Spend Efficiency
LLM + Cloud Cost Optimization
Comprehensive reviews of LLM usage patterns and cloud commitments that typically find 30-50% savings opportunities. Right-size your AI infrastructure spending without sacrificing performance.
Typical savings identified in existing spend
Weeks to complete analysis and recommendations
Typical ROI on optimization engagement
Who it's for
CFOs concerned about runaway AI and cloud spending
CIOs seeking to optimize infrastructure without degrading performance
CTOs needing independent assessment of vendor commitments
Timeline
What we do
- LLM usage pattern analysis across all deployments
- Token efficiency and model selection review
- Cloud commitment optimization (reserved instances, savings plans)
- Architecture review for cost-effective scaling
- Vendor pricing comparison and negotiation support
- Prompt optimization for reduced token consumption
- Caching and retrieval strategy assessment
- Implementation roadmap for identified savings
What we don't do
- Ongoing cost management services
- Vendor contract negotiation (guidance only)
- Infrastructure migration execution
- Performance optimization unrelated to cost
Where we find savings
Model Right-Sizing
15-25%Using appropriate models for each task complexity
Prompt Optimization
10-20%Reducing token consumption without quality loss
Caching Strategy
20-40%Avoiding redundant API calls for common queries
Cloud Commitments
20-35%Reserved instances and savings plans optimization
Deliverables
Cost Analysis Report
Detailed breakdown of current spending by category
Optimization Roadmap
Prioritized list of savings opportunities with estimates
Quick Wins
Immediate actions for 30-day savings
Architecture Recommendations
Longer-term changes for sustained optimization
Vendor Comparison
Neutral assessment of alternative providers
Implementation Support
Hands-on help with high-priority changes
Frequently asked questions
