audit/10_Cost_Control.md

Topic 10: Cost Control & Token Optimization Strategy

Audit Date: 2026-02-01 Auditor: Agent Antigravity Scope: FinOps & Efficiency

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1. The "Zero-Waste" Protocol

The system is engineered to minimize "Token Burn" (Wasted input tokens) and "Looping Costs".

A. Token Economy Strategy

Tactic	Implementation	Savings Impact
Prompt Caching	The massive SCAM_TAXONOMY (2k tokens) is static. Groq caches likely 90% of inputs.	High (~50%)
Model Downsizing	"Stall" messages and simple "Hook" replies use 8B models instead of 70B.	High (~80%)
Strict Output	JSON_SCHEMA forces the LLM to output only the JSON, no "Here is your analysis..." chatter.	Medium (~20%)

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2. Hard Limits (Bill Shock Prevention)

• Max Context: config.MAX_CONVERSATION_LENGTH = 50.
  • Why? Prevents infinite loops where an attacker keeps the bot talking forever.
• Max Tokens: LLM_MAX_TOKENS = 500.
  • Why? Prevents the model from generating 4-page essays when a 1-line reply is needed.
• Rate Limits: 30 Requests/Minute.
  • Calculation: 30 Req * 2k In / 500 Out = ~75k Tokens/Min Max.
  • Cost: At Groq prices (~$0.50/M), max burn is ~$0.04/min. Sustainable.

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3. Dynamic Optimization

• File: app/core/llm_client.py
• Logic: _switchboard(role)
  • If Status == Hook (Simple): Use FAST_MODEL (Cheap).
  • If Status == Extract (Complex): Use SMART_MODEL (Expensive).
  • Result: You don't pay for a PhD (70B) to say "Hello" (8B).

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4. Financial Resilience

The system includes code to switch providers if RateLimitError (429) occurs.

1. Try Groq (Primary).
2. Fail -> Try OpenAI (Backup).
3. Fail -> Return Mocked/Heuristic Response (Free).

• Audit: Verified in LLMClient.generate_with_retry().