to show you what safe, automated, cashback-protected savings can look like for your cloud environment. Sign Up now!
1. What’s the difference between AWS Savings Plans and Reserved Instances?
Savings Plans are more flexible than RIs and follow your compute usage even if you change instance families or sizes. Reserved Instances are more rigid but can offer deeper discounts if your environment stays stable. Most teams don’t stay that stable, which is why both models carry risk if usage drops or architectures change.
2. Why do teams struggle to choose between Savings Plans and RIs?
Because AWS gives you discounts only if your future usage matches a long-term contract. If your usage dips, you still pay for the full commitment. That fear of underutilization leads to chronic under-commitment, even when teams know they could save more.
3.How does Usage.ai help me avoid overcommitting in AWS?
Usage.ai’s Flex Commitments give you SP/RI-level discounts without locking you into AWS terms. And if your usage drops, Usage.ai reimburses the unused portion as real cashback so you never pay for commitments you didn’t use.
4. Does Usage.ai really provide cashback?
Yes. When a Flex Commitment is underutilized, Usage.ai pays back the unused portion in real money. This is one of the biggest differences between Usage.ai and AWS-native commitments.
5. How often does Usage.ai update savings recommendations?
Usage.ai recalculates all commitment recommendations every 24 hours. AWS native recommendations can lag by up to a week, which means they’re often outdated by the time you act on them. Daily optimization is far better for teams with fast-changing environments.
6. Does Usage.ai automatically buy Savings Plans or RI's for me?
No. Usage.ai never auto-purchases anything. Every recommendation goes through a customer approval workflow so your team stays fully in control.
7. What happens if my engineering team right-sizes or modernizes our workloads?
With AWS-native commitments, usage changes can break your coverage. With Flex Commitments, Usage.ai simply adapts. You get protected when usage dips, and you continue saving when it rises. Modernization and right-sizing no longer create financial risk.
8. Does Usage.ai work across all AWS accounts and organizations?
Yes. Usage.ai is built for multi-account and multi-organization setups. All analysis, recommendations, and approvals work across entire cloud footprints without additional overhead.
9. How is Usage.ai priced?
Usage.ai charges only on realized savings, not subscriptions or platform fees. For compute commitments, the fee is around 20% of savings; it can be higher for more complex workloads like databases. If you don’t save money, you don’t pay anything.
10. Can Usage.ai help if my usage is highly unpredictable?
Absolutely. Flex Commitments were built for volatile or fast-changing environments. Daily optimization + cashback makes unpredictable usage patterns safe to commit.
Share this post
Compare AWS Savings Plans vs Reserved Instances with a practical breakdown. Learn tradeoffs and how Usage.ai’s Flex Commit + Cashback improves savings reliability.
Microsoft standardizes pricing for online services under Enterprise Agreements starting November 1, 2025, removing volume discounts and increasing costs for many enterprises. Azure expands AI and cloud service innovations to enhance automation, analytics, and sustainability. Infrastructure and reliability improvements include new Availability Zones and enhanced security compliance. Strategic implications focus on balancing cost impacts with increased AI capabilities and regional growth. Enterprises must prepare for pricing changes while leveraging new service capabilities for competitive advantage.
AWS is strategically rationalizing its product portfolio, moving 20+ services to maintenance or sunset phases to focus on innovation and resource allocation. AI tool enhancements in Bedrock and SageMaker reinforce AWS’s leadership in enterprise AI capabilities. Price reductions on GPU-powered EC2 instances up to 45% support cost-efficient AI and HPC workloads. Swift resolution of US-EAST-1 regional outage underscores AWS’s commitment to operational resilience. These updates reflect AWS’s overarching 2025 vision: prioritize AI-driven innovation, reliable infrastructure, and streamlined service offerings for enterprise scalability.
