Reducing Cloud Costs by 35% Through Rightsizing and Auto Scaling
A SaaS company was rapidly growing in size, with cloud costs increasing over different geographical regions. Excessive numbers of instances, underutilized resources at low usage times, and lack of insight into usage drove up costs without matching business value.
Bobcares measured the workload behavior, resource usage, traffic patterns, and application dependencies prior to rightsizing, auto scaling, load balancing, scheduling scaling, monitoring, and cost-visibility dashboards. The changes resulted in a nearly 35% drop in cloud infrastructure costs without compromising application performance under peak demand and automated cost savings in manual operations.
Business Challenge
Cloud costs were spiraling out of control due to the growth of applications. Even with overprovisioned resources, idle infrastructure, lack of utilization visibility, and manual scaling operations, unnecessary spending and delays in capacity scaling during traffic surges were observed. The customer wanted a flexible infrastructure structure that would prevent the cost of supporting applications from climbing out of hand while maintaining application performance, reliability, and customer experience.
Technology Used
Blueprint
Bobcares assessed workload utilization and traffic patterns to identify oversized resources and replace them with appropriately sized instances. Auto scaling adjusted compute capacity according to demand, while load balancing distributed traffic across instances. Scheduled scaling handled predictable workloads, with monitoring, dashboards, and alerts providing continuous cost and performance visibility.
Key Outcomes
- Reduced cloud infrastructure costs by nearly 35%.
- Improved overall resource utilization.
- Enabled automatic scaling based on demand.
- Maintained stable performance during traffic spikes.
- Reduced unnecessary resources during low-demand periods.
- Improved visibility into cloud spending.
- Reduced manual intervention and operational overhead.
Customer Profile
The Challenges
- Overprovisioned virtual machines operated at low utilization levels.
- Resources remained active during low-traffic periods, creating unnecessary costs.
- Manual scaling delayed responses to traffic spikes.
- Some workloads had resources allocated beyond their actual requirements.
- Limited visibility made it difficult to assess resource utilization.
- Uncontrolled resource consumption resulted in unexpected spending increases.
- Engineering teams lacked sufficient insight to identify cloud cost leakage.
Why Bobcares?
- Assessed actual workload behavior before making infrastructure changes.
- Used CPU, memory, traffic, and dependency data to identify inefficient resource allocation.
- Combined rightsizing with dynamic scaling instead of relying on overprovisioning.
- Added monitoring and cost visibility to support continuous optimization.
- Introduced scaling policies suited to both real-time and predictable demand.
- Focused on reducing costs without disrupting application operations or performance.
What We Delivered
- Assessed existing cloud infrastructure and workload behavior.
- Analyzed CPU and memory utilization.
- Reviewed traffic patterns and application dependencies.
- Rightsized oversized compute instances.
- Configured auto scaling based on workload demand.
- Introduced scheduled scaling for predictable workload patterns.
- Implemented load balancing to distribute traffic across instances.
- Added monitoring and cost-visibility dashboards.
- Configured alerts for abnormal resource consumption.
- Optimized resource planning for stable workloads.
The Results
Cloud Infrastructure Cost
- Before: Cloud infrastructure costs were rising beyond a sustainable level as the application expanded.
- After: Total cloud infrastructure costs were reduced by almost 35%.
Resource Utilization
- Before: Overprovisioned instances and inefficient resource allocation resulted in low utilization.
- After: Resource utilization improved across the cloud environment through rightsizing and demand-based scaling.
Scaling
- Before: Manual scaling caused delayed capacity adjustments during traffic spikes.
- After: Auto scaling dynamically adjusted compute capacity based on demand.
Application Performance
- Before: Manual scaling created a risk of delayed capacity during periods of increased traffic.
- After: Application performance remained stable during traffic spikes without relying on continuous overprovisioning.
Cost Visibility
- Before: Limited visibility made it difficult to identify resource waste and unexpected spending.
- After: Monitoring dashboards and alerts provided better visibility and control over cloud consumption.
Operational Effort
- Before: Scaling and resource management required greater manual intervention.
- After: Automated scaling reduced manual intervention and operational overhead.
The Business Impact
- Reduced infrastructure expenditure created additional budget flexibility for product development and business growth.
- Improved resource efficiency reduced pressure from unnecessary cloud capacity.
- Automatic scaling gave the business greater flexibility to respond to workload changes.
- Stable performance during traffic spikes supported a consistent customer experience.
- Better cost visibility enabled the engineering team to manage spending proactively.
- Reduced manual intervention lowered the operational burden on engineering teams.
- The optimized infrastructure provided a more sustainable model for continued SaaS growth.
Ongoing Collaboration
- Continuous monitoring of resource utilization and workload behavior.
- Ongoing review of cloud cost visibility and consumption patterns.
- Adjustment of scaling policies as workload demand changes.
- Monitoring of application performance during traffic variations.
- Continued rightsizing and resource planning for changing workloads.
- Review of abnormal resource consumption through configured alerts.
Conclusion
Bobcares helped the SaaS company reduce cloud infrastructure costs by almost 35% through rightsizing, auto scaling, scheduled scaling, load balancing, and continuous monitoring. The optimized environment improved resource efficiency while maintaining stable application performance during traffic spikes.
The resulting infrastructure provided better cost control, reduced operational effort, and greater flexibility to scale with changing workload demand.
