llm-cost-optimization

Imported from sickn33/agentic-awesome-skills (skills/llm-cost-optimization/SKILL.md). Install upstream with npx skills add sickn33/agentic-awesome-skills --skill llm-cost-optimization. Copyright stays with the author (MIT).

LLM Cost Optimization

Evaluate LLM cost reductions through caching, model selection, prompt optimization and self-hosting; measure quality and total cost before claiming savings.

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

Estimates and cache boundaries

All savings ranges, model IDs and prices below are historical illustrations, not current quotes or guaranteed outcomes. Verify provider pricing, cache-write/read charges, minimum prefixes and batch availability for the chosen model. Use an approved budget/data scope before paid calls or uploads. The semantic cache is a toy: namespace keys by tenant, authorization scope, model/version, system prompt and generation settings, enforce retention and never reuse a similar answer across users or for permission-sensitive/fresh data. Replace blocking Redis KEYS with a bounded index/SCAN or vector query; handle expired entries and zero vectors. The sample is not production-ready.

Cost Levers by Impact

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)

Prompt Caching (Provider-Side)

# Anthropic — cache long system prompts (saves 90% on cached tokens)
import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a helpful assistant.",
        },
        {
            "type": "text",
            "text": open("large-context.txt").read(),  # large doc
            "cache_control": {"type": "ephemeral"},     # cache this!
        }
    ],
    messages=[{"role": "user", "content": "Summarize the key points."}],
)
# Cache creation/read pricing and eligibility vary by model/provider/TTL; measure actual usage.
print(f"Cache read tokens: {response.usage.cache_read_input_tokens}")

# OpenAI — prompt caching is automatic for repeated prefixes >1024 tokens
# No code change needed; check usage.prompt_tokens_details.cached_tokens

Batching with OpenAI Batch API (50% Discount)

import json
from openai import OpenAI

client = OpenAI()

# Prepare batch requests
requests = [
    {
        "custom_id": f"task-{i}",
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "gpt-4o-mini",
            "messages": [{"role": "user", "content": f"Classify: {text}"}],
            "max_tokens": 50,
        }
    }
    for i, text in enumerate(texts)
]

# Write JSONL file
with open("batch.jsonl", "w") as f:
    for req in requests:
        f.write(json.dumps(req) + "\n")

# Upload and create batch
batch_file = client.files.create(file=open("batch.jsonl", "rb"), purpose="batch")
batch = client.batches.create(
    input_file_id=batch_file.id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
)
print(f"Batch ID: {batch.id}")  # poll status with client.batches.retrieve(batch.id)

Semantic Caching

import hashlib
import json
import redis
import numpy as np
from sentence_transformers import SentenceTransformer

r = redis.Redis(host="localhost", port=6379)
embed_model = SentenceTransformer("BAAI/bge-small-en-v1.5")

SIMILARITY_THRESHOLD = 0.92
CACHE_TTL = 3600 * 24  # 24 hours

def cached_llm_call(prompt: str, llm_fn) -> str:
    # 1. Exact match (free)
    exact_key = f"exact:{hashlib.sha256(prompt.encode()).hexdigest()}"
    if cached := r.get(exact_key):
        return cached.decode()

    # 2. Semantic match
    query_vec = embed_model.encode(prompt)
    cached_keys = r.keys("sem:*")
    for key in cached_keys:
        data = json.loads(r.get(key))
        similarity = np.dot(query_vec, data["embedding"]) / (
            np.linalg.norm(query_vec) * np.linalg.norm(data["embedding"])
        )
        if similarity >= SIMILARITY_THRESHOLD:
            return data["response"]

    # 3. Cache miss — call LLM
    response = llm_fn(prompt)

    # Store exact match
    r.setex(exact_key, CACHE_TTL, response)

    # Store semantic embedding
    sem_key = f"sem:{hashlib.sha256(prompt.encode()).hexdigest()}"
    r.setex(sem_key, CACHE_TTL, json.dumps({
        "embedding": query_vec.tolist(),
        "response": response,
        "prompt": prompt,
    }))
    return response

Prompt Compression

# LLMLingua — compress long prompts by 3–20× with minimal quality loss
from llmlingua import PromptCompressor

compressor = PromptCompressor(
    model_name="microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
    device_map="cpu",
)

compressed = compressor.compress_prompt(
    long_context,
    ratio=0.5,       # keep 50% of tokens
    rank_method="longllmlingua",
)
print(f"Original: {len(long_context.split())} words")
print(f"Compressed: {len(compressed['compressed_prompt'].split())} words")
print(f"Savings: {compressed['saving']}")

Self-Hosting Break-Even Calculator

def break_even_analysis(
    monthly_api_spend_usd: float,
    gpu_cost_per_hour_usd: float = 2.50,   # e.g., A10G on AWS
    billable_hours: float = 720,          # uptime billed, not GPU utilization
) -> dict:
    if monthly_api_spend_usd <= 0 or gpu_cost_per_hour_usd < 0 or billable_hours < 0:
        raise ValueError("spend must be positive; rate and billable hours nonnegative")
    monthly_gpu_cost = gpu_cost_per_hour_usd * billable_hours
    break_even = monthly_gpu_cost / monthly_api_spend_usd
    recommendation = (
        "Self-host now — strong ROI" if break_even < 0.5 else
        "Self-host if traffic grows 2×" if break_even < 0.8 else
        "Stick with API — not enough scale yet"
    )
    return {
        "monthly_gpu_cost": f"${monthly_gpu_cost:.0f}",
        "monthly_api_spend": f"${monthly_api_spend_usd:.0f}",
        "gpu_as_pct_of_api": f"{break_even*100:.0f}%",
        "recommendation": recommendation,
    }

# Example: $5k/month on OpenAI, $2.50/hr A10G
print(break_even_analysis(5000))
# → GPU-only cost $1,800/mo = 36% of API spend; add storage, ops, idle capacity and quality costs

Cost Dashboard (Grafana)

# Emit cost metrics to Prometheus
from prometheus_client import Counter, Histogram

llm_cost_total = Counter(
    "llm_cost_usd_total",
    "Total LLM spend in USD",
    ["model", "team", "task_type"],
)
llm_tokens_total = Counter(
    "llm_tokens_total",
    "Total tokens used",
    ["model", "token_type"],  # token_type: prompt, completion, cached
)

def track_call(model, team, task_type, response):
    cost = calculate_cost(model, response.usage)
    llm_cost_total.labels(model=model, team=team, task_type=task_type).inc(cost)
    llm_tokens_total.labels(model=model, token_type="prompt").inc(
        response.usage.prompt_tokens)
    llm_tokens_total.labels(model=model, token_type="completion").inc(
        response.usage.completion_tokens)

Best Practices

• Use gpt-4o-mini or claude-haiku for 80% of tasks — they're 10–30× cheaper.
• Enable prompt caching for system prompts >1,024 tokens (Anthropic) or >1,024 tokens (OpenAI).
• Audit your top 5 prompts by token count — compress or cache them.
• Set hard budget limits with LiteLLM or Bifrost virtual keys before costs spiral.
• Compare measured quality, throughput, billable uptime and full operating cost before choosing self-hosting; no monthly-spend threshold alone establishes break-even.

Related Skills

• llm-gateway (llm-gateway) - Centralized cost control
• llm-caching (llm-caching) - Semantic caching patterns
• vllm-server (vllm-server) - Self-hosted inference
• agent-observability (agent-observability) - Token and cost telemetry

Limitations

• Guidance executes against real environments: confirm target, blast radius, and rollback plan before applying anything.
• Never deploy to production without explicit approval. Docs-only import: upstream scripts and templates not bundled.

Example

git status && git diff --stat
kubectl diff -f manifest.yaml

Adapted from BagelHole/DevOps-Security-Agent-Skills (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: helper scripts and templates not bundled.