What's the Difference Between AWS Savings Plans and Reserved Instances?

AWS Savings Plans commit you to a fixed dollar-per-hour spend rate and automatically apply discounts across EC2, Fargate, and Lambda regardless of instance type. Reserved Instances commit to a specific instance family, size, and region for 1–3 years in exchange for deeper but rigid discounts. Savings Plans suit evolving, dynamic workloads. Reserved Instances suit stable, predictable compute with no planned architectural changes.

Quick Summary

• Savings Plans offer broad flexibility and apply across EC2, Fargate, and Lambda.
• Reserved Instances can provide deeper discounts but require fixed instance attributes (family, size, region).
• Most teams face underutilization risk because usage patterns shift due to right-sizing, autoscaling, and architectural changes.
• Flex Commitments provide SP/RI-level savings without AWS lock-in and include cashback protection for unused commitments.

Bottom line: If your cloud environment changes more than once per quarter, AWS Savings Plans offer safer coverage. If your compute hasn't changed in 18+ months, Standard RIs may offer a deeper discount. If you want commitment-level savings without absorbing AWS's financial risk on either option, Usage.ai's Flex Commitments add cashback protection to the equation and have delivered $91.9M+ in verified savings across 300+ enterprise customers.

Introduction

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 guide breaks down the full SP vs RI comparison, answers the questions FinOps teams actually ask (including whether you can use both together), and explains how Usage.ai's Flex Commitment and Cashback model introduces a smarter, safer way to capture discounts without absorbing commitment risk.

What is an AWS Savings Plan?

AWS Savings Plans are a flexible pricing model introduced by AWS in 2019 that allows organizations to reduce compute costs by committing to a consistent hourly spend on compute resources over a one- or three-year term.

Instead of committing to specific infrastructure configurations, organizations commit to a fixed dollar amount of compute usage per hour (for example, $20/hour). AWS then automatically applies the discounted rate to any eligible compute usage until the commitment is fully utilized.

If usage exceeds the committed spend, additional consumption is billed at standard on-demand pricing.

This design allows the discount to automatically adapt to changing infrastructure, making Savings Plans particularly useful for environments where workloads scale dynamically or infrastructure changes frequently.

Key characteristics:

• Applies automatically across EC2, Fargate, and Lambda (Compute SP)
• Works across instance families, sizes, and many regions
• No machine-level lock-in (only a dollar-per-hour commitment)
• Discounts can reach ~72%, depending on term and payment option
• Designed for teams with evolving or unpredictable compute patterns

When AWS Savings Plans make sense: Dynamic environments, containerized workloads, serverless adoption, frequent instance-family upgrades, or multi-account architectures.

‍

Also read: What is Cloud Unit Economics?

‍

Types of AWS Savings Plans

AWS offers three Savings Plan types, each designed with a different flexibility profile:

Compute Savings Plans (most flexible): Apply across all EC2 instance families, all sizes, all regions, plus AWS Fargate and AWS Lambda. If you switch from m5 to m6i, or scale from medium to xlarge, or move workloads to containers, your discounts follow automatically. This is the right choice for teams with diverse or fast-changing compute patterns.

EC2 Instance Savings Plans (slightly higher discount): Apply only to a specific EC2 instance family within a single region (e.g., m6i in us-east-1). They offer a marginally better discount than Compute SPs but require more forecasting accuracy. Moving to a new instance family or region breaks coverage entirely.

SageMaker Savings Plans: For organizations running ML training, inference, and processing workloads at scale, AWS offers SageMaker-specific Savings Plans. These follow the same spend-commitment model but apply exclusively to SageMaker usage. If your team is growing AI/ML infrastructure, model SageMaker SPs separately from your compute commitments. They don't overlap with EC2 or Fargate discounts.

What are AWS Reserved Instances (RIs)?

AWS Reserved Instances (RIs) are a long-standing pricing model that allows organizations to receive discounted compute pricing in exchange for committing to specific infrastructure configurations for a fixed term.

When purchasing a Reserved Instance, organizations commit to attributes such as:

• Instance family (e.g., m5, c6g)
• Instance size (optional depending on scope)
• AWS region
• Operating system
• Tenancy type

‍

Because the commitment is tied directly to infrastructure configuration, AWS can provide higher discounts compared to on-demand pricing, often reaching up to 75% savings for long-term commitments. However, this rigidity also introduces infrastructure planning risk if workloads change.

Key characteristics:

• Locked to exact configurations (e.g., m5.xlarge in us-east-1)
• Standard RIs offer the deepest EC2-specific discounts (up to ~72%)
• Convertible RIs allow changes but only to equal or greater value
• Does not apply to Fargate or Lambda
• Higher risk if your compute architecture changes

When RIs make sense: Long-lived workloads, legacy monoliths, stable databases, predictable EC2 fleets where the instance family, size, and region are unlikely to change.

‍

Also read: AWS Budgets vs Cost Explorer: Key Differences Explained

‍

Standard RIs vs. Convertible RIs 

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 offer more flexibility. 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, but the manual overhead of tracking coverage and executing exchanges makes them operationally expensive compared to more automated approaches.

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. 

Regional RIs vs. Zonal RIs

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. 

‍

Also read: 18 Proven Ways to Cut 30–50% of Your Cloud Bill 

‍

The Strengths of Reserved Instances

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.

1. Deepest EC2-Specific Discounts

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.

2. Ideal for Extremely Predictable Workloads

Some workloads genuinely don’t move around much. These long-lived systems are where RIs shine:

• Legacy application servers
• Steady-state databases
• Analytics clusters that rarely resize
• Regulated workloads with strict placement requirements

3. Useful in Environments with Very Little Architectural Change

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.

Where Reserved Instances Break in Modern Engineering

RIs 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.

RIs fall apart when:

• You change instance families (m5 → m6i → m7i)
• You move workloads to containers or serverless
• You adopt Graviton
• You autoscale aggressively
• You right-size infrastructure
• You shift regions
• You deprecate or merge services
• Engineering makes changes without informing FinOps

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 Hidden Cost of RIs: Why You Pay for What You Don’t Use

The biggest hidden cost of RIs is its underutilization, because AWS offers zero protection when RIs go unused.

Here’s the uncomfortable math:

• If your usage drops, you still pay for the full RI.
• If engineering shifts instance families, RI coverage drops to zero immediately.
• If workloads shrink seasonally, unused RIs become stranded costs.

This is why many companies either:

• Under-buy RIs out of caution, or
• Buy aggressively and regret it when the environment shifts

RIs reward perfect forecasting. Unfortunately, real engineering environments rarely behave perfectly.

‍

Also read: Cloud Waste in AWS, Azure, and GCP: How to Eliminate It

‍

AWS Savings Plans: The Modern Flexible Standard for AWS Discounts

AWS 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.

How Savings Plans Work (Compute SP vs EC2 Instance SP)

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:

Compute Savings Plans (CSPs)

• Compute Savings Plans apply across all EC2 instance families, meaning you can switch from an m5 to an m6i to an m7i without losing discount coverage.
• They also apply across all EC2 instance sizes, so autoscaling can move from medium to large to xlarge and still remain fully discounted.
• Compute SPs apply globally across regions, which enables teams operating in multi-region architectures to stay protected even as workloads migrate or expand geographically.
• They discount AWS Fargate and AWS Lambda, making Compute SPs the only AWS commitment type that spans VM-based, container-based, and serverless compute layers.
• Compute SPs adapt seamlessly when organizations adopt new hardware generations such as Graviton, ensuring that modernization efforts don’t break coverage or invalidate prior commitments.

EC2 Instance Savings Plans (Slightly Higher Discount)

• EC2 Instance Savings Plans apply only to a specific EC2 instance family within a single region, such as m6i in us-east-1, making them less flexible but potentially slightly cheaper.
• They do not protect workloads if engineering shifts to a new family, such as moving from m6i to m7i or from x86 to Graviton-based instances.
• They also do not cover Fargate or Lambda, which makes them less suitable for organizations with mixed compute patterns.
• EC2 Instance SPs require more forecasting accuracy, as even small architectural changes can disrupt coverage.

‍

Also read: 10 Biggest Cloud Cost Optimization Challenges (and How to Solve Them)

‍

Strengths of Savings Plans 

Savings Plans solve many of the operational bottlenecks associated with Reserved Instances, making them well-suited for fast-moving engineering teams.

1. Savings Plans provide architectural flexibility that aligns with modern engineering practices.

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.

2. Savings Plans automatically discount multiple compute services that exist across the modern stack.

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.

3. Savings Plans dramatically reduce operational complexity compared to Reserved Instances.

They eliminate the need for conversions, instance-type tracking, and configuration-specific governance workflows, which significantly reduces coordination across FinOps, platform teams, and engineering.

4. Savings Plans accommodate gradual modernization, such as shifting toward Graviton.

As teams migrate from x86 to ARM-based architectures, Savings Plans follow those changes automatically, preventing discount loss during transitional periods.

5. Savings Plans can support a multi-region strategy without requiring region-specific commitments.

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.

Limitations of Savings Plans

Even with significantly better flexibility, Savings Plans still fall short in several important areas.

1. Savings Plans still rely on accurate forecasting of future spend.

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.

2. Savings Plans do not protect against seasonal or event-driven volatility.

Teams with large traffic swings, such as retail, travel, or streaming may be locked into high hourly commitments during periods of low activity.

3. Savings Plans do not offer AWS-native underutilization guarantee.

If an SP is unused, AWS does not reimburse customers in any form; the financial risk remains entirely on the buyer.

4. Savings Plans are still multi-year commitments with limited flexibility around term structure.

They require a 1- or 3-year commitment, which may not align with modern agile or growth-stage infrastructure planning.

5. Savings Plans can be mis-sized if engineering makes even small changes.

Right-sizing, moving workloads to spot, or shifting to a new architecture may drop spend enough to create underutilization.

‍

Also read: AWS Database Savings Plans Explained for DB Teams

‍

Savings Plans vs Reserved Instances: The Definitive Comparison

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:

Decoding The 10 Strategic Difference Between SP and RI

1. What You Commit To

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.

2. Flexibility

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.

3. Services Covered

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.

4. Discount Range

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.

5. Architectural Drift Tolerance

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.

6. Autoscaling Compatibility

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.

7. Modification or Conversion Effort

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.

8. Ideal Workloads

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.

9. Underutilization Risk

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. 

10. Recommendation Freshness

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.

‍

Also read: Cloud Cost Analysis: How to Measure, Reduce, and Optimize Spend

‍

The Shared Failure Point: Both SPs and RIs Are Unprotected

AWS Savings Plans and Reserved Instances share one critical, structural weakness: AWS provides zero financial protection if your usage drops.

If your commitments go unused:

• AWS keeps the money
• You receive no reimbursement of any kind
• You absorb 100% of the financial risk

This isn't a design flaw you can engineer around. It's built into the AWS commercial model. Whether you choose the more flexible Savings Plan or the more rigid Reserved Instance, the outcome of underutilization is identical. You pay for something you didn't use, and AWS doesn't give it back.

Can You Use AWS Savings Plans and Reserved Instances at the Same Time?

Yes, and many mature FinOps teams do. The key is understanding how AWS applies commitment discounts in priority order when multiple commitment types are active:

1. Zonal Reserved Instances apply first (most specific, highest discount for exact AZ/instance match)
2. Regional Reserved Instances apply next (region-wide, no AZ restriction)
3. EC2 Instance Savings Plans apply after (instance family, single region)
4. Compute Savings Plans apply last (broadest coverage, all regions/services)

This stacking logic means you can use RIs for your most stable, predictable workloads. For example, a production RDS cluster or a legacy EC2 fleet that hasn't changed instance families in two years, while using Compute Savings Plans to cover the rest of your dynamic EC2, Fargate, and Lambda usage.

However, you still carry two separate underutilization exposures. If usage drops on either commitment type, AWS doesn't compensate you on either.

AWS Savings Plans vs Reserved Instances: Decision Framework

Below is a step-by-step framework you can use before purchasing any AWS commitment:

Step 1: Assess forecasting confidence

• Can you predict EC2 usage within ±10% for 12+ months? Consider Standard RIs for those specific workloads
• Usage varies more than ±20%? Savings Plans are significantly safer

Step 2: Check engineering velocity

• Has your instance family changed in the last 6 months? Savings Plans (RIs will break alignment)
• Stable, unchanged instance types for 18+ months? RIs may qualify; model utilization carefully
• Actively migrating to Graviton or new generations? Savings Plans only; RIs will not follow the migration

Step 3: Map your compute surface

• Running Fargate, Lambda, or containers alongside EC2? Only Compute Savings Plans cover all three
• Pure, stable EC2 with fixed configurations? RIs or EC2 Instance SPs both work; RIs offer deeper discount
• Multi-region deployments? Compute SPs span regions; RIs are region-specific

Step 4: Evaluate underutilization risk tolerance

• Zero tolerance for stranded costs? Flex Commitments with cashback protection eliminate this risk entirely
• Comfortable accepting AWS's no-reimbursement terms? Native SPs and RI blend with careful coverage monitoring
• Seasonal or event-driven usage spikes? Avoid locking into high commitments; Flex Commitments adjust dynamically

Step 5: Check governance requirements

• Need approval workflows before any commitment is purchased? Neither native SP nor RI provides this natively
• Multi-org or multi-account oversight required? AWS cost tools are account-scoped; you need external orchestration
• Engineering, Finance, and FinOps need a shared view? Usage.ai provides cross-team approval workflows, daily recommendations, and unified reporting

‍

Also read: Cloud Cost Monitoring vs Cost Control: What’s the Real Difference?

‍

What Are Flex Commitments? (Usage.ai's Answer to the SP vs RI Dilemma)

Flex Commitments are Usage.ai's alternative to AWS-native commitments. They deliver Savings Plan or Reserved Instance–level discounts while removing the long-term lock-in and underutilization risk that make native AWS commitments financially dangerous for most engineering teams.

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 that AWS never offers.

Think of it this way. AWS's commitment model is like paying 12 months of gym membership upfront, knowing you might only go for six. Usage.ai's model is like paying for the months you actually attended and getting a refund for the ones you didn't.

Key features of Flex Commitments

• SP/RI-level discounts without AWS lock-in: Flex Commitments deliver similar savings benefits but do not require a multi-year contractual commitment through AWS directly.
• Cashback protection for unused commitments: If part of a Flex Commitment goes unused, Usage.ai reimburses the unused portion in real cash (via ACH/wire), not credits. Cashback is calculated monthly and visible in the Usage.ai billing dashboard before payout. 
• 24-hour forecasting refresh: Recommendations update every 24 hours based on actual recent usage and not a multi-week lookback window. This means you're always acting on current data, not data that's 72+ hours stale the way AWS native tools operate.
• Full customer approval workflow: No commitments are purchased automatically. Every recommendation requires explicit customer sign-off. Your team stays in complete control.
• Fees only on realized savings: Usage.ai charges approximately 20% on EC2 savings (higher for more complex services like RDS). If you don't save money, you don't pay anything. If you save $0, the fee is $0.
• Broader service coverage. While AWS Savings Plans cover EC2, Fargate, and Lambda, Flex Commitments extend to RDS, ElastiCache, Redshift, DynamoDB, OpenSearch, and more services where native commitment tools are more limited.

When Flex Commitments Are the Right Choice

Flex Commitments are useful in environments with:

• Usage is highly volatile (autoscaling, seasonality, traffic spikes)
• Your team is undergoing active modernization (x86 → Graviton, EC2 → Fargate)
• You operate a multi-region or multi-account architecture
• You have low organizational tolerance for financial lock-in risk
• Finance and Engineering teams have conflicting forecasting confidence levels
• You want commitment-level savings but can't ensure stable configurations or conservative forecasting windows

Choosing Between Savings Plans, Reserved Instances, and Flex Commitments

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 Accuracy: For predictable usage (±10% over 12+ months), RIs may be viable for specific workloads. Whereas for moderate fluctuation (±20–40%), Savings Plans are safer. For highly variable or unpredictable usage, AWS-native commitments become difficult to size accurately. Flex Commitments remove the risk.

‍

Engineering Velocity: Frequent architectural change, like moving between EC2 generations, migrating from EC2 to Fargate, adopting Graviton, or changing regions reduces RI effectiveness to near zero. Savings Plans tolerate more change but still depend on consistent spend levels.

‍

Modernization Roadmap: RIs generally break during modernization. Savings Plans adapt better but still carry usage risk if spend drops. Flex Commitments are designed for exactly this transition period. They absorb the financial risk that accompanies any architectural change.

‍

Usage Volatility: Seasonal spikes, batch workloads, and shifting demand create underutilization exposure with both native commitment types. Flex Commitments address this directly through cashback on underutilized portions.

‍

Compute Surface Mix: Modern workloads span EC2, EKS, Fargate, Lambda, GPU, and Spot. RIs apply only to EC2 and require alignment. Compute SPs cover multiple surfaces but rely on consistent overall spend.

‍

Governance and Compliance: Neither SPs nor RIs include native approval workflows, cross-account oversight, or change-management compliance. Usage.ai provides all three.

‍

Organizational Risk Tolerance: RIs have the highest risk around configuration and usage drift. Savings Plans have medium risk around usage drift only. Flex Commitments gets you the lowest risk via. cashback protection. 

Where Flex Commitments Fit Into This Framework

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'?

‍

Looking Forward: How to Save Up to 50% of Your Cloud Spend with Usage.ai

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.

Across 300+ enterprise customers, Usage.ai has delivered $91.9M+ in verified cloud savings with 30–50% reductions achieved within 60 days, zero code changes, and full cashback protection on every commitment.

The platform connects to your AWS environment in 5–10 minutes (read-only access to metadata only), begins cost analysis immediately, and delivers personalized recommendations within 3–7 days. Commitments are never purchased without your explicit approval. 

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!

‍

Frequently Asked Questions

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. Are AWS Savings Plans better than Reserved Instances?

For most modern engineering environments, yes. Savings Plans are better when workloads evolve, teams adopt new instance families, or compute spans Fargate and Lambda. RIs are better only when the compute footprint is highly stable and unchanged for extended periods, typically 18+ months. The deeper discount RIs offer only delivers more value if you maintain near-perfect utilization.

‍

3. Can you use AWS Savings Plans and Reserved Instances at the same time?

Yes. AWS applies commitment discounts in priority order: Zonal RIs first, Regional RIs second, EC2 Instance SPs third, Compute SPs last. Many mature FinOps teams use RIs on their most stable workloads and Savings Plans to cover dynamic compute.

‍

4. What happens if I don't use my Savings Plan or Reserved Instance?

AWS does not reimburse unused Savings Plans or Reserved Instances. If your usage drops below your commitment level, you continue paying the committed hourly rate. This is the core financial risk of both commitment types and exactly why Usage.ai's Flex Commitments include real cashback for unused portions paid via ACH or wire.

‍

5. Can I cancel an AWS Savings Plan or Reserved Instance early?

AWS Savings Plans cannot be cancelled once purchased. Standard Reserved Instances can be sold on the AWS Marketplace (with listing fees and no guaranteed buyer), but there's no exit path for Savings Plans or Convertible RIs. This inflexibility is one of the primary drivers organizations turn to Flex Commitments.

‍

6. How much can I save with AWS Savings Plans vs Reserved Instances?

Savings Plans offer up to ~66% savings versus On-Demand pricing. Standard Reserved Instances offer up to ~72% for 3-year, all-upfront commitments. The gap narrows significantly when you factor in underutilization. Note that an RI at 70% utilization delivers less real savings than a Savings Plan at 100% utilization.

‍

7. 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.

‍

8. 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.

‍

9. Does Usage.ai provide real cashback or just credits?

Real money. When a Flex Commitment is underutilized, Usage.ai pays back the unused portion via ACH or wire transfer. This is one of the most significant differentiators between Usage.ai and AWS-native commitment tools.

‍

10. Does Usage.ai automatically buy Savings Plans or RIs for me?

No. Usage.ai never auto-purchases anything without explicit approval. Every recommendation goes through a customer-controlled approval workflow so your team stays fully in control of what gets committed.

‍

11. 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, including AWS, Azure, and GCP.

‍

12. How is Usage.ai priced?

Usage.ai charges only on realized savings and not subscriptions or platform fees. For EC2 commitments, the fee is approximately 20% of savings; it may be higher for more complex workloads like databases. If you save $0, you pay $0.

‍

13. Can Usage.ai help if my usage is highly unpredictable?

Absolutely. Flex Commitments were designed for volatile or fast-changing environments. The combination of daily optimization and real cashback makes unpredictable usage patterns safe to commit against.

‍

‍

‍