Auto-Scaling Infrastructure
Dynamic scaling architecture with predictive scaling and spot instance optimization.
Duration
4 months
Team Size
3 developers
Industry
SaaS
Category
Cloud & DevOps
Auto-Scaling Infrastructure
A dynamic scaling architecture that automatically adjusts capacity based on demand, utilizing spot instances and predictive scaling for optimal cost efficiency.
The Challenge
A SaaS company had traffic that varied wildly:
- Unpredictable spikes - 10x traffic during marketing campaigns
- Over-provisioned - Paying for peak capacity 24/7
- Manual scaling - Ops team scrambling during peaks
- Cost waste - 70% of capacity unused most of the time
They needed intelligent, automated scaling.
Our Approach
We built a multi-layer auto-scaling system with cost optimization.
Scaling Strategy
- Reactive Scaling - Scale based on current metrics
- Predictive Scaling - Anticipate known patterns
- Spot Optimization - Use cheap capacity when available
- Event-Based - Scale for scheduled events
The Solution
Application Scaling
- Kubernetes HPA for pods
- KEDA for event-driven scaling
- Custom metrics scaling
- Scale-to-zero for dev
Infrastructure Scaling
- Cluster autoscaler
- Spot instance integration
- On-demand fallback
- Multi-AZ distribution
Predictive Scaling
- Time-series forecasting
- Scheduled scaling actions
- Campaign-aware scaling
- Machine learning predictions
Cost Optimization
- Spot fleet management
- Reserved instance coverage
- Savings plan optimization
- Waste elimination
Technology Stack
| Layer | Technologies |
|---|---|
| Cloud | AWS (EC2, EKS) |
| Container | Kubernetes |
| Scaling | KEDA, HPA, Cluster Autoscaler |
| Spot | Spot.io, AWS Spot Fleet |
| Monitoring | Prometheus, Grafana |
| IaC | Terraform |
Results & Impact
The architecture delivered significant savings:
- 70% lower costs through spot and right-sizing
- 100x spikes handled automatically
- Zero manual intervention required
- Sub-minute scale-up response
Scaling Patterns
Workload Types
- Web traffic (request-based)
- Queue processing (queue depth)
- Scheduled jobs (cron-based)
- Batch processing (event-based)
Spot Strategies
- Diversified instance pools
- Graceful spot interruption handling
- On-demand fallback automation
- Savings tracking
Client Testimonial
"We went from $200K monthly compute costs to $60K while actually handling more traffic. The spot instance optimization was a game-changer."
— VP of Engineering, SaaS Company
Optimizing scaling? Contact us to discuss auto-scaling solutions.
Key Results
70% reduction in compute costs
Handles 100x traffic spikes
Zero manual scaling
Sub-minute scale-up time
Technology Stack
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