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Arlo Gains Cloud Cost Clarity and EKS Optimization with nOps

Industry

Smart Home Security

Location

United States

Challenge

Limited cost allocation and reporting, lack of granular EKS visibility, high container/node excess capacity, and significant manual overhead in cloud cost management

Featured Service

Showbacks, Business Unit Economics, EKS Visibility & Optimization, Automated Cost Recommendations, Anomaly Detection

Industry

Smart Home Security

Location

United States

Challenge

Limited cost allocation and reporting, lack of granular EKS visibility, high container/node excess capacity, and significant manual overhead in cloud cost management

Featured Service

Showbacks, Business Unit Economics, EKS Visibility & Optimization, Automated Cost Recommendations, Anomaly Detection

Overview

Arlo, a leader in connected smart home security, needed a more effective way to understand and control its growing cloud costs. Leadership sought granular visibility into Kubernetes (EKS) usage, the ability to allocate spend across teams and product lines, and actionable insights to reduce waste.

By adopting the nOps platform, Arlo gained real-time cost reporting, EKS efficiency metrics, and business unit economics—empowering both finance and engineering teams to collaborate on cost governance.

Challenges

  • Lack of accessible reporting: Arlo’s leadership & FinOps team struggled with immediate access to reports, dashboards, and granular cost allocation. A lot of manual work was involved, spread across multiple spreadsheets and data sources.
  • Complex allocations: Finance and engineering teams needed better ways to track costs by team, department, and tie the associated costs back to our unit economics metric, camera usage.
  • EKS inefficiency: Over-provisioning at the container and node level was contributing to increased AWS costs. The team sought optimization recommendations to reduce costs within the budget while simultaneously maintaining service levels.
  • Manual overhead: Without automation, analyzing usage and detecting anomalies required significant staff time each month.

Solution with nOps

nOps worked with Arlo’s engineering and finance teams to deploy platform capabilities that addressed their cost visibility and optimization challenges:

  • Showbacks & Business Unit Economics
    • Allocated cloud costs across applications and teams, introducing unit metric to track cost efficiency
    • Delivered straightforward reporting that both engineers and finance could use to make informed decisions.
  • EKS Visibility & Optimization
    • Provided full visibility from the cluster down to the pod level.
    • Uncovered significant excess capacity in both nodes and containers, well above industry benchmarks, highlighting major opportunities for cost reduction.
  • Anomaly Detection & Insights
    • Enabled proactive detection of anomalous cost patterns.
    • Delivered automated cost recommendations, prioritized by potential savings and effort required.
  • Operational Efficiency
    • Freed over 80 hours per month previously spent on manual reporting and reconciliation.
    • Enabled Arlo to redeploy team time toward innovation and product development.

Business Impact

With nOps, Arlo’s finance and engineering teams gained a shared view of cloud spend, real-time dashboards, and clarity on cost drivers. Cloud waste was dramatically reduced, unlocking hundreds of thousands in monthly savings. Leadership now makes confident, data-driven decisions, while engineering accelerates innovation without being slowed by manual cost management. tracking.

Customer Testimonials

“ As a FinOps lead at Arlo, I view cost transparency as non-negotiable, and nOps has delivered exactly that. The platform gave us end-to-end visibility into our cloud spend, eliminating guesswork and surfacing the true cost drivers across our environment. What stands out is how quickly the insights translate into action. nOps doesn’t just present data, it highlights the specific optimizations, anomalies, and inefficiencies we can address immediately. Those recommendations have driven real cost reductions for us while strengthening accountability across engineering teams. ”
— Alex Kuan, FinOps Lead, Arlo

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