Scaling AI Infrastructure for 100x Growth
Architected ML infrastructure that scaled from 1K to 100K daily predictions while reducing costs by 70%.
The Challenge
Our AI client's models were gaining traction, but their infrastructure costs were unsustainable. At current growth rate, they'd burn through runway in 4 months.
- •GPU costs exceeding revenue by 3x
- •15-minute inference time for complex models
- •No ability to handle traffic spikes
- •Manual model deployment taking days
Our Approach
We redesigned their ML infrastructure for efficiency, implementing intelligent caching and auto-scaling to dramatically reduce costs.
Infrastructure Audit (Week 1-2)
- ✓Cost analysis
- ✓Performance profiling
- ✓Architecture review
Optimization (Weeks 3-8)
- ✓Model optimization
- ✓Intelligent caching layer
- ✓Auto-scaling implementation
Automation (Weeks 9-12)
- ✓CI/CD for ML models
- ✓A/B testing framework
- ✓Cost monitoring
The Results
The platform now serves 100K+ predictions daily at 70% lower cost with 50ms average latency.
Daily Predictions
100K+10K%
Infrastructure Cost
$8K/mo70%
Inference Time
50ms99.7%
Deployment Time
10 min99%
"Drexus turned our biggest weakness into our competitive advantage. Our infrastructure costs are now 10x lower than competitors while being 100x faster."
Technology Stack
ml
PyTorch
TensorFlow
ONNX
infrastructure
Kubernetes
Ray
Triton
data
Apache Kafka
Redis
S3
monitoring
Prometheus
Grafana
MLflow
Key Outcomes
✓Reduced infrastructure costs by 70%
✓Improved inference speed by 99.7%
✓Enabled 100x growth in predictions
✓Automated model deployment process
Project Details
- Industry
- AI/ML
- Timeline
- 3 months
- Team Size
- 3-4 engineers
- Services
- ML InfrastructureCost OptimizationPerformance Engineering