Streamlining Cloud Operations and Cost Optimization
A top‑tier aerospace manufacturer relies on eight AWS GovCloud regions to power real‑time avionics analytics, high‑fidelity wind‑tunnel simulations, and petabyte‑scale computational‑fluid‑dynamics (CFD) workloads that underpin every new‑airframe design. The environment runs more than 2,100 containerized micro‑services and bursts thousands of GPU‑accelerated jobs during flight‑test campaigns, driving a cloud bill of roughly $5.6 million per month. While mission‑critical, this scale created chronic over‑provisioning, sluggish autoscaling, and limited cost visibility—challenges that threatened project timelines and program budgets.
Solution Implemented
- Karpenter Spot‑first elasticity: sub‑minute node provisioning on Graviton2 & GPU fleets, with cluster‑autoscaler as steady‑state backup.
- Real‑time FinOps layer: Kubecost 2.3 + CloudZero for 5‑minute cost attribution and >10 % anomaly alerts in Slack.
- GitOps modernization: Terragrunt‑refactored Terraform and Argo CD + Kustomize for region‑wide, declarative deployments.
- Performance tuning & observability: HPA/VPA optimization, Graviton C7g CFD pools, Prometheus/Grafana dashboards, and Lambda@Edge cold‑start monitoring (‑42 % latency).
Outcomes Expected
- Raise average node utilization from 40 % to ≥ 70 %, eliminating >$1 M/month in idle waste.
- Cut pod‑ready times from 17 min to ≤ 3 min, accelerating flight‑test and CFD iteration loops.
- Deliver near real‑time (≤ 5 min) cost visibility, enabling programme leads to correct overspend instantly.
- Achieve a 30 %+ overall cloud‑cost reduction while maintaining—or improving—HPC and telemetry performance SLAs.
Location
Industry
Services
Notable Tech
Challenge
Test‑flight telemetry and design‑iteration cycles generated unpredictable surges that forced the company to over‑provision Kubernetes clusters to avoid queuing critical jobs. Average node utilisation languished near 40 percent, while on‑demand instances purchased during peak simulation windows often added $900 k per event to the bill. Engineers waited up to 20 minutes for additional capacity during spikes, and financial reporting lagged two days behind, obscuring which programmes or aircraft lines were driving costs.
Solution
Our team began with a deep telemetry analysis, then deployed Karpenter to provide sub‑minute, Spot‑first node provisioning across AWS Graviton2 and GPU fleets, keeping 70 percent of burst capacity on discounted instances while retaining cluster‑autoscaler for steady‑state jobs. We introduced real‑time FinOps visibility with Kubecost 2.3 and CloudZero, delivering five‑minute cost attribution and anomaly alerts when project spend deviated by more than ten percent. Observability was unified under Prometheus/Grafana with Datadog APM for code‑level traces, ensuring that latency in telemetry‑ingest pipelines could be tied directly to infrastructure events.
On the infrastructure‑as‑code front, legacy Terraform stacks were refactored with Terragrunt for DRY, repeatable patterns, and all application deployments migrated to a GitOps workflow using Argo CD and Kustomize. Finally, performance tuning focused on high‑efficiency CPU and GPU node pools—moving CFD transcoders to Graviton C7g and leveraging Lambda@Edge cold‑start instrumentation that cut median start‑up latency by 42 percent during ground‑station data bursts.
Elastic Infrastructure
- Karpenter for rapid node provisioning (70 % Spot mix, AWS Graviton2 + GPU)
- Cluster‑autoscaler fallback for long‑running workloads
FinOps & Observability
- Kubecost 2.3 + CloudZero for 5‑min cost allocation & anomaly alerts (> 10 %)
- Prometheus / Grafana dashboards; Datadog APM traces
GitOps & IaC
- Terragrunt‑refactored Terraform modules
- ArgoCD + Kustomize for multi‑region app delivery
Performance Tuning
- HPA/VPA optimization; transcoders on Graviton C7g & GPU Spot nodes
- Lambda@Edge cold‑start monitoring (‑42 % median)
Implementation
The engagement unfolded in four sprints: a four‑week discovery phase, a six‑week pilot cluster supporting a single flight‑test programme, a ten‑week global rollout to the remaining seven regions, and a final optimisation phase that fine‑tuned FinOps dashboards and automated alert thresholds. Knowledge‑transfer workshops equipped the aerospace firm’s own DevSecOps group to maintain and extend the new platform.
Results & Impact
Within three months, average node utilisation climbed from 40 percent to 72 percent, trimming idle waste across all regions. Spot diversification and rapid scaling eliminated roughly $1.7 million in monthly costs, cutting the cloud bill by 31 percent. Peak‑simulation pod‑ready times dropped from seventeen to three minutes, allowing engineering teams to run more design iterations per day and shorten air‑frame validation cycles. Real‑time cost dashboards now surface overspend within minutes, enabling programme managers to course‑correct before budget thresholds are breached.
Key Takeaways
- Karpenter + Spot Instances boosted utilization to 71 % and eliminated $1.9 M/month in waste.
- Real‑time FinOps dashboards empower teams to catch spend anomalies within minutes.
- GitOps across nine regions removed configuration drift and enabled hour‑level rollouts.
- Performance and cost optimizations now scale seamlessly to meet global viewer demand.
Pairing Karpenter with a disciplined Spot‑first strategy delivers immediate elasticity and significant savings. Real‑time FinOps dashboards empower engineers to course‑correct spend anomalies before they escalate. GitOps standardization across nine regions eradicates configuration drift and enables hour‑level rollouts, while targeted performance optimizations ensure that both customer experience and cost efficiency scale hand‑in‑hand.
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