More than 40% of EC2 instances run under 10% CPU utilization at peak—costing your business thousands every month. Yet many teams layer Reserved Instances on top of this waste, locking themselves into commitments they don’t need.
The real question isn’t just Reserved vs Spot vs On-Demand; it’s which model fits your actual usage pattern, risk tolerance, and financial goals. Choose wrong, and you’ll either overpay for idle capacity or scramble when critical workloads get interrupted.
This guide walks you through the trade-offs: pricing mechanics, availability guarantees, commitment levels, and the specific use cases where each option shines. By the end, you’ll know exactly how to structure your EC2 fleet for maximum cost efficiency without compromising performance.
Understanding the three AWS EC2 pricing models
AWS offers three primary ways to pay for EC2 compute: On-Demand, Reserved Instances, and Spot Instances. Each has distinct pricing, commitment requirements, and availability characteristics that fundamentally shape how you architect and budget your infrastructure.
On-Demand Instances are the default. You pay for compute capacity by the second with a 60-second minimum, with no upfront commitment or long-term contract. AWS bills per-second across all Regions and Availability Zones for Amazon Linux, Windows, RHEL, Ubuntu, and Ubuntu Pro. This flexibility comes at a price—On-Demand rates are the highest. For example, an m7i.large in us-east-1 might cost around $0.1008 per hour On-Demand. This model makes sense for unpredictable workloads, short-term projects, or testing new applications before committing to a pricing strategy.
Reserved Instances require you to commit to a specific instance type, region, and operating system for 1 or 3 years in exchange for a discount. Standard RIs can deliver up to 72% savings compared to On-Demand when you commit to a 3-year term with full upfront payment. You get three payment options: All Upfront (highest discount), Partial Upfront (moderate discount), and No Upfront (smallest discount but still substantial). The trade-off? You’re locked into that specific configuration. If your architecture changes or you migrate to a different instance family, your RI becomes less valuable or entirely wasted. Poor tagging and tracking causes 15-25% of RI waste because teams lose visibility into which commitments apply to which workloads.
Spot Instances let you bid on spare AWS capacity at discounts up to 90% off On-Demand pricing. The catch: AWS can reclaim Spot capacity with a two-minute warning when demand for On-Demand or Reserved capacity increases. Spot pricing fluctuates based on supply and demand within each availability zone and instance type. A c7i.2xlarge that costs $0.357 per hour On-Demand might be available as Spot for $0.054 during periods of low demand—85% cheaper. But when capacity tightens, that instance could be interrupted mid-task.
Each model exists because AWS customers have fundamentally different needs. Your job is to match the model to the workload’s tolerance for interruption, predictability of usage, and budget constraints.
Cost comparison: What you’ll actually pay
The headline discount numbers sound compelling, but real-world savings depend on how you combine these models and whether you’ve right-sized your instances first.
Take an m7i.2xlarge running 24/7 in us-east-1 as a baseline. On-Demand costs roughly $0.4032/hour or about $292/month. A 1-year Standard RI with Partial Upfront drops that to approximately $0.26/hour ($188/month)—a 35% reduction. A 3-year Standard RI All Upfront can reach $0.157/hour ($114/month), saving 61%. Meanwhile, Spot pricing for the same instance might average $0.06/hour ($44/month) during stable periods, an 85% discount.
The real power emerges when you layer optimizations. If you haven’t right-sized your instances, these discounts just make waste cheaper. Right-sizing can reduce EC2 costs by approximately 35% on its own. Combine that with a 3-year Reserved Instance and you’re looking at cumulative savings exceeding 70%. Add Graviton migration on top—which costs approximately 18-20% less per hour than comparable Intel Xeon instances—and you can push total savings to 50-55%. GOV.UK demonstrated exactly this path: they achieved 15% per-instance savings migrating from m6i (x86) to m7g (Graviton), with total savings reaching 55% when combined with right-sizing and Savings Plans.
The optimal strategy isn’t 100% Reserved or 100% Spot. Most teams should target 70-80% coverage through commitments for steady-state workloads, leaving 20-30% on On-Demand for variable peaks and unexpected growth. This balances cost savings with operational flexibility. For fault-tolerant workloads like batch processing, CI/CD pipelines, or analytics jobs, integrate Spot to capture 60-90% savings on that portion of your fleet.
Watch for hidden costs. Reserved Instances charge you whether you use the capacity or not. If you commit to 10 RIs but only run 7 instances consistently, you’re paying for 3 idle reservations. On the Spot side, interruptions create indirect costs—retries, checkpointing overhead, and engineer time spent making workloads Spot-tolerant. Factor those into your ROI calculation.
Commitment and flexibility: What you’re actually signing up for
The discount comes at the cost of flexibility. Understanding exactly what you’re committing to—and what happens when your needs change—is critical to avoiding expensive mistakes.
Reserved Instance commitments lock you into a specific instance family, size, region, and platform. A Standard RI for c7i.4xlarge instances in us-east-1 running Linux won’t apply if you later migrate to m7g instances, switch to Windows, or expand into eu-west-1. This rigidity has burned countless teams who forecasted poorly or whose architecture evolved faster than their 3-year commitment.
Convertible RIs offer a middle ground: you can exchange them for different instance families, operating systems, or tenancies during the term. The trade-off is a slightly lower discount—typically 54-66% versus the 72% maximum for Standard RIs. Convertible RIs suit teams who expect architectural shifts but still want meaningful savings on predictable baseline capacity.
EC2 Instance Savings Plans provide flexibility within a single instance family and region. Commit to spend $100/hour on R7 instances in us-east-1, and AWS applies that commitment across all R7 sizes (r7i.large, r7i.2xlarge, etc.) and operating systems. You’re not locked to a specific size, which helps when you right-size instances mid-term. The discount can reach up to 72% for 3-year commitments—matching Standard RIs—but only applies within that family and region.
Compute Savings Plans offer the broadest flexibility: your commitment applies across EC2 instance families, regions, operating systems, and even extends to Fargate and Lambda. Commit to $50/hour of compute spend, and AWS applies it wherever you use compute, automatically favoring the highest discount. The maximum discount is lower—up to 66%—but the freedom to migrate workloads, adopt new instance types, or shift regions without losing your commitment is invaluable for dynamic environments.
On-Demand and Spot have zero commitment. On-Demand gives you complete flexibility—launch and terminate instances at will with no penalty. Spot lets you request capacity without any term obligation, but AWS controls availability. You can’t “reserve” Spot capacity; you bid for whatever is available at that moment.
What happens when commitments don’t match usage? Unused RI hours are simply lost—you paid for them, but if the instance isn’t running, you get nothing back. Effective June 1, 2025, AWS restricts RIs and Savings Plans to single customer usage, blocking resellers and MSPs who previously pooled commitments across multiple end customers. This makes accurate forecasting even more critical.
Availability and interruption risk: When your instances disappear
On-Demand and Reserved Instances guarantee capacity availability. Spot Instances do not. That distinction fundamentally shapes which workloads belong on each model.
On-Demand capacity is always available (barring rare service outages or regional capacity constraints). Launch an On-Demand instance and it stays running until you terminate it. The same guarantee applies to Reserved Instances—your reservation ensures AWS holds that capacity for you. This predictability is essential for production web servers, databases, real-time APIs, and any workload where interruption means user-facing downtime or data loss.
Spot Instances can be interrupted at any time. When AWS needs the capacity back—because On-Demand or Reserved demand has spiked in that availability zone and instance type—you get a two-minute warning via an instance metadata signal or EventBridge. After two minutes, the instance is terminated. No exceptions. Spot interruption rates vary dramatically based on instance type and AZ, ranging from less than 5% interruption frequency for some families to more than 20% for highly sought-after compute-optimized types during peak hours.
The two-minute warning is enough to checkpoint your work and gracefully shut down—if you’ve built for it. Stateless workloads like containerized microservices behind a load balancer handle interruptions easily: the load balancer stops routing to the terminating instance, and a replacement spins up. Stateful workloads require checkpointing logic: save progress to S3 or DynamoDB, then resume from the checkpoint when a new Spot instance launches. Batch jobs, rendering pipelines, CI/CD builds, and big data processing are ideal Spot candidates if you design retry and checkpoint mechanisms.
Many teams run production workloads on a baseline of On-Demand or Reserved capacity, then scale out with Spot during traffic spikes. For example, cover your minimum 10 instances with Reserved Instances, keep 5 On-Demand for predictable daily peaks, and add 20 Spot instances when a marketing campaign drives unexpected load. The Spot layer absorbs burst traffic at 60-90% savings, and if AWS reclaims some Spot capacity, your baseline Reserved plus On-Demand instances keep the service running.
Use diversified Spot requests across multiple instance types and availability zones. Instead of requesting only c7i.2xlarge Spot instances in us-east-1a, request c7i.2xlarge, c6i.2xlarge, and m7i.2xlarge across us-east-1a, 1b, and 1c. This spreads risk; interruptions rarely hit all types and AZs simultaneously. Configure Auto Scaling groups with capacity-optimized allocation strategy—AWS automatically picks the Spot pools with the lowest interruption risk.
Each interruption triggers a replacement instance launch, increasing the likelihood of cold-start latency and momentary capacity shortfalls. For workloads sensitive to latency (think real-time bidding, gaming servers, or live video transcoding), even rare interruptions can degrade user experience. Quantify the business cost of interruption—if a two-minute outage on one instance loses $500 in revenue, Spot’s 85% discount might not offset the risk.
Best-fit use cases: Matching models to workloads
The right pricing model isn’t universal—it depends on your workload’s runtime pattern, tolerance for interruption, and budget predictability.
Use On-Demand for unpredictable, spiky traffic—your SaaS app sees 3x load during product launches or seasonal events. On-Demand lets you scale up instantly without commitment waste during quiet periods. It’s also ideal for short-term projects or testing, like spinning up a staging environment for two weeks to test a new feature. Committing to a Reserved Instance for such a brief need would lock in costs with zero return. For new workloads with unknown patterns, start on On-Demand, monitor for a quarter, then optimize with commitments once patterns stabilize.
Use Reserved Instances (or EC2 Instance Savings Plans) for steady-state, predictable workloads. Your production database runs 24/7 on r7i.4xlarge instances in us-east-1. Usage has been consistent for six months and you expect it to remain so for the next 1-3 years. Lock in a Standard RI or EC2 Instance Savings Plan for up to 72% savings. Even if your web tier scales from 10 to 50 instances during peaks, those baseline 10 instances run continuously. Cover them with Reserved Instances or a Compute Savings Plan, and use On-Demand or Spot for the variable 0-40 instance range. A financial services firm running a compliance workload that must use c7i instances in a specific region due to regulatory constraints should use a 3-year Standard RI to maximize savings, since the architecture won’t change.
Use Spot Instances for batch processing and analytics—ETL jobs, log processing, or data pipeline tasks that can retry if interrupted. Spot offers 60-90% savings and the stateless, idempotent nature of these jobs absorbs interruptions cleanly. CI/CD build fleets are perfect candidates: automated test runs and build jobs are inherently retriable. A two-minute interruption just means the job re-queues to another Spot instance. Teams running Jenkins, GitLab CI, or GitHub Actions on EC2 can cut CI costs by 70-85% with Spot.
Containerized workloads with horizontal scaling—microservices running in Kubernetes or ECS that can tolerate individual pod or task terminations—work well on Spot. Use Spot for worker nodes or ECS tasks, and let the orchestrator reschedule tasks to surviving nodes when interruptions occur. Machine learning training runs that checkpoint every N epochs can resume from the last checkpoint if a Spot instance is interrupted mid-training. The 85% discount on GPU instances (p4d, g5) makes Spot essential for cost-effective ML at scale. Dev, test, and staging environments that don’t require 100% uptime are ideal Spot candidates—run your entire staging environment on Spot instances and save 60-90%. If an instance gets interrupted during a manual test, just restart the test—no business impact.
Most mature AWS users run a blend. For example, a media company might use Reserved Instances for their always-on API tier (achieving 60% savings), Spot for video encoding jobs (85% savings), and On-Demand for the 20% variability in web traffic. This layered approach captures maximum savings while maintaining SLAs. When properly tuned, such mixed fleets can reduce total compute costs by 50-70% compared to pure On-Demand.
Savings Plans: The flexible alternative to Reserved Instances
Savings Plans represent AWS’s evolution beyond the rigidity of Reserved Instances. Instead of committing to specific instance configurations, you commit to a dollar-per-hour spend on compute for 1 or 3 years. AWS then applies that commitment as a discount wherever you use eligible compute resources.
A Reserved Instance says, “I will run 10 c7i.4xlarge instances in us-east-1 for 3 years.” A Savings Plan says, “I will spend $100/hour on compute for 3 years—apply it wherever I use EC2, Fargate, or Lambda.” The latter adapts as your architecture evolves.
Compute Savings Plans offer up to 66% savings by committing to a dollar-per-hour spend across any EC2 instance family, size, region, operating system, and tenancy. They also cover Fargate and Lambda. This flexibility suits teams migrating workloads between instance families (moving from C instances to M instances, for example), expanding into new regions, or adopting serverless services alongside EC2.
EC2 Instance Savings Plans can reach up to 72% discounts when committing to a specific instance family in one region with size flexibility within that family—matching Standard RI discounts. You get size flexibility (commit to M7 in us-east-1, and your plan applies to any M7 size) and OS flexibility, but you can’t use it for a different family or region. This suits stable, single-family workloads that need maximum savings without the rigidity of Standard RIs.
If your architecture is in flux—migrating from x86 to Graviton, expanding globally, or adopting Fargate—Compute Savings Plans give you insurance against commitment waste. If you have a stable, single-family regional footprint (say, all R7 instances in us-east-1), EC2 Instance Savings Plans deliver the highest discount. Many teams use a hybrid: EC2 Instance Savings Plans for core, predictable workloads (databases, steady APIs) and Compute Savings Plans for variable, multi-region services.
RIs apply first, then Savings Plans cover remaining usage. This lets you layer commitments for maximum savings. For example, buy a small pool of Convertible RIs for your most stable workloads, then add a Compute Savings Plan to cover variable usage across regions and instance types. AWS automatically applies the highest discount to each hour of usage.
Operational considerations: Managing commitments and interruptions
Choosing the right pricing model is only half the battle. Operationalizing it—tracking utilization, managing interruptions, and avoiding waste—requires process and tooling.
AWS Cost Explorer provides built-in RI and Savings Plan utilization reports. Target 80% or higher utilization for commitments; anything below 70% suggests over-commitment. Check coverage reports to see what percentage of your eligible usage is covered by commitments versus running at On-Demand rates. The goal: maximize utilization of commitments (so you’re not paying for unused reservations) while covering as much usage as possible (so you’re not overpaying On-Demand rates).
Use cost allocation tags to attribute RI and Savings Plan spend to specific teams or projects. Without tagging, your finance team sees a single $50,000/month commitment charge with no visibility into which business units or applications are consuming it. Poor tagging is responsible for 15-25% of commitment waste because teams can’t accurately forecast or right-size their share of the commitment pool.
Instrument your application to listen for the EC2 Spot Instance interruption notice via instance metadata or EventBridge. On receiving the two-minute warning, trigger graceful shutdown: stop accepting new work, finish in-flight requests, checkpoint state to durable storage (S3, DynamoDB), and terminate. For batch jobs, implement idempotent retry logic—use SQS or Step Functions to re-queue interrupted tasks automatically.
Use EC2 Auto Scaling with mixed instance types and capacity-optimized allocation to minimize interruption impact. Auto Scaling will automatically launch replacement instances when Spot capacity is reclaimed, and capacity-optimized allocation steers you toward the least-interrupted Spot pools. Monitor Spot interruption rates per instance type using CloudWatch or third-party tools; if a specific type shows more than 15% weekly interruptions, diversify to more stable alternatives.
Right-size before buying Reserved Instances or Savings Plans. Purchasing a 3-year RI for an oversized m7i.4xlarge when an m7i.2xlarge would suffice means you’ve locked in waste for three years. Use AWS Compute Optimizer or Cost Explorer recommendations to identify over-provisioned instances, then right-size them and buy commitments based on actual need.
Start conservatively: cover 60-70% of baseline usage with commitments, leaving 30-40% on On-Demand. This buffer absorbs forecast errors and unexpected growth without forcing you to over-commit. As usage stabilizes and you gain confidence in your forecasts, incrementally increase commitment coverage to the 70-80% sweet spot.
Your architecture, traffic, and business priorities change. Schedule quarterly reviews to assess RI and Savings Plan utilization, coverage, and alignment with current workloads. If utilization is low, you may have over-committed or migrated workloads without adjusting commitments. If coverage is low, you’re missing savings opportunities. Use these reviews to buy additional commitments, trade Convertible RIs for different configurations, or let expiring RIs lapse if usage has declined.
Combining pricing models for maximum savings
Real-world cost optimization isn’t about choosing one pricing model—it’s about layering multiple models to match the diversity of your workloads.
Picture your EC2 fleet as three tiers. The baseline tier runs 24/7 and never scales down—databases, core APIs, always-on services. Cover this tier with Reserved Instances or EC2 Instance Savings Plans to lock in 60-72% savings. The variable tier scales up and down daily or weekly based on traffic—web servers, background workers, batch jobs. Use On-Demand for the predictable portion and Spot for the bursty, interruptible portion. The experimental tier includes dev, test, and staging environments that don’t require high availability. Run these almost entirely on Spot to capture 60-90% savings.
A SaaS company running 50 EC2 instances might structure it this way: 20 are production databases and core services that never stop—these get 3-year EC2 Instance Savings Plans for 72% savings. 15 are web servers that scale from 15 to 40 during business hours—buy a Compute Savings Plan covering the baseline 15, use On-Demand for the next 10, and Spot for the peak 15. The remaining 15 instances are dev and test environments—run 100% on Spot. Result: the company saves 65% on total compute compared to pure On-Demand, while maintaining production SLAs.
Savings Plans apply to On-Demand usage first, then leftover commitment can apply to Spot usage at the Spot rate. This means if you have a $100/hour Compute Savings Plan and you’re using $80/hour of On-Demand plus $30/hour of Spot, the plan covers the $80 On-Demand fully and $20 of Spot at Spot rates. You don’t “waste” Savings Plan capacity on Spot—it simply prioritizes On-Demand coverage because that’s where the discount is largest.
Don’t put all your Spot eggs in one basket. If you run 100% c7i.2xlarge Spot instances in us-east-1a, an interruption wave could take down your entire fleet. Spread across c7i, c6i, and m7i instance types and across multiple availability zones. Use EC2 Fleet or Auto Scaling with instance type weighting to automatically favor cheaper or more available types while maintaining overall capacity.
Pricing model selection compounds with other cost levers. Right-sizing can reduce compute costs by approximately 35%. Graviton instances cost approximately 18-20% less per hour than comparable Intel Xeon instances, and organizations typically achieve 20-40% cost reductions when migrating compatible workloads. Switching from gp2 to gp3 EBS volumes cuts storage costs by 20%. Stack all of these: migrate to Graviton (20% savings), right-size (35% savings), add a 3-year Savings Plan (66% additional savings on the right-sized Graviton price), and integrate Spot for 60-90% of bursty workloads. The cumulative effect can push total compute savings above 70%.
Tools and services to optimize EC2 pricing decisions
Manual management of Reserved Instances, Savings Plans, and Spot fleets quickly becomes overwhelming as your AWS footprint grows. Fortunately, AWS provides native tools and third-party platforms can automate much of the heavy lifting.
AWS Cost Explorer visualizes spending trends, RI and Savings Plan utilization, and coverage gaps. Use the built-in recommendations to identify opportunities to purchase additional commitments or right-size existing ones. For deeper analysis, export the Cost & Usage Report (CUR) to S3 and query it with Athena or load it into your BI tool. CUR provides line-item detail on every resource, discount, and charge—essential for tracking which teams or projects are driving costs.
AWS Compute Optimizer is a free service that analyzes CloudWatch metrics and recommends optimal instance types and sizes. It identifies over-provisioned instances and suggests downsizing or switching to Graviton. Compute Optimizer can flag 20-30% of your instances as optimization candidates. Review these recommendations quarterly and implement the ones that align with your performance requirements.
Before committing to Reserved Instances or Savings Plans, model scenarios in the AWS Pricing Calculator. Input your expected usage (hours per month, instance types, regions) and compare On-Demand vs 1-year RI vs 3-year RI vs Savings Plan costs. This forward-looking analysis helps you choose the optimal commitment level and term.
AWS publishes historical Spot interruption rates per instance type. Use this data to select the most stable Spot pools for your workload. Instance types with less than 5% interruption frequency are prime candidates for production-adjacent workloads; those above 15% suit only fully fault-tolerant batch jobs.
Hykell goes beyond AWS native capabilities by automating commitment management and continuous right-sizing. Hykell’s AI-powered rate optimization forecasts usage, algorithmically blends Convertible RIs, Savings Plans, and flexible instruments, and actively buys, sells, or converts commitments as workloads shift—delivering effective savings rates of 50-70% or more on compute with zero engineering lift. This eliminates the manual toil of tracking utilization, adjusting commitments, and identifying optimization opportunities every quarter.
Configure EC2 Auto Scaling groups with mixed instance types and purchase options (On-Demand plus Spot). Use capacity-optimized allocation for Spot and define instance type weights so the Auto Scaling group can substitute equivalent types when interruptions occur. For containerized workloads, ECS and EKS capacity providers automatically manage the underlying EC2 fleet, scaling Spot and On-Demand nodes based on task demand.
Implement a consistent tagging strategy (Environment, Team, Project, CostCenter) across all EC2 instances, EBS volumes, and other resources. AWS Cost Allocation Tags let you break down spending by these dimensions in Cost Explorer and CUR. Without proper tagging, you can’t accurately attribute RI and Savings Plan spend to the teams consuming the resources—leading to 15-25% commitment waste as teams over- or under-forecast their needs.
What to do next: A practical decision framework
You’ve seen the trade-offs; now here’s how to act. Follow this framework to structure your EC2 pricing strategy in the next 30 days.
Pull 60-90 days of Cost Explorer and Cost & Usage Report data. Identify your top 20 instance types by spend—they’ll represent 70-80% of your compute cost. For each major workload, note: average monthly runtime (hours), variability (does it scale 2x during peaks or stay flat?), criticality (production vs dev and test), and interruption tolerance (can it retry or checkpoint?). Use AWS Compute Optimizer to flag over-provisioned instances. Right-size these before buying any commitments. More than 40% of EC2 instances run under 10% CPU utilization at peak, and right-sizing can cut costs by approximately 35%. Don’t lock waste into a 3-year commitment.
Create three buckets: Baseline and steady workloads run 24/7 with predictable usage (databases, core APIs)—target Reserved Instances or EC2 Instance Savings Plans for 70-80% of this tier. Variable and elastic workloads scale daily or weekly (web tiers, workers)—target a Compute Savings Plan for the baseline, On-Demand for predictable peaks, Spot for bursty overflow. Fault-tolerant workloads and dev environments (batch jobs, CI/CD, staging)—target Spot Instances for 60-90% of capacity. Map each workload to a bucket and estimate the instance-hours per month. This segmentation becomes your purchasing roadmap.
For the baseline tier, buy 1-year commitments covering 60-70% of your identified usage. Choose Convertible RIs or Compute Savings Plans if you expect architectural changes (Graviton migration, new regions, serverless adoption). Choose Standard RIs or EC2 Instance Savings Plans only if the workload is locked in stone for 1-3 years. For the variable tier, deploy Auto Scaling groups with 70% On-Demand baseline plus 30% Spot for peaks. Use diversified instance types and capacity-optimized allocation. Let this run for 30 days, monitor interruption rates and scaling behavior, then adjust the Spot percentage up if interruptions are low. For the fault-tolerant tier, move aggressively to Spot. Start with 80% Spot and 20% On-Demand to maintain a small stable baseline, then push to 90-100% Spot as confidence in your retry and checkpoint logic grows.
Track these KPIs monthly: RI and Savings Plan utilization (aim for 80% or higher; below 70% means you over-committed), RI and Savings Plan coverage (target 70-80% of eligible usage covered; below 60% means you’re missing savings), Spot interruption rate (under 10% is excellent; above 20% requires diversification or workload redesign), and Effective Savings Rate (your blended discount across all pricing models—teams using automated platforms like Hykell often achieve ESR of 50-70% or more). Review these metrics quarterly. As workloads evolve, buy additional commitments, let expiring RIs lapse, or trade Convertible RIs for new configurations.
Manual commitment management doesn’t scale beyond a few dozen instances. Consider automation platforms that continuously right-size, forecast usage, and manage RI and Savings Plan portfolios. Hykell’s automated optimization can reduce your cloud costs by up to 40% by eliminating the toil of tracking utilization and adjusting commitments—freeing your team to focus on building instead of cost administration.
Turning cost visibility into cost savings
You now understand the mechanics of AWS EC2 On-Demand, Reserved Instances, and Spot Instances—and how Savings Plans layer flexibility on top of traditional commitments. The path to 50-70% compute savings is clear: right-size first, cover steady workloads with Reserved Instances or Savings Plans, integrate Spot for fault-tolerant tiers, and continuously monitor utilization to avoid waste.
But knowing what to do and actually doing it every quarter are different challenges. Most teams leave 20-40% of potential savings on the table because they lack the time, tooling, or expertise to continuously optimize commitments and pricing models as workloads evolve.
Hykell eliminates that gap. We audit your AWS usage, uncover hidden savings opportunities, and automate the ongoing work of right-sizing, commitment management, and discount optimization. You’ll save up to 40% on AWS—and we only take a slice of what you save. If you don’t save, you don’t pay. Ready to stop leaving money on the table? Explore how Hykell can optimize your AWS costs on autopilot.