AI/ML cost optimization that learns
Optimize GPU usage, training costs, and inference scaling. The only platform that understands both AI workloads and cloud infrastructure optimization.
AI/ML Cost Challenges
GPU Costs
Expensive GPU instances running 24/7 with poor utilization
Training Expenses
Model training costs spiraling out of control
Inference Scaling
Unpredictable inference demands and scaling challenges
Model Optimization
Balancing model performance with infrastructure costs
AI/ML Optimization Strategies
GPU Utilization Optimization
60-80% savingsMaximize expensive GPU usage with intelligent scheduling and resource sharing
Training Cost Management
50-70% savingsReduce model training costs through efficient resource allocation and spot instances
Inference Optimization
40-60% savingsOptimize model serving costs with intelligent scaling and resource management
API Cost Management
30-50% savingsOptimize costs for external AI APIs like OpenAI, Anthropic, and cloud AI services
Real AI/ML Workload Optimizations
Large Language Model Training
Multi-GPU training clusters running for weeks
Spot instance orchestration with checkpointing
Real-time Inference APIs
Variable traffic with expensive always-on GPU instances
Intelligent autoscaling with GPU sharing
Batch Data Processing
Periodic ML jobs with over-provisioned infrastructure
Job scheduling with spot instances
Model Experimentation
Research teams with idle development instances
Auto-shutdown and resource sharing
AI Research Lab Success Story
AI Research Lab
Computer Vision & NLP Research
Challenge
Managing $200K/month GPU costs across multiple research projects with unpredictable workloads
Implementation
Results
Across all AI workloads
Through intelligent sharing
With spot instance management
Better resource allocation
AI Provider Cost Optimization
OpenAI
Services
Optimizations
Anthropic
Services
Optimizations
AWS AI Services
Services
Optimizations
Google Cloud AI
Services
Optimizations
AI/ML-Specific Features
GPU Pool Management
Intelligent GPU sharing and scheduling across training and inference workloads.
Model Lifecycle Optimization
Optimize costs throughout the entire ML lifecycle from experimentation to production.
API Cost Intelligence
Track and optimize usage across all AI API providers with intelligent routing.