LLM Cost Optimizer

Overview

LLM API costs grow fast — a chatbot doing 10K conversations/day at $0.01 each is $3K/month. This skill builds cost controls: track spending per feature and user, route simple tasks to cheap models, cache repeated queries, compress prompts, and alert before budgets blow up.

When to Use

• Monthly LLM bill is growing and you need visibility into what's driving it
• Some features use GPT-4o when GPT-4o-mini would work fine
• Users ask the same questions repeatedly — cache those responses
• Need budget limits per team, feature, or API key
• Comparing total cost of ownership between providers

Instructions

Strategy 1: Cost Tracking Middleware

Wrap every LLM call to log tokens, cost, model, and feature. Know exactly where money goes.

// cost-tracker.ts — Track LLM costs per feature, model, and user
/**
 * Middleware that wraps LLM API calls, logs token usage
 * and estimated cost, and enforces budget limits.
 * Drop-in replacement for direct API calls.
 */

interface CostEntry {
  timestamp: string;
  model: string;
  feature: string;         // Which product feature made this call
  userId?: string;
  inputTokens: number;
  outputTokens: number;
  cachedTokens: number;
  costUsd: number;
  latencyMs: number;
}

// Pricing per 1M tokens (input / output) — update as providers change
const PRICING: Record<string, { input: number; output: number; cached?: number }> = {
  "gpt-4o":                  { input: 2.50,  output: 10.00, cached: 1.25 },
  "gpt-4o-mini":             { input: 0.15,  output: 0.60,  cached: 0.075 },
  "claude-sonnet-4-20250514":   { input: 3.00,  output: 15.00, cached: 0.30 },
  "claude-haiku-3-20250722": { input: 0.25,  output: 1.25,  cached: 0.025 },
  "llama-3.1-8b":            { input: 0.05,  output: 0.05 },  // Self-hosted estimate
};

export class CostTracker {
  private entries: CostEntry[] = [];
  private budgets: Map<string, number> = new Map();  // feature → monthly limit USD

  /**
   * Calculate cost for a single LLM call.
   */
  calculateCost(model: string, inputTokens: number, outputTokens: number, cachedTokens: number = 0): number {
    const pricing = PRICING[model];
    if (!pricing) return 0;

    const inputCost = ((inputTokens - cachedTokens) * pricing.input) / 1_000_000;
    const cachedCost = pricing.cached
      ? (cachedTokens * pricing.cached) / 1_000_000
      : 0;
    const outputCost = (outputTokens * pricing.output) / 1_000_000;

    return inputCost + cachedCost + outputCost;
  }

  /**
   * Log an LLM call and check budget.
   */
  track(entry: Omit<CostEntry, "costUsd" | "timestamp">): CostEntry {
    const costUsd = this.calculateCost(
      entry.model, entry.inputTokens, entry.outputTokens, entry.cachedTokens
    );

    const full: CostEntry = {
      ...entry,
      costUsd,
      timestamp: new Date().toISOString(),
    };

    this.entries.push(full);

    // Check budget
    const monthlySpend = this.getMonthlySpend(entry.feature);
    const budget = this.budgets.get(entry.feature);
    if (budget && monthlySpend > budget) {
      console.warn(`⚠️ Budget exceeded for "${entry.feature}": $${monthlySpend.toFixed(2)} / $${budget}`);
    }

    return full;
  }

  setBudget(feature: string, monthlyLimitUsd: number): void {
    this.budgets.set(feature, monthlyLimitUsd);
  }

  getMonthlySpend(feature?: string): number {
    const now = new Date();
    const monthStart = new Date(now.getFullYear(), now.getMonth(), 1);

    return this.entries
      .filter((e) => new Date(e.timestamp) >= monthStart)
      .filter((e) => !feature || e.feature === feature)
      .reduce((sum, e) => sum + e.costUsd, 0);
  }

  /**
   * Generate a cost report grouped by feature and model.
   */
  report(): Record<string, { calls: number; tokens: number; cost: number }> {
    const groups: Record<string, { calls: number; tokens: number; cost: number }> = {};

    for (const entry of this.entries) {
      const key = `${entry.feature} → ${entry.model}`;
      if (!groups[key]) groups[key] = { calls: 0, tokens: 0, cost: 0 };
      groups[key].calls++;
      groups[key].tokens += entry.inputTokens + entry.outputTokens;
      groups[key].cost += entry.costUsd;
    }

    return groups;
  }
}

Strategy 2: Smart Model Router

Route tasks to the cheapest model that can handle them. Hard tasks → expensive model. Easy tasks → cheap model.

// model-router.ts — Route LLM calls to the cheapest capable model
/**
 * Analyzes task complexity and routes to the appropriate model.
 * Simple classification/extraction → mini model (~95% cheaper).
 * Complex reasoning/coding → full model.
 */

interface RouteDecision {
  model: string;
  reason: string;
  estimatedCostRatio: number;  // 1.0 = full price, 0.1 = 10% of full price
}

export function routeModel(task: string, context?: string): RouteDecision {
  const taskLower = task.toLowerCase();
  const contextLength = (context || "").length;

  // Simple extraction / classification → mini model
  if (
    taskLower.includes("extract") ||
    taskLower.includes("classify") ||
    taskLower.includes("categorize") ||
    taskLower.includes("summarize") ||
    taskLower.includes("translate") ||
    taskLower.includes("format")
  ) {
    return {
      model: "gpt-4o-mini",
      reason: "Simple extraction/classification task",
      estimatedCostRatio: 0.06,  // ~6% of GPT-4o cost
    };
  }

  // Short context + simple question → mini
  if (contextLength < 2000 && !requiresReasoning(taskLower)) {
    return {
      model: "gpt-4o-mini",
      reason: "Short context, simple task",
      estimatedCostRatio: 0.06,
    };
  }

  // Code generation / debugging → full model
  if (
    taskLower.includes("write code") ||
    taskLower.includes("debug") ||
    taskLower.includes("refactor") ||
    taskLower.includes("architect")
  ) {
    return {
      model: "claude-sonnet-4-20250514",
      reason: "Code generation requires strong reasoning",
      estimatedCostRatio: 1.0,
    };
  }

  // Complex reasoning → full model
  return {
    model: "gpt-4o",
    reason: "Complex task requiring strong reasoning",
    estimatedCostRatio: 0.8,
  };
}

function requiresReasoning(task: string): boolean {
  const reasoningKeywords = [
    "analyze", "compare", "evaluate", "design", "architect",
    "debug", "optimize", "explain why", "trade-off", "recommend",
  ];
  return reasoningKeywords.some((k) => task.includes(k));
}

Strategy 3: Semantic Cache

Cache LLM responses by meaning, not exact match. "What's the weather?" and "How's the weather today?" should hit the same cache.

# semantic_cache.py — Cache LLM responses by semantic similarity
"""
Caches LLM responses using embedding similarity.
If a new query is semantically similar to a cached one,
return the cached response instead of calling the API.
Saves 30-60% on repetitive workloads.
"""
import hashlib
import json
import time
from typing import Optional
import numpy as np
import openai

class SemanticCache:
    """LLM response cache using embedding similarity."""

    def __init__(self, similarity_threshold: float = 0.92, ttl_seconds: int = 3600):
        """
        Args:
            similarity_threshold: Min cosine similarity to consider a cache hit (0.92 = very similar)
            ttl_seconds: Cache entry expiration time
        """
        self.threshold = similarity_threshold
        self.ttl = ttl_seconds
        self.cache: list[dict] = []  # In production, use Redis or a vector DB
        self.client = openai.OpenAI()
        self.stats = {"hits": 0, "misses": 0, "saved_usd": 0.0}

    def get(self, query: str) -> Optional[str]:
        """Check cache for a semantically similar query.

        Args:
            query: The user's query

        Returns:
            Cached response if found, None otherwise
        """
        query_embedding = self._embed(query)
        now = time.time()

        best_match = None
        best_score = 0.0

        for entry in self.cache:
            # Skip expired entries
            if now - entry["timestamp"] > self.ttl:
                continue

            score = self._cosine_similarity(query_embedding, entry["embedding"])
            if score > best_score:
                best_score = score
                best_match = entry

        if best_match and best_score >= self.threshold:
            self.stats["hits"] += 1
            self.stats["saved_usd"] += best_match.get("cost_usd", 0.01)
            return best_match["response"]

        self.stats["misses"] += 1
        return None

    def set(self, query: str, response: str, cost_usd: float = 0.01) -> None:
        """Store a query-response pair in the cache."""
        self.cache.append({
            "query": query,
            "response": response,
            "embedding": self._embed(query),
            "cost_usd": cost_usd,
            "timestamp": time.time(),
        })

    def _embed(self, text: str) -> list[float]:
        response = self.client.embeddings.create(
            model="text-embedding-3-small",
            input=text
        )
        return response.data[0].embedding

    def _cosine_similarity(self, a: list[float], b: list[float]) -> float:
        a_arr, b_arr = np.array(a), np.array(b)
        return float(np.dot(a_arr, b_arr) / (np.linalg.norm(a_arr) * np.linalg.norm(b_arr)))

Examples

Example 1: Cut chatbot costs by 60%

User prompt: "Our support chatbot costs $3K/month on GPT-4o. Most questions are FAQs. Help me reduce costs without hurting quality."

The agent will:

• Add cost tracking middleware to identify which question types cost the most
• Set up semantic cache for FAQ-type questions (covers ~40% of volume)
• Route simple questions to GPT-4o-mini (covers ~30% more)
• Keep GPT-4o only for complex, novel questions
• Result: ~60% cost reduction while maintaining quality on hard questions

Example 2: Set up budget alerts

User prompt: "I want to cap our AI spending at $500/month per team and get alerts at 80%."

The agent will use CostTracker with per-team budgets, add webhook alerts at 80% threshold, and generate weekly cost reports broken down by team and feature.

Guidelines

• Track before optimizing — you can't reduce what you don't measure
• Mini models handle 70% of tasks — GPT-4o-mini and Haiku are dramatically cheaper
• Semantic cache threshold 0.92+ — lower risks returning wrong answers
• Cache TTL depends on data freshness — static FAQs: hours. Dynamic data: minutes.
• Prompt caching is free money — OpenAI and Anthropic cache system prompts automatically
• Batch API = 50% off — OpenAI's Batch API halves cost for non-realtime workloads
• Shorter prompts = lower costs — remove examples and instructions the model already knows
• Monitor cost per user — detect abuse early, especially on free tiers
• Self-hosted models for high volume — at 1M+ calls/month, Llama on your own GPU can be cheaper