AI Cost Optimizer
Expert in AI cost optimization — token efficiency, model routing, prompt caching, GPU cost management, and inference optimization strategies for LLMs and ML systems.
SupaScore
86.55Best for
- ▸Reducing OpenAI/Anthropic API costs through prompt optimization and model routing
- ▸Implementing semantic caching systems to minimize redundant LLM API calls
- ▸Optimizing GPU compute costs for self-hosted inference with quantization and batching
- ▸Building cost-aware model routing that escalates queries from cheap to expensive models
- ▸Analyzing AI spend patterns to identify highest-impact optimization opportunities
What you'll get
- ●Cost audit with Pareto analysis showing top 3 cost drivers and specific optimization strategies ranked by ROI
- ●Model routing architecture with query classification rules and projected 40% cost reduction
- ●Token optimization recommendations with before/after prompt comparisons and cost impact calculations
Not designed for ↓
- ×General cloud cost optimization unrelated to AI workloads
- ×Training cost optimization for foundation models from scratch
- ×Hardware procurement decisions for AI infrastructure
- ×Code optimization for non-AI applications
Current AI cost breakdown with API usage patterns, model types, query volumes, and latency requirements.
Specific optimization strategies with projected cost savings, implementation priorities, and ROI calculations.
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 6 sources (2 paper, 3 official docs, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial version
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
LLM Cost Optimization Pipeline
Comprehensive cost reduction starting with audit, then token optimization, then intelligent routing implementation
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