How an AI-driven digital pathology company reduced its AWS cloud spend by 38.5% in just two months by eliminating idle resources, adopting spot instances, and implementing real-time FinOps governance without impacting performance.
Idle & Underutilized Resources
AI-Powered Digital Pathology & Healthcare Technology
Growth-stage deep-tech company with 400+ pathologists in network and 10+ global partners
Amazon Web Service (AWS)
Soaring, unpredictable AWS costs driven by high computational demands of AI model training, over-provisioned resources, and zero cost visibility
AWS Cloud Cost Optimization & FinOps Consulting
AI-driven healthcare companies operate at the intersection of innovation and operational complexity running large-scale machine learning workloads that demand significant cloud compute. For this pioneering digital pathology company, the cost of powering AI model training on AWS had grown unpredictable and unsustainable.
Techanek was engaged to conduct a comprehensive AWS cost audit, identify inefficiencies, and implement a structured FinOps strategy. Within two months, the client achieved a 38.5% reduction in monthly cloud costs with zero impact on platform performance, AI model quality, or innovation velocity. The engagement established a long-term cost governance framework that continues to deliver savings on autopilot.
The client faced four critical operational and security challenges that were inhibiting growth and increasing risk:
AWS costs fluctuated significantly month-over-month due to the variable compute demands of AI training cycles, making it nearly impossible to forecast budgets or plan cloud spend effectively.
Multiple EC2 instances, Elastic IPs, storage buckets, and EFS file systems were running idle or severely underutilized consuming budget without delivering value.
Infrastructure had been provisioned to handle worst-case peak usage. During non-peak periods which represented the majority of operational hours these resources sat unused, generating significant waste.
The organization lacked a unified view of resource utilization and cost distribution. Without tagging, monitoring, or anomaly detection, overspend went undetected until the monthly bill arrived.
Techanek designed a three-phase automated infrastructure deployment architecture built on native AWS services
A comprehensive audit using AWS Cost Explorer and AWS Trusted Advisor to identify idle EC2 instances, unused Elastic IPs, outdated snapshots, and unmanaged EFS file systems.
EC2 instance types matched precisely to actual AI training workloads. Auto Scaling Groups implemented for dynamic provisioning. EFS lifecycle management configured for automatic data tiering.
AWS Spot Instances introduced for fault-tolerant AI training jobs (up to 70% savings). AWS Savings Plans enrolled for predictable workloads to lock in discounted compute rates.
Infrequently accessed datasets transitioned to S3 Glacier. S3 Intelligent-Tiering enabled for automatic object movement between storage classes based on real usage patterns.
AWS Budgets and Cost Anomaly Detection configured for real-time alerting. Tag-based governance policies enforced across all resources. Live cost dashboards established for continuous visibility.
AWS Cost Explorer
AWS Trusted Advisor
AWS Budgets
AWS Cost Anomaly Detection
Tag Policies
Amazon S3 (Intelligent-Tiering)
S3 Glacier
Amazon EFS
GPU-enabled EC2 for deep learning training & inference
The automation transformation delivered measurable impact across security, efficiency, and scalability dimensions:
Monthly AWS bills were inconsistent and unpredictable, making budget forecasting nearly impossible for the finance and engineering teams.
A 38.5% reduction in monthly cloud costs achieved within two months through right-sizing, spot instances, and storage optimization with costs now fully predictable and governed.
Multiple EC2 instances, Elastic IPs, EFS file systems, and S3 buckets were idle or severely underutilized silently consuming budget with zero business value.
All idle and underutilized resources were identified and terminated, converting pure waste into recovered budget and improving overall infrastructure efficiency.
GPU-enabled EC2 instances for deep learning and AI model training ran on On-Demand pricing around the clock regardless of actual training schedules or utilization.
Migration to AWS Spot Instances for non-critical AI training jobs delivered up to 70% compute savings the single largest cost reduction in the entire engagement.
Resources were over-provisioned to handle worst-case peak loads, resulting in significant waste during the majority of non-peak operational hours.
Auto Scaling Groups dynamically provision and de-provision compute based on real demand eliminating over-provisioning waste while maintaining full performance during peak loads.
There was no centralized view of resource utilization or cost distribution. Overspend went undetected until the monthly AWS bill arrived too late to act.
Live dashboards, AWS Budgets, and Cost Anomaly Detection provide real-time visibility and proactive alerting replacing monthly billing surprises with continuous, actionable cost intelligence.
Comprehensive AWS environment audit using Cost Explorer and Trusted Advisor to identify all waste
EC2 right-sizing aligned to actual AI training and inference workload profiles
Auto Scaling Groups for dynamic compute provisioning across peak and non-peak periods
AWS Spot Instances for fault-tolerant AI model training delivering up to 70% compute savings
AWS Savings Plans enrollment for predictable workloads with locked-in discounted rates
S3 Intelligent-Tiering and Glacier archival for automated, cost-efficient dataset storage
Real-time cost anomaly detection and alerting via AWS Budgets and Cost Anomaly Detection
Tag-based resource governance for per-team cost attribution and ongoing spend accountability
For AI-driven companies, cloud infrastructure is not just an operational expense it is the engine powering innovation. But without proper governance and optimization, that engine becomes a financial liability.
By partnering with Techanek, this digital pathology company transformed its AWS environment from an unpredictable cost center into a lean, governed, and highly efficient infrastructure achieving a 38.5% reduction in cloud spend within just two months, without sacrificing a single point of performance or innovation capacity.
Whether you are running GPU-intensive AI workloads, managing multi-account environments, or simply facing an AWS bill that no longer makes sense the opportunity to optimize is always there. Techanek helps you find it, fix it, and keep it fixed
