How to get AWS discounts without compromising performance or reliability

Your AWS bill probably includes a 30–40% waste tax that you’re paying every month without realizing it. The culprit isn’t usage growth or feature expansion—it’s the gap between what AWS discount programs promise and what most teams actually capture.

AWS offers multiple pathways to slash costs, from Reserved Instances and Savings Plans that can cut compute expenses by 72% to storage optimizations that trim bills by 20–40%. Yet most engineering teams either don’t know which levers to pull or lack the bandwidth to pull them consistently. The result: hundreds of thousands of dollars annually left on the table while everyone stays too busy fighting fires to fix the underlying problem.

This guide walks through every AWS discount mechanism available in 2025—covering compute pricing models, storage tiering, container optimization, and even certification exam voucher strategies—so you can make informed decisions that actually stick without burning engineering cycles on constant manual tuning.

AWS compute discounts: Reserved Instances vs. Savings Plans

Your largest discount opportunity sits in the EC2 compute layer, where AWS offers commitment-based discounts ranging from 40–70% off On-Demand rates. The trick is choosing the right commitment model without locking yourself into inflexible constraints that cost more than they save.

Standard Reserved Instances deliver up to 72% savings (occasionally reaching 75% for specific SKUs) when you commit to a particular instance type in a specific region for one or three years. They work brilliantly for steady-state workloads that won’t migrate families or jump regions—think persistent databases, always-on APIs, or long-running analytics clusters. The catch: you need exact instance type and region matching to capture maximum discounts, so any architectural shift (moving from m5 to m7i, changing regions for compliance) strands your commitment.

Convertible Reserved Instances trade roughly 10–15% less discount (typically 54–66% off) for the flexibility to exchange instance families, sizes, or regions during your term. Consider them insurance against architectural evolution. If you’re migrating from x86 to Graviton instances or uncertain whether your m6i workloads might shift to c6i, Convertibles let you adjust without forfeiting your commitment.

Compute Savings Plans offer up to 66% savings with maximum flexibility across regions, instance families, sizes, operating systems, and even Fargate and Lambda. You commit to a dollar-per-hour spend rather than specific capacity, and AWS automatically applies your discount to the most expensive eligible usage. For 2025, industry consensus favors Savings Plans over RIs because they adapt to workload changes without manual exchanges or market resales.

EC2 Instance Savings Plans sit between Compute Plans and RIs—you commit to a specific instance family in one region but gain size flexibility within that family. They can reach up to 72% discounts and work well when you know your family (say, memory-optimized r6i) but need to scale between r6i.large and r6i.4xlarge as load fluctuates.

The optimal coverage sweet spot for steady-state workloads targets 70–80% through commitments, leaving 20–30% on On-Demand for variable peaks and unexpected growth. Covering more than 80% risks paying for unused commitments when load drops; covering less wastes discount potential on predictable baselines.

One critical 2025 policy shift affects partner-managed environments: effective June 1, 2025, AWS restricts RIs and Savings Plans to single customer usage, blocking MSPs and resellers who previously pooled commitments across multiple end customers. If you work through an AWS partner, confirm they’ve adapted their RI management to comply with decentralized ownership rules.

Right-sizing: the prerequisite for every discount strategy

Buying discounts on oversized instances locks in waste at a reduced rate—you’re still overpaying, just less egregiously. Organizations typically waste 35% of cloud spend on inefficient resource allocation, and AWS Compute Optimizer routinely uncovers 30–50% over-provisioning in environments running on autopilot with no regular optimization cycles.

Right-sizing your instances before locking in commitments unlocks 20–40% cost savings on compute resources alone. Development environments commonly run m5.xlarge instances at sub-10% CPU utilization when t3.medium would handle the same load at 40% lower cost. The delta compounds when you apply RIs or Savings Plans—correctly sized baselines mean every discount dollar works harder.

Start by pulling 90 days of EC2 usage from CloudWatch or Cost Explorer. Flag instances showing consistent CPU utilization below 40% and memory utilization below 50%—these are prime downsizing candidates. Test your hypothesis in staging first: resize one instance, run load tests mimicking production patterns, and verify latency and throughput stay within acceptable ranges. Our guide on AWS EC2 Auto Scaling best practices details the metrics and testing workflows for validating right-sizing without risking user-facing performance.

The compounding effect matters more than individual tactics in isolation. A GOV.UK case study achieved 55%+ total savings by layering optimizations: first migrating to Graviton instances (15–25% cost advantage for CPU-bound workloads), then right-sizing based on new performance profiles, and finally applying Savings Plans to the reduced baseline. Each step amplified the next.

Spot Instances: up to 90% off for the right workloads

AWS Spot Instances let you consume unused EC2 capacity at discounts reaching 90% compared to On-Demand pricing. The trade-off: AWS can reclaim your instances with just two minutes’ notice when capacity tightens or market prices shift.

Spot shines for batch processing where checkpointing lets you resume after interruptions, CI/CD pipelines where orchestrators handle node failures gracefully, big data analytics with frameworks like Spark designed for fault tolerance, and machine learning training runs that save model checkpoints every few minutes. One financial services firm allocated 25% of workloads to Spot, saving 85% on that quarter of their compute spend while maintaining overall reliability through thoughtful workload segmentation.

Spot fails for stateful databases without replication strategies that tolerate node loss, real-time API services where two-minute termination notices cause customer-facing outages, and mission-critical processes governed by strict SLAs that can’t absorb interruption windows. The most effective pattern combines Spot with On-Demand fallbacks through Auto Scaling Groups—when Spot capacity disappears or prices spike beyond your threshold, your ASG automatically launches On-Demand instances to maintain capacity. This hybrid approach typically saves 60–70% while keeping availability high enough for production workloads that can tolerate brief scale-down events during capacity crunches.

For implementation tactics like spot fleets, checkpointing strategies, and fault-tolerant architectures, our Spot vs. Reserved Instance comparison guide covers risk mitigation patterns engineering teams use in production.

Storage optimization: EBS and S3 discounts

EBS volume optimization represents one of the highest-return, lowest-risk moves most teams overlook. Many environments provision gp3 volumes with 3,000 IOPS when workloads rarely burst above 100 IOPS, paying for performance headroom they never use. Switching from gp2 to gp3 delivers identical performance at 30% lower cost, and right-sizing IOPS and throughput settings based on actual CloudWatch metrics can save another 20–40% on storage bills. Start by auditing gp3 volumes with provisioned IOPS above baseline (3,000 IOPS or 125 MB/s throughput)—if your VolumeReadOps and VolumeWriteOps metrics consistently stay below provisioned levels, you’re overpaying for unused capacity.

S3 storage tiering slashes costs for data accessed infrequently. The UK Ministry of Justice captured significant savings by implementing lifecycle policies that automatically transition objects between S3 Standard for hot data accessed frequently, S3 Infrequent Access for data touched less than monthly (roughly 40% cheaper than Standard), and S3 Glacier for archival data where retrieval times measured in hours are acceptable (80%+ cheaper than Standard). If your access patterns are unpredictable or you don’t want to manage lifecycle rules manually, S3 Intelligent-Tiering automatically moves objects between access tiers and delivers up to 20% savings with no operational overhead beyond enabling it per bucket.

Don’t ignore the hidden costs buried in S3 pricing: PUT, GET, and lifecycle transition requests plus data transfer charges. At scale, request pricing (around £0.005 per 1,000 requests) and egress fees (starting at $0.09/GB) materially impact total cost. A bucket serving 50 million GET requests monthly pays roughly $250 just in request fees before counting storage or transfer. Our AWS Pricing Calculator guide explains how to model requests and transfer accurately so your cost projections match reality.

Kubernetes and container optimization

The Amazon EKS vs. ECS decision carries direct cost implications beyond just feature sets and learning curves. ECS charges nothing for orchestration—you pay only for the EC2 or Fargate resources running your tasks. EKS bills $0.10 per hour per cluster (roughly $73 monthly) on top of your compute costs, justified by advanced Kubernetes features like pod orchestration, custom scheduling, and multi-cloud portability. For smaller workloads or teams prioritizing simplicity and tight AWS integration, ECS often wins on total cost; for complex microservices architectures or organizations needing Kubernetes expertise across clouds, EKS justifies its orchestration fee.

Regardless of which orchestration platform you choose, optimal capacity utilization for container clusters targets around 85%—higher risks resource contention and failed pod placements, lower wastes money on idle nodes. Many unoptimized EKS clusters run at 30% capacity, meaning you could halve node counts through better bin packing, horizontal pod autoscaling tuned to actual load patterns, and cluster autoscaler configurations that aggressively scale down during low-traffic windows.

Graviton instances offer 15–25% cost advantages for CPU-bound and disk-bound containerized workloads, with Domo and DoubleCloud reporting 20% price-performance improvements after migrating to Graviton-based node groups. Most modern container images compile cleanly for ARM64 architecture with minimal code changes—the biggest lift usually comes from updating CI/CD pipelines and testing thoroughly before shifting production traffic.

Database discounts: RDS and Aurora

Amazon Aurora supports reader auto-scaling that adds or removes read replicas based on CloudWatch metrics like CPU or connections, plus Serverless v2 for continuous capacity scaling without connection drops during resize events. For steady-state workloads with predictable load patterns, RDS Reserved Instances deliver up to 72% savings versus On-Demand, while Compute Savings Plans offer similar discounts with flexibility to shift between database families if your data model evolves.

Standard RDS vertical scaling—changing instance size—requires downtime and planned maintenance windows since resizing involves stopping and restarting the database instance. Aurora handles this more gracefully through rolling upgrades across read replicas, but vertical scaling events remain disruptive enough that you’ll want to batch them during scheduled maintenance rather than scaling reactively in response to load spikes. Horizontal scaling by adding read replicas introduces no downtime but requires your application to route read queries appropriately and tolerate eventual consistency for non-primary reads.

Scheduled scaling works exceptionally well for databases with time-of-day patterns. One retail company cut RDS costs by 65% by stopping development database instances outside business hours—reducing monthly runtime from 744 hours (24/7) to 168 hours (weekdays 8am–6pm) eliminates 77% of compute charges with zero impact on developer productivity.

Lambda and serverless discounts

AWS Lambda pricing follows a pure consumption model: you pay for invocation requests and GB-seconds (memory allocation multiplied by execution duration). Reserved Instances don’t exist for Lambda because there’s no persistent capacity to reserve, but Compute Savings Plans cover Lambda with up to 17% discounts when you commit to a baseline dollar-per-hour spend across your serverless workloads.

The Lambda Free Tier provides 1 million requests and 400,000 GB-seconds per month permanently—sufficient for many small APIs, scheduled automation tasks, and event-driven workflows. Beyond Free Tier limits, the first 6 billion GB-seconds cost $0.0000166667 per GB-second, scaling down in price tiers as usage increases.

A mobile app developer achieved 80% cost savings by replacing traditional server architecture with Lambda and serverless technologies, completely eliminating costs associated with idle servers sitting at 5% utilization waiting for infrequent traffic spikes. The trade-off: cold start latency for infrequently invoked functions and more complex observability compared to long-running processes.

Right-sizing Lambda memory matters despite the serverless abstraction. While 128 MB functions cost less per invocation, functions often complete faster with more memory allocated since CPU scales proportionally with memory. The optimal memory allocation balances cost per invocation against execution duration—many I/O-bound workloads hit their sweet spot around 512–1024 MB where faster execution offsets higher per-millisecond costs.

AWS Free Tier strategies and gotchas

AWS Free Tier offers three distinct usage models with different expiration rules. Always Free services like Lambda (1 million requests monthly), DynamoDB (25 GB storage), and CloudWatch (10 custom metrics) remain free indefinitely regardless of account age. 12 Months Free resources including 750 hours monthly of t2.micro or t3.micro EC2, 30 GB EBS storage, and 5 GB S3 Standard storage apply only during your first year after account creation. Trial offers like SageMaker (250 hours monthly for two months) and Lightsail (750 hours for one month) provide short-term free access to specific services for initial evaluation.

Common Free Tier traps catch teams unaware. Free Tier EC2 hours apply per account, not per instance—running two t3.micro instances simultaneously consumes 1,488 hours monthly, exceeding your 750-hour limit and triggering charges for the overage. Data transfer out to the internet costs money after the first 1 GB monthly even when you’re within Free Tier compute limits. Stopped EC2 instances don’t burn compute hours but attached EBS volumes continue accruing storage charges. Elastic IPs cost $0.005 per hour when not actively attached to a running instance, turning “free” test environments into surprise monthly bills.

Set up billing alerts through AWS Budgets before launching resources. A simple $1 budget with alert thresholds at 50%, 80%, and 100% sends email notifications when your Free Tier usage approaches limits, giving you time to shut down test environments before they generate material charges.

AWS Enterprise Discount Program (EDP)

Organizations spending $1 million or more annually on AWS can negotiate customized discount structures through the Enterprise Discount Program, also called Private Pricing Agreements. While exact discount percentages remain confidential and vary by negotiation, EDPs typically deliver headline discounts similar to Reserved Instances and Savings Plans (40–70% off On-Demand rates) but with simpler management and broader coverage spanning your entire AWS service portfolio.

EDPs use commitment-based contracts with one- to five-year terms and tiered discount structures that reward spend growth. Coverage extends across 200+ AWS services including compute, storage, databases, networking, and machine learning, with allowances for up to 25% of committed spend coming from AWS Marketplace purchases. The primary benefits beyond raw discount rates come from enhanced cost predictability through locked-in pricing for your commitment period, simplified financial management versus juggling dozens of individual RIs, and scalability as expanding usage can raise your effective discount tier automatically.

EDP negotiations require thorough preparation: audit current AWS usage to eliminate waste before committing (Gartner research suggests organizations can waste up to 70% of cloud spend without active optimization), document multi-year growth projections across major service categories, and understand your leverage points including multi-cloud optionality and competitive offers. Many enterprises stack EDPs with Savings Plans—the EDP establishes baseline discounts across all services while Savings Plans handle variable compute workload patterns that exceed or fall short of committed levels.

One notable policy shift: beginning January 2024, EDP customers are prohibited from selling discounted RIs on AWS Marketplace, eliminating secondary market liquidity as an exit strategy if your capacity needs change mid-term.

AWS certification exam discounts and voucher programs

AWS certification exam voucher discount programs exist primarily through AWS Training and Certification partners, though specific eligibility criteria and discount amounts vary by region and program availability. Bulk purchase programs allow organizations buying 10 or more exam vouchers to negotiate volume discounts, typically ranging from 10–20% off standard exam fees depending on volume and partnership tier.

AWS Partner Network (APN) members often receive training credits and exam voucher discounts as part of their partnership benefits. If your organization holds Select, Advanced, or Premier tier APN status, check your benefits portal for available exam vouchers and training credit allocations that can offset certification costs for your team.

Re-certification exam vouchers are sometimes offered at reduced rates when renewing certifications approaching expiration, though AWS has moved toward free digital badge renewals for many certifications to reduce friction in maintaining current credentials. Training bundle discounts combine instructor-led courses or digital training with exam vouchers at package pricing below the sum of individual components, often reducing total cost by 15–25% compared to purchasing separately.

For current voucher availability, pricing, and eligibility requirements specific to your region and organization, contact your AWS account team or visit the AWS Training and Certification portal to view active programs.

Monitoring and optimization tools

AWS Cost Explorer provides historical spend analysis and forecasting capabilities essential for tracking optimization progress. Use it to identify spending trends by service and resource, analyze Reserved Instance utilization (targeting 85% or higher), and track RI coverage against the recommended 70% threshold for steady workloads. Cost Explorer also surfaces anomalies like unexpected spending spikes that warrant investigation before they compound into material monthly overages.

AWS Budgets establishes spending thresholds and triggers alerts when actual or forecasted costs exceed your defined limits. Configure alerts for both operational metrics (like “RI Utilization drops below 70%” or “Uncovered EC2 Spend exceeds 30%”) and absolute spend thresholds by account, service, or tag dimension. Email and SNS notifications ensure relevant stakeholders learn about cost anomalies quickly enough to investigate root causes and implement corrections before month-end close.

AWS Trusted Advisor evaluates your environment against AWS best practices across five pillars including cost optimization, performance, security, fault tolerance, and service limits. Business and Enterprise support tiers unlock all 482 checks compared to just 56 core checks on Basic support. For a typical environment spending $50,000 monthly, Business Support costs approximately $1,000 monthly but commonly identifies $5,000–$15,000 in monthly savings opportunities through recommendations like terminating idle resources, right-sizing oversized instances, and converting steady workloads to Reserved Instances.

AWS Compute Optimizer uses machine learning trained on your actual CloudWatch utilization metrics to recommend optimal instance types, sizes, and purchasing options. It’s free for all AWS customers and provides quantified cost impact estimates showing exactly how much you’d save by implementing each recommendation. Unlike Cost Explorer which reports historical patterns, Compute Optimizer generates forward-looking optimization suggestions with confidence levels based on statistical analysis of your workload characteristics.

AWS Config tracks resource configuration changes and evaluates compliance against rules you define. While powerful for governance and audit use cases, Config costs can escalate quickly if not managed carefully—a typical setup tracking 10,000 configuration items with 50,000 monthly rule evaluations runs approximately $80 monthly. Optimize Config costs by restricting continuous recording mode to critical resources, consolidating rule evaluations across accounts to reach lower pricing tiers, and limiting recording to resource types you actually need for compliance rather than tracking everything AWS offers.

Third-party tools like CloudHealth, Cloudability, and Kubecost layer advanced analytics, automated recommendations, and cross-cloud cost management on top of native AWS tools. They excel at multi-cloud environments, sophisticated showback and chargeback models, and granular cost allocation beyond what AWS tagging supports natively. However, most third-party tools still require manual implementation of their recommendations—they’ll tell you which instances to right-size and calculate projected savings, but you still need engineering cycles to actually resize instances, adjust commitments, or modify configurations.

Real-world savings examples

A media streaming company reduced AWS costs by 30% by systematically implementing AWS Trusted Advisor recommendations—purchasing Reserved Instances to cover proven steady-state workloads and eliminating idle resources flagged by automated checks. The optimization cycle required minimal engineering effort since Trusted Advisor explicitly identified underutilized instances and provided quantified savings estimates for each action.

One e-commerce platform cut development environment costs by 40% after utilization analysis revealed m5.xlarge instances consistently running below 15% CPU. Switching to t3.medium instances matched actual load requirements while slashing compute spend with zero performance impact during normal development workflows. Peak load scenarios requiring more CPU relied on t3’s burstable credits rather than permanently provisioned capacity.

A financial services firm achieved 62% annual EC2 cost reduction, saving $155,000 from an original $250,000 baseline, through a thoughtfully segmented hybrid instance strategy. They allocated 60% of workloads to Reserved Instances for predictable baselines, 25% to Spot Instances for fault-tolerant batch processing (saving 85% on that segment), and kept 15% on On-Demand for spiky traffic and mission-critical processes requiring guaranteed capacity.

A UK government department realized 55%+ total savings by compounding multiple optimization tactics: migrating eligible workloads to Graviton instances (15–25% cost advantage for their CPU-bound workloads), right-sizing based on Graviton performance profiles, then layering Savings Plans on top of the reduced baseline. Each optimization step amplified the next rather than delivering isolated gains.

Why most teams still overspend

Even armed with comprehensive discount mechanisms and optimization tools, organizations typically leave 20–40% of potential savings unrealized. Engineering bandwidth tops the list of root causes—right-sizing requires continuous attention as workloads evolve, RI management demands quarterly reviews to prevent underutilization waste, and Savings Plan optimization means balancing commitment levels against growth uncertainty. Most DevOps teams prioritize feature delivery over cost optimization because users notice missing features immediately while cloud waste accumulates invisibly until finance escalates it.

Complexity creates analysis paralysis when teams face decisions among 10+ pricing models (On-Demand, three RI flavors, two Savings Plan types, Spot) across hundreds of instance families spanning multiple regions. Each workload theoretically deserves its own pricing strategy, but evaluating thousands of combinations for hundreds of resources exceeds practical analysis capacity for small teams juggling operational responsibilities.

Fear of performance impact drives over-provisioning that persists indefinitely. Teams size instances generously to ensure performance during initial deployments, then hesitate to touch working configurations even when months of utilization data show massive headroom. The risk of triggering a production incident by under-sizing resources feels larger than the certainty of wasted spend, so the waste continues.

Lack of visibility compounds organizational inertia. Without proper cost allocation tags and showback reports, teams can’t attribute spending to specific projects, business units, or customers. Cost optimization becomes shared accountability that devolves into no one’s actual responsibility—every team assumes someone else will handle it while focusing on their local priorities.

Rapid infrastructure change outpaces procurement cycles. By the time you’ve analyzed three-year RI commitment options, your architecture has migrated to containers or serverless, stranding reservations you can’t easily modify or resell. The mismatch between multi-year financial commitments and monthly architectural evolution creates friction that pushes teams toward flexible but more expensive On-Demand usage.

Automated optimization: the 40% solution

Manual cost optimization delivers diminishing returns after capturing obvious wins like terminating idle resources, right-sizing egregiously oversized instances, and implementing basic Reserved Instances for clearly steady workloads. Further optimization requires continuous monitoring and adjustment as workloads evolve, capacity needs shift, and AWS introduces new instance families with better price-performance ratios.

This is where automated AWS cost optimization changes the game. Rather than generating reports and recommendations that require engineering cycles to implement, automated platforms analyze your Cost and Usage Reports, identify optimization opportunities across compute, storage, and database layers, then implement changes automatically with built-in performance monitoring and rollback mechanisms if metrics degrade.

The distinction matters: traditional FinOps tools tell you what to optimize and calculate projected savings, but you still need to resize instances manually, adjust RI coverage, modify Savings Plan commitments, and tune storage configurations. Automated platforms implement optimizations, monitor performance impacts in real-time, and adjust continuously as your infrastructure evolves without requiring ongoing manual intervention.

Consider a concrete example: a financial services company with 200+ AWS accounts reduced AWS Config costs by 43% in the first month through automated recording mode optimization, rule consolidation across accounts to reach lower pricing tiers, and selective resource tracking that monitored only compliance-critical resource types. Executing these changes manually would have required weeks of analysis across account hierarchies, permissions management, and staged rollouts to avoid breaking compliance workflows.

Another customer increased Reserved Instance utilization from 62% to 93%, improved right-sizing metrics by 37%, and reduced total AWS bills by 29% within three months through automated RI portfolio management, right-sizing recommendations implemented during scheduled maintenance windows, and continuous optimization adjustments responding to workload pattern changes detected in CloudWatch metrics.

The typical outcome: up to 40% cost reduction without performance trade-offs, zero ongoing engineering effort after initial setup, and transparent performance-based pricing where you only pay a percentage of realized savings—if you don’t save money, you don’t pay.

Your discount optimization checklist

Here’s a 30-day action plan to systematically capture AWS discount opportunities without derailing ongoing projects or risking production stability.

Week 1: Audit and establish your baseline. Pull 90-day usage reports from AWS Cost Explorer showing compute, storage, and database spending by service and resource. Identify steady-state workloads running 24/7 with predictable load patterns—these are your primary targets for Reserved Instances or Savings Plans. Check current RI and Savings Plan utilization and coverage metrics to quantify how much of your existing commitments you’re actually using versus leaving on the table. Calculate your effective compute rates (total EC2 spend divided by total instance-hours consumed) to establish a baseline for measuring optimization impact.

Week 2: Capture quick wins that require minimal planning. Terminate truly idle resources including unattached EBS volumes, EC2 instances stopped for more than seven days, and unassociated Elastic IP addresses that incur hourly charges while unused. Right-size obviously oversized instances showing CPU utilization consistently below 20% and memory utilization below 30%—these represent pure waste with negligible risk of performance impact. Schedule non-production environments (development, testing, staging) to run only during business hours using AWS Instance Scheduler or Lambda-based automation, potentially cutting dev/test costs by up to 70%. Implement S3 lifecycle policies transitioning infrequently accessed objects to cheaper storage classes after 30 or 90 days based on your access patterns.

Week 3: Lock in strategic commitments based on proven patterns. Calculate baseline steady-state capacity across your primary instance families by analyzing the 10th percentile of usage over 90 days—this represents load you sustain even during low-traffic periods. Purchase Compute Savings Plans or Reserved Instances covering 70–80% of that baseline, leaving 20–30% on On-Demand to handle variable peaks and unexpected growth without overcommitting to capacity you might not use. Implement Spot Instances for batch jobs, CI/CD build fleets, and non-critical workloads that tolerate interruption, targeting 60–90% savings on that segment of your compute spend.

Week 4: Establish monitoring and iteration cycles. Configure Cost Explorer custom reports tracking RI utilization, Savings Plan coverage, and spend by cost allocation tags you’ve defined for projects, teams, or business units. Set up AWS Budgets alerts triggering notifications when utilization drops below 80%, uncovered spend exceeds 30% of total compute usage, or absolute spending breaches your monthly forecasts. Enable AWS Trusted Advisor checks if you’re on Business or Enterprise support to surface ongoing optimization opportunities automatically. Establish weekly cost review meetings with stakeholders who can authorize optimization actions and monthly retrospectives assessing whether implemented changes delivered projected savings without performance regressions.

For detailed implementation guidance including metric definitions, testing workflows, and rollback procedures, consult our comprehensive AWS cost management best practices guide.

Get to 40% savings on autopilot

The manual approach to AWS cost optimization scales until your infrastructure grows beyond a few dozen resources and your team’s attention splinters across operational fires, feature deadlines, and architectural migrations. At that point, continuous optimization demands automation—not quarterly audits, spreadsheet analysis, and manual change tickets that consume engineering cycles without guaranteeing results.

Hykell provides automated AWS cost optimization that combines right-sizing, Reserved Instance and Savings Plan portfolio management, EBS optimization, and container efficiency into a single platform operating continuously without ongoing manual intervention. We ingest your Cost and Usage Reports directly, identify hidden savings opportunities across compute, storage, and database layers, and implement optimizations automatically while monitoring CloudWatch performance metrics to detect and roll back any changes that degrade latency, throughput, or error rates.

The outcome: up to 40% cost reduction without compromising performance or reliability, zero ongoing engineering effort after initial onboarding, and transparent performance-based pricing where we only take a percentage of savings we actually deliver. If we don’t reduce your AWS bill, you don’t pay—the only risk is continuing to overspend month after month while optimization opportunities compound.

Ready to stop leaving money on the table? Use our AWS savings calculator at hykell.com to estimate your potential savings based on current spend patterns, or schedule a cost audit to receive a detailed breakdown of optimization opportunities in your specific environment with quantified impact estimates. We only succeed when you save money, so our incentives align perfectly with yours.