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Maximizing Cloud Efficiency: How EverOps and Datadog Cut Costs by 30% Without Sacrificing Performance
A global SaaS company operating across AWS, Azure, and GCP faced a 45% year-over-year surge in cloud costs. Despite their significant investment in cloud infrastructure, leadership struggled to pinpoint the root causes of escalating expenses. Finance teams lacked granular insights into cost drivers, while engineering teams lacked real-time visibility into inefficient resource usage. The result? A reactive, frustrating cycle of unexpected invoices and budget overruns.
The company knew they needed a real-time, detailed view of their cloud spend, but they were uncertain where to begin. They explored built-in cloud cost tools like AWS Cost Explorer, Azure Cost Management, and Apptio Cloudability, but these lacked real-time anomaly detection and deep observability into application-level inefficiencies.
The Solution: A Datadog-Powered FinOps Strategy
EverOps implemented a multi-layered Datadog-powered Cloud Cost Optimization Strategy by integrating Datadog’s Cloud Cost Management, Continuous Profiler, and Infrastructure Monitoring. Here’s how we did it:
Real-Time Cost Visibility with Cloud Cost Management
- We ingested and analyzed cloud billing data in Datadog’s Cloud Cost Management tool.
- Custom dashboards were built to provide service-by-service breakdowns across AWS, Azure, and GCP.
- Engineers could see which teams were driving cloud expenses, helping align cost accountability across departments.
Infrastructure Optimization with Continuous Profiler
- We deployed Datadog’s Continuous Profiler across EC2 instances, Kubernetes workloads, and serverless functions to analyze CPU and memory usage at the application level.
- This revealed underutilized resources, allowing us to downscale oversized instances and adjust auto-scaling policies dynamically.
- We identified several wasteful background processes and redundant compute cycles, reducing CPU overhead.
Automated Cost Alerts and Anomaly Detection
- We configured Datadog’s anomaly detection and cost-based alerting, triggering notifications when specific services exceeded expected budgets.
- Engineers could immediately investigate cost spikes, preventing unexpected billing surprises.
Workload Right-Sizing and Auto-Scaling Recommendations
- Using historical performance data from Datadog’s monitoring, we optimized auto-scaling rules for Kubernetes and VM-based workloads.
- Implemented horizontal pod autoscaling (HPA) policies that dynamically adjusted based on real-time application demand.
- Fine-tuned reserved instance and spot instance utilization to leverage more cost-effective cloud resources.
The Business Outcome: Real Results, Real Savings
- Cloud costs dropped by 30% within six months, saving the company $3.2M annually.
- Engineers had real-time insights into the financial impact of their infrastructure choices, reducing wasted spend without affecting performance.
- Finance and engineering teams worked together more effectively, aligning on a proactive FinOps strategy rather than reacting to unexpected invoices.
- The company prevented future cost overruns by automating budget governance processes through Datadog’s real-time monitoring.
A global SaaS company struggling with rising cloud costs across AWS, Azure, and GCP needed a way to optimize spending without sacrificing performance. EverOps, a premier Datadog partner, implemented a comprehensive cost optimization strategy using Datadog’s Cloud Cost Management, Continuous Profiler, and Infrastructure Monitoring. By providing real-time cost visibility, identifying underutilized resources, automating anomaly detection, and fine-tuning auto-scaling policies, EverOps helped the company cut cloud costs by 30%, saving $3.2M annually. More importantly, this transformation enabled engineering and finance teams to align on a proactive FinOps strategy, ensuring long-term cost efficiency without compromising scalability.
