According to AWS research, a typical enterprise environment has 35% of its resources underutilized. That translates to hundreds of thousands of dollars in preventable spending each year. The challenge isn’t just visibility—it’s acting on what you see without adding engineering overhead.
Understanding AWS cost optimization: The core framework
AWS structures cost optimization around five interconnected pillars: cost-effective resources, matching supply with demand, expenditure awareness, optimizing over time, and pricing model optimization. These aren’t just theoretical categories—they’re actionable levers that drive real savings.
The framework works because it addresses both immediate waste (like orphaned EBS volumes) and systemic inefficiencies (like over-provisioned instances running 24/7). Organizations that implement these practices systematically typically achieve 20-40% reductions in overall cloud spending, often discovering that their biggest savings come from areas they weren’t actively monitoring.
Essential AWS cost visibility tools
Before you can optimize, you need to see where money is going. Three native AWS tools form the foundation of cost visibility:
AWS Cost Explorer provides the historical view you need to identify trends and anomalies. It tracks costs across services, accounts, and custom tags with up to 18 dimensions of filtering. Data refreshes at least once every 24 hours, giving you near-real-time visibility into spending patterns. The tool also generates three-month forecasts based on historical usage, helping you anticipate future costs before they hit your bill.
AWS Budgets shifts you from reactive to proactive management. You can set custom alerts for spending thresholds tied to specific services, accounts, or tags, with notifications triggering before you exceed limits. The automation capabilities let you implement guardrails—like shutting down non-production instances when budget thresholds are reached—without manual intervention.
The AWS Pricing Calculator handles pre-deployment cost modeling across over 200 major services. Unlike Cost Explorer, which analyzes historical spend, the Pricing Calculator lets you simulate “what-if” scenarios before provisioning resources. Teams that validate estimates against actual usage after deployment often discover 10-20% variances, typically from underestimated data transfer or request costs.
Right-sizing EC2 instances: Your highest-impact optimization
Right-sizing EC2 instances can reduce costs by up to 40% while improving performance. The strategy is straightforward: match instance types and sizes to actual workload requirements rather than provisioning for theoretical peak capacity.
Start by collecting 2-4 weeks of CloudWatch data across four key metrics: CPU utilization, memory utilization, network throughput, and EBS bandwidth. Target 60-70% average utilization for production workloads—enough headroom for traffic spikes without paying for perpetually idle capacity. Data shows that 40% of EC2 instances sit below 10% CPU utilization at peak, representing clear over-provisioning.
Choosing the right EC2 instance type requires matching your workload characteristics to instance families. Compute-optimized instances (C7 series) excel at CPU-bound tasks like batch processing and video transcoding. Memory-optimized instances (R7 series) handle data-intensive applications like in-memory databases. Balanced instances (M7 series) serve most web applications and microservices efficiently.
Consider processor architecture as well. Graviton (ARM-based) instances typically deliver 10-20% lower costs with comparable or better performance than x86 alternatives. GOV.UK achieved approximately 15% per-instance savings by migrating from m6i to m7g Graviton instances, with total savings reaching 55% when combined with right-sizing and Savings Plans.
A practical example: an overprovisioned m7i.4xlarge instance costs approximately $700 per month. Right-sizing to m7i.2xlarge saves $350 monthly. Adding a 1-year Compute Savings Plan saves another $200 per month—totaling $550 monthly or $6,600 annually from a single instance optimization.
Optimizing EBS storage: Hidden savings in volume management
For many organizations, EC2 EBS cost represents a significant optimization opportunity. Three strategies deliver the most impact:
First, migrate from gp2 to gp3 volumes. Switching from gp2 to gp3 cuts storage costs by 20% while providing better performance. The gp3 volume type eliminates gp2’s unpredictable burst credit system, offering 3,000 baseline IOPS regardless of volume size and configurable IOPS up to 16,000 per volume. At approximately $0.08 per GB-month versus gp2’s $0.10 per GB-month in us-east-1, a 10TB migration saves roughly $200 monthly.
Second, right-size volume capacity and IOPS provisioning. A 1TB EBS volume using only 500GB represents a 50% cost inefficiency. AWS Compute Optimizer examines historical usage data and often identifies 20-30% of volumes as candidates for optimization. Monitor CloudWatch metrics like VolumeReadOps, VolumeWriteOps, and VolumeQueueLength to validate actual IOPS requirements before provisioning io1 or io2 volumes.
Third, eliminate orphaned and unattached volumes. These “zombie resources” continue generating charges even when detached from instances. A media company saved $4,000 monthly by implementing automated cleanup of 50TB of unused EBS volumes. Another financial services company discovered over $30,000 in monthly savings by systematically removing unattached volumes and migrating remaining workloads to gp3.
Strategic use of pricing models: Reserved Instances, Savings Plans, and Spot
AWS offers three primary pricing models beyond On-Demand: Reserved Instances, Savings Plans, and Spot Instances. Each targets different workload characteristics and risk tolerances.
Reserved Instances offer 30-75% savings with 1-3 year commitments for predictable workloads. Standard Reserved Instances provide up to 75% discounts but lock you into a specific instance family and region. Convertible Reserved Instances offer up to 54% savings with the flexibility to change instance families as your needs evolve.
Compute Savings Plans versus EC2 Instance Savings Plans present a flexibility-versus-discount tradeoff. Compute Savings Plans cover EC2 instances regardless of family, size, operating system, tenancy, or region—plus Fargate and Lambda—with savings up to 66% for 3-year commitments. EC2 Instance Savings Plans lock you to a specific instance family within a chosen region but deliver up to 72% savings. One manufacturing company reduced forecasted AWS spend by 43% using a 3-year Compute Savings Plan.
The strategic approach: cover 60-80% of your baseline capacity with Reserved Instances or Savings Plans, leaving the remainder on On-Demand for variability and bursts. A mid-sized financial services firm achieved 62% annual EC2 cost reduction by moving 60% of workloads to Reserved Instances (saving 40%), 25% to Spot (saving 85%), and leaving 15% On-Demand for critical processing.
Spot Instances offer up to 90% discount for fault-tolerant, non-critical workloads. The tradeoff: AWS can terminate Spot instances with two minutes’ notice when it needs capacity back. Spot works well for batch processing, CI/CD pipelines, development environments, and stateless applications. A SaaS provider reduced compute costs by 62% by implementing a blend of Reserved Instances for baseline usage and Spot Instances for batch processing workloads.
Kubernetes and EKS cost optimization
Amazon EKS charges $0.10 per hour for cluster management—approximately $73 per month per cluster. Worker nodes represent the largest cost component, with general-purpose m5.large instances costing $0.096 per hour and compute-optimized c5.large instances at $0.085 per hour.
Right-sizing nodes and workloads is the most effective cost reduction strategy for EKS environments. Use the Vertical Pod Autoscaler to analyze pod consumption and automatically adjust CPU and memory requests to optimal values. An e-commerce company reduced Kubernetes costs by 35% by implementing node auto-scaling for variable workloads, using Reserved Instances for baseline capacity and Spot instances in Auto Scaling Groups for traffic bursts.
Group similar workloads using Kubernetes node selectors and taints/tolerations to enable dedicated node groups with optimized instance types. Running compute-heavy workloads on C-series instances and memory-intensive applications on R-series instances eliminates the need to over-provision general-purpose instances across your entire cluster.
For EKS networking optimization, consider that data transfer between pods on different nodes in the same availability zone incurs charges. AWS recommends grouping related workloads within the same AZ when possible and using CloudWatch Container Metrics to identify resource usage patterns that indicate opportunities for consolidation.
Automated resource scheduling and lifecycle management
Implementing automated schedules to turn off non-production resources during off-hours can reduce related costs by up to 70%. Development and testing environments typically don’t need to run 24/7. By shutting down these instances during nights and weekends, you eliminate 128 hours of weekly charges—reducing monthly costs by approximately 65% for those workloads.
AWS Systems Manager and Lambda functions enable automated operational tasks like snapshot creation and volume cleanup. For example, configure Lambda to scan for unattached volumes older than 30 days, create final snapshots, and terminate volumes automatically. This prevents zombie resources from accumulating and ensures consistent enforcement of lifecycle policies.
Implement AWS Data Lifecycle Manager policies for automated EBS snapshot creation and retention. Production volumes should have snapshots taken within the last 24 hours, with cross-region copies and re-encryption using region-specific Customer Master Keys for disaster recovery. Test quarterly restores from random snapshots to validate integrity and recovery procedures.
Tagging strategies and cost allocation
Implementing a tiered tagging approach with business context, technical, and operational tags enables accurate cost allocation. Without consistent tagging, organizations typically waste 15-25% of Reserved Instance and Savings Plan investments due to poor resource attribution.
A comprehensive tagging scheme includes Environment (production, staging, development), Owner (team or individual responsible), Application (which system or service), CostCenter (for chargeback), and DataClassification (sensitivity level). These tags flow through to AWS Cost Explorer and enable filtering by any combination of dimensions—for example, showing all production database costs for a specific business unit.
AWS KPIs for cost optimization should track Reserved Instance utilization (target above 80%), EBS volume efficiency, spend-per-business-outcome, and cloud-driven revenue growth. One retail company used comprehensive tagging and KPI tracking to identify that Reserved Instance utilization had fallen to 62%; after optimization, it rose to 93% while overall right-sizing improved by 37%, reducing their AWS bill by 29% within three months.
Monitoring and continuous optimization
AWS Compute Optimizer provides free, automated recommendations based on historical CloudWatch metrics and often identifies 20-30% of resources as optimization candidates. The service analyzes actual usage patterns and suggests right-sizing opportunities, alternative instance types, and migration to newer generations that deliver better price-performance ratios.
Set CloudWatch alarms for key EBS performance metrics including VolumeThroughputPercentage greater than 80% and VolumeQueueLength greater than 1. These thresholds indicate when volumes are approaching capacity limits and may need provisioned IOPS adjustments or instance throughput upgrades.
For EC2 Auto Scaling, monitor GroupDesiredCapacity, GroupInServiceInstances, GroupPendingInstances, and scaling success metrics. Track ASGAverageCPUUtilization or ALB RequestCountPerTarget for target tracking policies, and configure alarms when desired capacity significantly exceeds or falls below in-service instances—indicating potential configuration issues.
Data transfer and networking cost optimization
Data transfer fees can be substantial, especially for cross-region and inter-AZ traffic. UK fintech startup discovered a 37% underestimation in cross-AZ traffic costs after deploying infrastructure that hadn’t properly modeled peak transfer patterns.
Use VPC endpoints for AWS service access to avoid data transfer charges that would otherwise apply when routing through internet gateways. When possible, keep related resources in the same availability zone to minimize cross-AZ transfer costs. For multi-region architectures, explicitly model replication traffic in the AWS Pricing Calculator—the tool won’t infer it automatically.
CloudFront can reduce data transfer costs for content delivery by caching at edge locations closer to users. Additionally, compress data before transfer and evaluate whether batch transfers during off-peak hours can leverage lower-cost networking options.
Enterprise Discount Programs for large-scale deployments
The AWS Enterprise Discount Program provides tailored, tiered discount structures for organizations with substantial AWS usage, typically requiring at least $1 million in annual spend. The program uses commitment-based models with 1-5 year terms and customized discount tiers that can extend across the entire AWS service portfolio.
Benefits include enhanced cost predictability through locked-in pricing, simplified financial management with unified discounts versus managing many individual Reserved Instances, and scalability rewards as usage grows. The tiered structure means expanding usage can raise effective discount rates over time.
Preparing for EDP negotiations requires thorough usage analysis to identify waste (Gartner notes that up to 70% of cloud spend can be wasted without optimization), documented growth plans, and understanding your leverage. Audit current spending patterns and optimize inefficiencies before committing to multi-year terms to avoid locking in costs from over-provisioned resources.
Graviton migration for stacked savings
Migrating to AWS Graviton instances can cut compute costs by up to 40% while improving performance and lowering carbon footprint. Graviton2 and Graviton3 processors offer up to 40% better price-performance over comparable x86 instances with broad support across EC2, RDS, Lambda, ECS, and EKS.
The economic case is compelling: Graviton delivers better unit economics, sustainability gains, performance improvements with faster throughput and lower latency, and increased AWS service optimization. These savings stack on top of existing Reserved Instances and Savings Plans, creating compounding cost reduction.
A structured migration approach includes workload compatibility assessment to identify Graviton-ready applications, performance benchmarking with side-by-side tests, phased migration planning, automated infrastructure changes with rollout and rollback protections, and live monitoring to track adoption progress and savings impact. Graviton is particularly well-suited for EC2-heavy environments running web servers, containers, and applications in Java, Go, or Python, as well as bursting and auto-scaling workloads where lower unit costs compound savings.
From visibility to automated action
AWS Cost Explorer and native tools provide essential visibility into cloud spending, but showing problems doesn’t fix them. A well-executed cloud cost audit can identify opportunities to reduce AWS spending by up to 40%, yet implementing those recommendations manually requires significant ongoing engineering effort.
Hykell’s automated cost optimization platform delivers continuous right-sizing, Graviton migration assistance, orphaned resource elimination, and automated EBS optimization without engineering lift. The platform uses AI-powered commitment planning to forecast usage from historical data, real-time patterns, and future events, then algorithmically blends Convertible Reserved Instances, Savings Plans, and flexible commitments to maximize effective savings rates—often 50-70% on compute.
Organizations achieve results like e-commerce platforms reducing costs by up to 75%, mobile app developers saving 80%, and enterprises cutting overall AWS bills by 40% while maintaining performance. With performance-based pricing that only charges a percentage of actual savings achieved, you don’t pay unless you save.
Ready to put your AWS cost optimization on autopilot? Discover how Hykell can reduce your cloud costs, eliminate manual optimization tasks, and gain real-time insights through automated intelligence. See how much you could save with zero engineering effort required.