Autonomous Cost Optimizer

Tracks and optimizes token usage and API costs during autonomous coding.

Quick Start

Track Usage

from scripts.cost_optimizer import CostOptimizer

optimizer = CostOptimizer(project_dir)
optimizer.track_usage(input_tokens=1500, output_tokens=500)

report = optimizer.get_usage_report()
print(f"Total cost: ${report.total_cost:.4f}")

Check Budget

if optimizer.is_within_budget(budget=10.00):
    # Continue working
    pass
else:
    # Trigger cost-saving measures
    await optimizer.enter_efficiency_mode()

Cost Optimization Workflow

┌─────────────────────────────────────────────────────────────┐
│                 COST OPTIMIZATION                           │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  TRACK                                                      │
│  ├─ Monitor token usage per request                        │
│  ├─ Calculate cost per feature                             │
│  ├─ Track cumulative session cost                          │
│  └─ Log usage to history                                   │
│                                                             │
│  ANALYZE                                                    │
│  ├─ Identify high-cost operations                          │
│  ├─ Compare efficiency across features                     │
│  ├─ Detect wasteful patterns                               │
│  └─ Calculate ROI per feature                              │
│                                                             │
│  OPTIMIZE                                                   │
│  ├─ Compact context when approaching limits                │
│  ├─ Cache repeated queries                                 │
│  ├─ Batch similar operations                               │
│  └─ Prioritize high-ROI features                           │
│                                                             │
│  REPORT                                                     │
│  ├─ Generate cost breakdown                                │
│  ├─ Show efficiency metrics                                │
│  └─ Recommend optimizations                                │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Pricing Reference

Model	Input (per 1M)	Output (per 1M)
Claude 3.5 Sonnet	$3.00	$15.00
Claude 3 Opus	$15.00	$75.00
Claude 3 Haiku	$0.25	$1.25

Efficiency Metrics

@dataclass
class EfficiencyMetrics:
    tokens_per_feature: float
    cost_per_feature: float
    features_per_dollar: float
    context_utilization: float
    cache_hit_rate: float

Optimization Strategies

Strategy	Savings	Trade-off
Context compaction	20-40%	Slight context loss
Response caching	30-50%	Storage needed
Batch operations	15-25%	Higher latency
Model selection	50-90%	Capability reduction

Integration Points

• context-compactor: Reduce context size
• memory-manager: Cache common queries
• autonomous-loop: Budget enforcement
• progress-tracker: Efficiency metrics

References

• references/PRICING-GUIDE.md - Cost calculations
• references/OPTIMIZATION-STRATEGIES.md - Strategies

Scripts

• scripts/cost_optimizer.py - Core optimizer
• scripts/usage_tracker.py - Track token usage
• scripts/budget_manager.py - Budget enforcement
• scripts/efficiency_analyzer.py - Analyze efficiency