llm-cost-optimization

LLM Cost Optimization

Cut LLM costs by 50–90% with the right combination of caching, model selection, prompt optimization, and self-hosting.

When to Use This Skill

Use this skill when:

• LLM API spend is growing faster than revenue
• You need to attribute AI costs to teams, products, or customers
• Implementing caching to avoid redundant LLM calls
• Deciding when to switch from API providers to self-hosted models
• Optimizing prompt length without sacrificing quality

Cost Levers by Impact

Strategy	Typical Savings	Effort
Semantic caching	20–50%	Low
Model right-sizing	30–70%	Low
Prompt compression	10–30%	Medium
Provider caching (prompt cache)	10–25%	Low
Batching offline workloads	50% (Batch API)	Medium
Self-hosting 7–8B models	80–95% at scale	High
Quantization	30–50% VRAM cost	Medium

Track Costs First

# Use LiteLLM's cost tracking (automatic per-model pricing)
import litellm

response = litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
)
cost = litellm.completion_cost(response)
print(f"Cost: ${cost:.6f}")

# Add custom cost callbacks
def log_cost(kwargs, completion_response, start_time, end_time):
    cost = kwargs.get("response_cost", 0)
    model = kwargs.get("model")
    user = kwargs.get("user")
    # Send to your analytics DB
    db.record_cost(user=user, model=model, cost=cost)

litellm.success_callback = [log_cost]

Model Right-Sizing

# Route by task complexity — don't use GPT-4o for everything
def get_model_for_task(task_type: str) -> str:
    routing = {
        "classification":     "gpt-4o-mini",      # ~30× cheaper than gpt-4o
        "summarization":      "gpt-4o-mini",
        "extraction":         "gpt-4o-mini",
        "simple_qa":          "gpt-4o-mini",
        "complex_reasoning":  "gpt-4o",
        "code_generation":    "claude-sonnet-4-6",
        "creative_writing":   "claude-opus-4-6",
    }
    return routing.get(task_type, "gpt-4o-mini")

# Cost comparison (per 1M tokens, 2025 approx.)
# gpt-4o-mini:          input $0.15 / output $0.60
# gpt-4o:               input $2.50 / output $10.00
# claude-sonnet-4-6:    input $3.00 / output $15.00
# llama-3.1-8b (self):  ~$0.05–0.10 all-in (GPU amortized)