Implement cost optimization recommendations

[jwilger]

Priority: LOW (Post-MVP)
Size: Large
Description: Build ML-based system to recommend cost optimizations. This helps organizations reduce LLM costs without sacrificing quality.

Context: LLM costs can be significant. Automated analysis can identify optimization opportunities that humans might miss.

Acceptance Criteria:

• Data analysis pipeline:
  • Usage pattern analysis
  • Model performance correlation
  • Cost driver identification
• Optimization recommendations:
  • Model downgrades for simple tasks
  • Prompt length optimization
  • Caching opportunities
  • Batch processing suggestions
• What-if analysis:
  • Cost impact simulation
  • Quality impact estimation
  • Risk assessment
• ML model:
  • Training pipeline
  • Feature engineering
  • Model monitoring
• ROI calculations:
  • Estimated savings
  • Implementation effort
• Integration with UI dashboard

Dependencies:

• Implement request replay functionality #38 (metrics aggregation)
• Implement sampling strategies #39 (analytics dashboard)

Related ADRs

• ADR-0015: Caching Strategy - Defines the caching implementation that enables cost optimization through response caching, cache hit rate analysis, and cache efficiency monitoring for cost reduction recommendations