LLM Cost Optimization
Reduce LLM API and infrastructure costs through model selection, prompt caching...
Install
npx skills add BagelHole/DevOps-Security-Agent-Skills --skill llm-cost-optimization -g -a claude-code -y- Repository
- BagelHole/DevOps-Security-Agent-Skills1.2k
- Category
- AI and agents
- Updated
- 6mo ago
- License
- MIT
- Context cost
- Mediumabout 2,157 tokens
- Runs scripts
- No, instructions only
What it does
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
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]
