Automated cost visibility for AWS environments
Enterprises waste approximately 35% of their cloud budgets on underutilized resources and missed optimization opportunities. Yet most organizations still manage AWS costs through manual analysis, spreadsheets, and quarterly reviews—an approach that’s fundamentally broken in dynamic cloud environments where new resources spin up every hour.
Automated cost visibility transforms this reactive process into continuous, intelligent monitoring that not only shows you where money goes but actively optimizes spending without constant engineering intervention. For AWS-focused FinOps teams and engineering leaders, automation represents the difference between chasing cost problems and preventing them—while capturing savings that manual approaches consistently miss.
Why manual AWS cost visibility fails at scale
Manual cost management worked when cloud environments were simple and relatively static. Today’s AWS infrastructures operate at entirely different scales and velocities.
Your engineering teams deploy resources across multiple regions, accounts, and services. Development environments proliferate. Kubernetes clusters scale dynamically. Serverless functions execute millions of times per day. In this environment, spreadsheet-based cost tracking becomes obsolete before you finish compiling it.
The practical limitations hit hard. Engineering teams spend 10-15% of their time on cloud cost management tasks—not strategic optimization work, but data collection, tagging cleanup, and chasing down resource owners.
Even worse, manual visibility creates dangerous blind spots. An unused EBS volume might cost $50 monthly—trivial enough to escape notice. But when you have 200 such volumes across multiple accounts, that’s $10,000 in completely wasted spend. One Hykell customer discovered exactly this scenario, eliminating over $10,000 monthly on orphaned snapshots and idle resources once automation revealed the full scope.
The opportunity cost matters too. While your team manually analyzes Cost Explorer reports, they’re not building features, improving reliability, or driving business value. Meanwhile, savings opportunities expire. Reserved Instance prices change. Spot Instance availability fluctuates. By the time manual analysis identifies an optimization, market conditions have shifted.
The four pillars of automated AWS cost visibility
Effective automated visibility operates across four interconnected domains: monitoring, rightsizing, policy enforcement, and auditing. Each pillar addresses specific cost management challenges while feeding insights to the others.
Real-time monitoring and anomaly detection
Automated monitoring continuously tracks spending patterns across all AWS services, accounts, and resources. Unlike static reports, these systems detect anomalies as they occur—not days or weeks later during monthly reviews.
The mechanics work through continuous data ingestion from AWS Cost and Usage Reports, CloudWatch metrics, and service-specific APIs. Advanced platforms apply machine learning to establish baseline spending patterns, then flag deviations that might indicate waste, misconfiguration, or unexpected usage spikes.
Consider data transfer costs, which often surprise teams during monthly bill reviews. Automated monitoring catches unusual egress patterns within hours. Maybe a misconfigured application is repeatedly downloading large S3 objects. Perhaps a test environment is accidentally serving traffic to external users. Automated alerts enable immediate investigation and remediation rather than expensive post-mortem analysis.
Dashboards that surface these insights to different stakeholders—engineering, finance, and leadership—ensure everyone operates with current cost context. A retail company using this approach cut cloud expenses by 25% by spotting and addressing cost anomalies within the same billing period they occurred.
Continuous rightsizing and resource optimization
Manual rightsizing reviews happen quarterly at best. Automated rightsizing analyzes resource utilization continuously and implements optimizations during scheduled maintenance windows without manual intervention.
AWS Compute Optimizer provides recommendations, but automation takes the critical next step: implementation. When an m5.2xlarge instance consistently runs at 15% CPU utilization, automated systems can safely downgrade it to m5.large—delivering immediate cost reductions while maintaining performance.
The same principle applies to storage. Automated EBS volume optimization identifies unattached volumes, oversized allocations, and candidates for gp2-to-gp3 migration. Those gp3 volumes deliver the same performance at roughly 30% lower cost—savings that accumulate across hundreds or thousands of volumes.
For Kubernetes environments, automated rightsizing becomes even more critical. Pod resource requests and limits directly impact node sizing and count. When developers overestimate resource requirements (a common pattern), clusters overprovision capacity. Automated systems analyze actual pod utilization and adjust requests accordingly, potentially cutting cluster costs by 30-50%.
Automated policy enforcement and compliance
Cost governance fails without consistent policy enforcement. Automated visibility systems enforce standards around resource tagging, spending limits, and architectural patterns—preventing cost problems rather than discovering them later.
Tag enforcement exemplifies this pillar. Every AWS resource should carry tags identifying owner, cost center, environment, and project. Manual tagging inevitably creates gaps. Automated systems can enforce tagging at provisioning time, block untagged resource creation, or automatically apply tags based on account, VPC, or other attributes.
Budget controls work similarly. Rather than monitoring spend manually and reacting when budgets are exceeded, automated systems enforce spending limits through AWS Budgets integration and policy. Set alerts at 80% of budget thresholds to provide teams time to adjust usage patterns before overruns occur.
Compliance automation also covers architectural standards. Perhaps your organization mandates that production workloads use Reserved Instances or Savings Plans for steady-state capacity. Automated systems can verify compliance, flag violations, and even implement corrective actions like purchasing appropriate commitments to maintain target coverage levels.
Continuous auditing and waste elimination
Automated auditing continuously scans your AWS environment for waste: idle resources, orphaned assets, and inefficient configurations that drain budget without delivering value.
The common waste sources are well-documented. Over-provisioned resources, idle infrastructure, and suboptimal pricing models represent the bulk of unnecessary spend. Automated auditing identifies these issues systematically rather than relying on someone to notice them during manual reviews.
Orphaned resources accumulate naturally. A developer spins up test EC2 instances and forgets to terminate them. Someone creates EBS snapshots for a migration project that finished months ago. Elastic IPs sit unattached after infrastructure changes. Each instance might cost dollars rather than thousands, but they accumulate into material waste.
One e-commerce platform achieved a 75% AWS cost reduction primarily by eliminating idle instances and implementing auto-scaling for cost savings. That’s not an unusual result—it reflects how much waste exists in typical cloud environments until systematic auditing exposes it.
Automated auditing also catches configuration inefficiencies. Using gp2 volumes instead of gp3. Running databases on general-purpose instances instead of memory-optimized types. Storing logs in S3 Standard instead of implementing lifecycle policies for tiered storage. Each represents a savings opportunity that manual reviews often miss.
How automation transforms Cloud Financial Management
Automation doesn’t just accelerate existing FinOps practices—it fundamentally changes what’s possible in cloud financial management.
Traditional FinOps operates on monthly or quarterly cycles: collect data, analyze spending, identify optimizations, create tickets, implement changes, measure results. By the time you complete one cycle, your environment has evolved and new inefficiencies have emerged.
Automated FinOps collapses these cycles into continuous operations. Data collection happens in real-time. Analysis runs constantly. Optimizations implement automatically after validation. Results appear immediately in cost dashboards and forecasts.
This shift enables proactive rather than reactive cost management. Instead of discovering a $50,000 cost spike during monthly bill review, automated anomaly detection flags unusual spending patterns within hours. Instead of scheduling quarterly rightsizing reviews, continuous optimization maintains efficient resource allocation automatically.
The financial planning benefits compound. Automated forecasting based on actual usage trends and upcoming commitments provides accurate predictions with 80% confidence intervals—not guesswork extrapolated from last month’s bill. One mid-sized tech company improved forecast accuracy from ±20% to ±5% through automation, enabling confident budgeting and capacity planning.
Automation also democratizes FinOps across the organization. When cost visibility requires manual report generation and analysis, only dedicated FinOps practitioners can access insights. Automated cost dashboards make current cost context available to engineers as they make provisioning decisions, finance teams as they manage budgets, and leadership as they evaluate cloud ROI.
AWS-native tools for cost visibility
AWS provides substantial native tooling for cost management. Understanding these capabilities—and their limitations—helps you architect an effective automated visibility strategy.
AWS Cost Explorer and Cost and Usage Reports
Cost Explorer provides interactive analysis of historical spending with filtering, grouping, and basic forecasting. The 12-month forward-looking forecasts operate with 80% confidence intervals and help teams anticipate future spending based on trends.
Cost and Usage Reports (CUR) deliver granular, line-item billing data that feeds sophisticated analysis. Every resource, every hour, with complete tagging and pricing details. CUR data enables unit economics calculations, chargeback systems, and deep-dive optimization analysis.
The limitation: these are visibility tools, not optimization tools. They show you what happened and predict what might happen. They don’t implement changes or continuously optimize configurations.
AWS Cost Optimization Hub
The Cost Optimization Hub consolidates recommendations from across AWS services—rightsizing suggestions from Compute Optimizer, Savings Plan opportunities from Cost Explorer, and more. It provides a unified view of potential savings.
But the Hub remains a recommendation engine. It identifies opportunities; you must implement them. In dynamic environments with thousands of resources, manually implementing hundreds of recommendations becomes impractical.
AWS Trusted Advisor
Trusted Advisor scans your AWS environment for optimization opportunities across cost, performance, security, and fault tolerance. The cost checks identify idle resources, underutilized instances, and unused Reserved Instances.
Organizations with AWS Business or Enterprise Support receive expanded checks covering more cost optimization scenarios. Yet again, Trusted Advisor recommends rather than remediates. The actual work—terminating idle resources, rightsizing instances, adjusting reservations—requires human action.
AWS Budgets and Cost Anomaly Detection
AWS Budgets enables spending limit alerts at account, service, or tag levels. Set monthly thresholds and receive notifications when actual or forecasted spending exceeds them. Combined with Cost Anomaly Detection’s machine learning-based alerts for unusual spending patterns, these tools provide proactive notification of cost issues.
The challenge: notifications require responses. Someone must investigate anomalies, identify root causes, and implement fixes. In organizations with limited FinOps resources, alert fatigue sets in as notifications outpace remediation capacity.
Where Hykell augments AWS-native visibility
AWS-native tools provide essential visibility and recommendations. Hykell transforms that foundation into continuous, automated optimization that implements savings without ongoing engineering effort.
The fundamental difference is action. Where AWS tools identify opportunities, Hykell implements them automatically after validation. Where native services provide metrics, Hykell uses those metrics to drive intelligent optimization decisions.
Automated implementation at scale
Hykell continuously monitors your AWS environment using Cost and Usage Reports, CloudWatch metrics, and service APIs. When optimization opportunities arise—an underutilized instance, an orphaned volume, a commitment purchase opportunity—the platform evaluates the change, validates that performance won’t suffer, and implements the optimization during appropriate maintenance windows.
This automated implementation operates across all four visibility pillars. Rightsizing recommendations become actual instance modifications. Waste audits trigger automated cleanup. Commitment opportunities convert into Reserved Instance or Savings Plan purchases that maximize coverage while preserving flexibility.
The scale matters. A typical mid-market AWS environment might have 500 optimization opportunities at any given time. Manual implementation requires investigating each recommendation, creating tickets, coordinating changes, and validating results—weeks of work. Automation implements the same optimizations in hours while continuously discovering new opportunities.
Intelligent commitment management
Reserved Instances and Savings Plans can deliver up to 72% discounts for committed usage. But optimal commitment management requires continuous analysis of usage patterns, marketplace conditions, and pricing changes.
Hykell automates this complexity through algorithmic commitment optimization. The platform analyzes your usage patterns, identifies steady-state workloads suitable for commitments, and automatically purchases or modifies commitments to maintain optimal coverage levels (typically 70-85%) while preserving flexibility through on-demand capacity.
The system also optimizes the commitment marketplace, automatically selling unused commitments when usage patterns shift and timing those sales for maximum value. US-East-1 Reserved Instances sell better on weekday mornings—details that automated reserved instance management captures but manual processes rarely do.
Performance-protected optimization
The biggest objection to cost optimization is fear of performance impact. Will rightsizing that instance cause application slowdowns? Will aggressive auto-scaling create user-facing latency?
Hykell addresses this through performance-protected optimization. Before implementing any change, the system analyzes historical performance metrics, validates that resources have headroom for optimization, and predicts performance impact. Changes only implement when confidence thresholds are met.
Post-implementation monitoring ensures optimizations deliver expected results. If performance degrades, the system can automatically roll back changes. This safety mechanism enables aggressive optimization while maintaining the performance and availability standards your business requires.
Multi-service coordination
Effective optimization requires coordinating changes across multiple AWS services. Rightsizing EC2 instances might necessitate adjusting corresponding EBS volumes. Optimizing an RDS database could require modifying ElastiCache configurations to maintain application performance.
Hykell orchestrates these multi-service optimizations automatically. When optimizing a web application tier, the platform considers compute, storage, database, caching, and networking as an integrated system rather than independent components. This holistic approach prevents the “whack-a-mole” pattern where optimizing one service creates bottlenecks elsewhere.
For Kubernetes environments, this coordination becomes critical. Node rightsizing must align with pod resource requests, horizontal pod autoscaling configurations, and cluster autoscaling policies. Hykell analyzes the complete Kubernetes stack to identify optimization opportunities that maintain cluster performance while reducing costs by 30-50%.
Real-world impact: Operationalizing savings at scale
The theoretical benefits of automated visibility translate into substantial, measurable results for organizations that implement comprehensive automation strategies.
Organizations typically waste 35% of cloud spend through inefficiencies. Automated optimization commonly recovers 30-40% of total AWS costs—not through service reduction or performance compromise, but through systematic efficiency improvements.
Case study: SaaS platform optimization
A mid-sized SaaS company with a $120,000 monthly AWS bill implemented automated cost visibility and optimization. The results demonstrate how multiple optimization strategies compound:
Automated idle resource elimination delivered $15,000 monthly savings from terminating forgotten test environments and orphaned resources. Intelligent rightsizing contributed $22,000 monthly savings by adjusting oversized instances to match actual utilization. Storage optimization provided $8,000 monthly savings through EBS volume rightsizing and snapshot lifecycle management. Strategic commitment management added $10,000 monthly savings from optimized Reserved Instance and Savings Plan coverage.
Total monthly savings: $45,000 (38% reduction) with no performance impact. The company reinvested those savings to hire three additional developers, accelerating product development without increasing operating budgets.
Critically, these optimizations required minimal ongoing engineering effort. Initial implementation took two weeks of light collaboration with Hykell. Ongoing maintenance is essentially zero—automation continuously refines optimizations as the environment evolves.
E-commerce platform at scale
An e-commerce platform running on AWS achieved even more dramatic results through comprehensive automated optimization. The platform handles significant traffic spikes during sales events while maintaining strict performance requirements.
Key optimizations included automated auto-scaling that reduced standing capacity by 25-30 servers during off-peak hours, intelligent Spot Instance utilization for batch processing workloads, storage tiering that moved older customer data to S3 Glacier, and right-sizing of RDS instances based on actual query performance requirements.
The result: 75% reduction in AWS costs, saving over $250,000 annually. Even more impressive, the platform now handles 3x normal traffic during peak events without infrastructure changes—the optimized baseline proved more resilient than the original over-provisioned environment.
Financial services compliance and optimization
A financial services firm demonstrates that automated optimization works even in highly regulated industries with strict compliance and performance requirements.
The company needed to maintain specific compliance frameworks around data residency, backup retention, and availability. These requirements initially seemed incompatible with aggressive cost optimization.
Automated visibility revealed otherwise. The firm achieved 43% AWS cost savings through optimized Reserved Instance and Spot Instance deployment, all while maintaining compliance requirements. The key was intelligent automation that understood regulatory constraints and optimized within those boundaries rather than around them.
Building your automated visibility strategy
Implementing automated cost visibility doesn’t require ripping out existing processes and starting over. The most successful implementations follow a phased approach that builds capability progressively.
Phase 1: Establish baseline visibility
Start by implementing comprehensive tagging across all AWS resources. Tags enable cost attribution, support chargeback models, and facilitate targeted optimization. Enforce tagging through AWS Service Catalog, CloudFormation templates, or Terraform modules that prevent untagged resource creation.
Deploy AWS Budgets at account and service levels to establish spending baselines. Set conservative alert thresholds initially—you can tighten them as optimization reduces waste. Configure Cost Anomaly Detection to flag unusual spending patterns for investigation.
Build basic cost dashboards using Amazon QuickSight or Grafana. Focus on key metrics: costs by service, costs by tag/team, Reserved Instance coverage, and month-over-month trends. Share these dashboards with relevant stakeholders to build organizational cost awareness.
Phase 2: Implement quick wins
Target low-hanging optimization opportunities that deliver immediate results without complex implementation. Terminate clearly idle resources—unattached EBS volumes, stopped instances that have been offline for 90+ days, unused Elastic IPs. Implement scheduled start/stop for non-production environments using AWS Instance Scheduler or Lambda functions. Migrate gp2 volumes to gp3 for immediate 30% storage cost reduction. Review AWS Trusted Advisor and Cost Optimization Hub recommendations for obvious waste.
These quick wins demonstrate value quickly while building organizational confidence in optimization efforts. They also reduce baseline costs before implementing more sophisticated automation.
Phase 3: Deploy automated optimization
With visibility established and initial wins captured, deploy automated optimization platforms that continuously identify and implement savings opportunities.
Hykell’s approach typically starts with compute optimization—the largest cost center in most AWS environments. Compute services represent 53% of total AWS spend after discounts are applied, with EC2 accounting for nearly 90% of that compute usage. Automated rightsizing, intelligent Spot Instance utilization, and optimized commitment management deliver substantial savings in this domain.
Storage optimization follows similar patterns: automated volume rightsizing, snapshot lifecycle management, and intelligent S3 tiering based on access patterns. Database optimization considers RDS instance types, Aurora Serverless for variable workloads, and read replica configurations.
For organizations running containerized workloads, Kubernetes optimization provides additional leverage. Automated analysis of pod resource requests, node pool configurations, and cluster autoscaling policies can reduce Kubernetes costs by 30-50% while improving resource utilization.
Phase 4: Continuous refinement and governance
Automated optimization isn’t “set and forget”—it’s continuous improvement with decreasing manual effort. Establish regular cadences for reviewing optimization results, validating that business objectives are met, and adjusting strategies as your AWS environment evolves.
Monthly reviews should examine total AWS spend trends and attribution by team/project, savings achieved through automation broken down by optimization type, Reserved Instance and Savings Plan coverage and utilization rates, performance metrics to validate that optimization hasn’t degraded application behavior, and upcoming commitment expirations and renewal decisions.
Quarterly reviews should assess strategic alignment: Are your AWS architectures evolving in ways that create new optimization opportunities? Are new AWS services or pricing models available that could deliver additional savings? Do current optimization strategies still align with business priorities?
The key metric is cost per unit of business value—cost per customer, per transaction, per API call, or whatever metric reflects your specific business model. Absolute AWS spend may increase as you grow, but cost per unit should decrease through continuous optimization.
Measuring success beyond cost reduction
While the primary goal is AWS cost reduction, comprehensive measurement considers multiple dimensions of optimization impact.
Unit economics and efficiency
Track how AWS costs scale relative to business metrics. A growing business should see AWS costs increase in absolute terms but decrease as a percentage of revenue or per-customer basis. This indicates that optimization is making each dollar of AWS spend more productive.
Calculate specific unit costs relevant to your business: cost per active user or per transaction for SaaS platforms, infrastructure cost per order processed for e-commerce, cost per hour of content delivered for media streaming, cost per terabyte processed or stored for data platforms.
One case study showed that while overall costs decreased by 30%, cost-per-transaction dropped by 45%—demonstrating that optimization delivered even greater efficiency gains than raw cost reduction suggested.
Engineering productivity impact
Quantify the engineering time reclaimed through automation. If your team previously spent 10-15% of their time on manual cost management, that capacity now redirects to feature development, reliability improvements, or other high-value activities.
One e-commerce platform reclaimed 15 engineering hours per week through automated optimization, improving their time-to-market by 40%. The financial value of this productivity gain often exceeds the direct cost savings via cloud automation.
Budget predictability and financial planning
Automated visibility and optimization improve forecast accuracy. When you understand spending patterns, have committed capacity appropriately sized, and maintain governance controls, financial planning becomes more reliable.
Track forecast accuracy over time. Organizations using automated forecasting typically improve from ±20% to ±5% accuracy, enabling confident capacity planning and budget allocation.
Environmental impact
More efficient AWS usage reduces environmental impact. While not traditionally a financial metric, many organizations now track sustainability as a core objective. Automated optimization inherently improves efficiency—running fewer resources for the same workload reduces energy consumption and carbon footprint.
AWS provides carbon footprint tools that quantify this impact. Correlating optimization initiatives with carbon reduction demonstrates additional value beyond cost savings.
Common implementation challenges and solutions
Even with strong automation capabilities, organizations encounter predictable challenges during implementation. Understanding these obstacles helps you navigate them successfully.
Organizational resistance to change
Teams accustomed to manual control may resist automated optimization, fearing performance impacts or loss of operational visibility. Address this through phased rollback capabilities that demonstrate automation can be reversed if issues arise, transparent reporting that shows exactly what automation is doing and what results it’s achieving, performance validation that proves optimization maintains or improves performance metrics, and collaborative implementation that involves engineering teams in defining optimization rules and constraints.
One successful pattern is starting with non-production environments where stakes are lower and teams can build confidence before production deployment.
Complexity in multi-account environments
Organizations using AWS Organizations with dozens or hundreds of accounts face coordination challenges. Optimization across accounts requires consolidated visibility, consistent governance, and centralized management.
Solutions include centralized Cost and Usage Report aggregation across all accounts, AWS Organizations service control policies enforcing tagging and provisioning standards, consolidated Reserved Instance and Savings Plan management at the payer account level, and unified dashboards showing costs across organizational units.
Hykell’s platform handles multi-account complexity automatically, treating your entire AWS organization as a unified optimization target while respecting account boundaries and organizational structures.
Kubernetes observability gaps
Kubernetes environments create unique visibility challenges. Container orchestration abstracts infrastructure from applications, making it difficult to attribute costs to specific teams, applications, or projects.
Comprehensive Kubernetes cost visibility requires pod-level monitoring, namespace-based attribution, and correlation of container resource requests with actual node costs. Specialized tools that understand Kubernetes semantics provide this visibility layer on top of AWS cost data.
Commitment management complexity
Reserved Instances and Savings Plans involve financial commitments that create risk if workloads change. Many organizations under-commit, missing substantial discounts, because they fear being locked into inappropriate reservations.
Automated commitment management addresses this through continuous optimization that adjusts commitments as usage patterns evolve. Rather than making large upfront commitments based on current usage, automation enables incremental purchases that build optimal coverage while preserving flexibility.
The marketplace component matters too. When commitments become inappropriate, automation can sell them at optimal times and prices rather than letting them sit unused. This dramatically reduces the risk of commitment strategies.
The path to 40% savings on autopilot
Achieving substantial, sustainable AWS cost reduction—the 30-40% savings demonstrated across case studies—requires combining multiple cloud optimization techniques through automated execution.
No single technique delivers 40% savings. Rightsizing might capture 10-15%. Commitment optimization adds 15-20%. Storage tiering contributes another 5-10%. Waste elimination provides 5-10%. The percentages overlap and compound, but the critical factor is comprehensive implementation across all optimization domains.
Manual implementation can’t maintain this comprehensiveness. You might complete a rightsizing project, but six months later your environment has evolved and new inefficiencies have emerged. Commitments purchased last year may no longer align with current usage. Storage that was optimized initially accumulates cruft as new projects launch.
Automation maintains optimization continuously. As your environment evolves, automated systems adapt. New resources are evaluated for rightsizing opportunities immediately. Commitment coverage adjusts automatically as usage patterns shift. Storage lifecycle policies apply to new data without manual intervention.
This continuous nature enables the “autopilot” model. After initial implementation, optimization continues without ongoing engineering effort. Your team focuses on building features and growing the business while automated systems maintain cost efficiency.
Hykell’s performance-based pricing model reflects this confidence. You don’t pay upfront implementation fees or ongoing license costs. Instead, Hykell takes a percentage of actual savings achieved. If optimization doesn’t deliver results, you don’t pay. This alignment ensures that incentives match outcomes—Hykell succeeds when you save money, not simply by selling software.
The investment case becomes straightforward. If your current AWS spend is $100,000 monthly and automation delivers 35% savings, you’re saving $35,000 monthly. The cost of achieving those savings through Hykell’s percentage model is substantially less than the total savings—providing positive ROI from month one while freeing engineering capacity for higher-value work.
Transform visibility into continuous optimization
Automated cost visibility for AWS environments represents more than incremental improvement over manual processes. It’s a fundamental transformation in how organizations manage cloud financial operations.
The progression from reactive cost management to proactive optimization—from quarterly reviews to continuous improvement—enables the 30-40% savings that mature FinOps practices commonly achieve. More importantly, it frees your organization from the constant burden of cost control, redirecting engineering talent toward innovation and growth.
AWS-native tools provide essential visibility and recommendations. Automation platforms like Hykell transform that foundation into implementation, turning insights into action and recommendations into realized savings.
Your specific path depends on current AWS maturity, organizational structure, and existing FinOps capabilities. But the destination is consistent: comprehensive automated visibility that maintains cost efficiency continuously while your team focuses on delivering business value rather than managing infrastructure costs.
Discover how much you could save through automated AWS cost visibility. Hykell’s free cost audit analyzes your current environment, identifies specific optimization opportunities, and provides estimated savings—with no commitment required. Most organizations uncover 30-40% savings potential in their first assessment. Get your free cost audit today and see what’s possible when cost optimization runs on autopilot.