Cost & Latency Optimizer

Optimize LLM applications for cost and performance.

Cost Breakdown Analysis class CostAnalyzer: def init(self): self.costs = { "llm_calls": 0, "embeddings": 0, "tool_calls": 0, } self.counts = { "llm_calls": 0, "embeddings": 0, }

def track_llm_call(self, tokens_in: int, tokens_out: int):
    # GPT-4 pricing
    cost = (tokens_in / 1000) * 0.03 + (tokens_out / 1000) * 0.06
    self.costs["llm_calls"] += cost
    self.counts["llm_calls"] += 1

def report(self):
    return {
        "total_cost": sum(self.costs.values()),
        "breakdown": self.costs,
        "avg_cost_per_call": self.costs["llm_calls"] / self.counts["llm_calls"],
    }

Caching Strategy import hashlib from functools import lru_cache

class LLMCache: def init(self, redis_client): self.cache = redis_client self.ttl = 3600 # 1 hour

def get_cache_key(self, prompt: str, model: str) -> str:
    content = f"{model}:{prompt}"
    return f"llm_cache:{hashlib.sha256(content.encode()).hexdigest()}"

def get(self, prompt: str, model: str):
    key = self.get_cache_key(prompt, model)
    return self.cache.get(key)

def set(self, prompt: str, model: str, response: str):
    key = self.get_cache_key(prompt, model)
    self.cache.setex(key, self.ttl, response)

Usage

cache = LLMCache(redis_client)

def cached_llm_call(prompt: str, model: str = "gpt-4"): # Check cache cached = cache.get(prompt, model) if cached: return cached

# Call LLM
response = llm(prompt, model=model)

# Cache result
cache.set(prompt, model, response)

return response

Model Selection MODEL_PRICING = { "gpt-4": {"input": 0.03, "output": 0.06}, "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015}, "claude-3-opus": {"input": 0.015, "output": 0.075}, "claude-3-sonnet": {"input": 0.003, "output": 0.015}, }

def select_model_by_complexity(query: str) -> str: """Use cheaper models for simple queries""" # Classify complexity complexity = classify_complexity(query)

if complexity == "simple":
    return "gpt-3.5-turbo"  # 60x cheaper
elif complexity == "medium":
    return "claude-3-sonnet"
else:
    return "gpt-4"

def classify_complexity(query: str) -> str: # Simple heuristics if len(query) < 100 and "?" in query: return "simple" elif any(word in query.lower() for word in ["analyze", "complex", "detailed"]): return "complex" return "medium"

Prompt Optimization def optimize_prompt(prompt: str) -> str: """Reduce token count while preserving meaning""" optimizations = [ # Remove extra whitespace lambda p: re.sub(r'\s+', ' ', p),

    # Remove examples if not critical
    lambda p: p.split("Examples:")[0] if "Examples:" in p else p,

    # Use abbreviations
    lambda p: p.replace("For example", "E.g."),
]

for optimize in optimizations:
    prompt = optimize(prompt)

return prompt.strip()

Example: 500 tokens → 350 tokens = 30% cost reduction

Batching async def batch_llm_calls(prompts: List[str], batch_size: int = 5): """Process multiple prompts in parallel""" results = []

for i in range(0, len(prompts), batch_size):
    batch = prompts[i:i + batch_size]

    # Parallel execution
    batch_results = await asyncio.gather(*[
        llm_async(prompt) for prompt in batch
    ])

    results.extend(batch_results)

return results

10 sequential calls: ~30 seconds

10 batched calls (5 parallel): ~6 seconds

Latency Hotspot Analysis import time

class LatencyTracker: def init(self): self.timings = {}

def track(self, operation: str):
    def decorator(func):
        def wrapper(*args, **kwargs):
            start = time.time()
            result = func(*args, **kwargs)
            duration = time.time() - start

            if operation not in self.timings:
                self.timings[operation] = []
            self.timings[operation].append(duration)

            return result
        return wrapper
    return decorator

def report(self):
    return {
        op: {
            "count": len(times),
            "total": sum(times),
            "avg": sum(times) / len(times),
            "p95": sorted(times)[int(len(times) * 0.95)]
        }
        for op, times in self.timings.items()
    }

Usage

tracker = LatencyTracker()

@tracker.track("llm_call") def call_llm(prompt): return llm(prompt)

After 100 calls

print(tracker.report())

{"llm_call": {"avg": 2.3, "p95": 4.1, ...}}

Optimization Recommendations def generate_recommendations(cost_analysis, latency_analysis): recs = []

# High LLM costs
if cost_analysis["costs"]["llm_calls"] > 10:
    recs.append({
        "issue": "High LLM costs",
        "recommendation": "Implement caching for repeated queries",
        "impact": "50-80% cost reduction",
    })

    if cost_analysis["avg_cost_per_call"] > 0.01:
        recs.append({
            "issue": "Using expensive model for all queries",
            "recommendation": "Use gpt-3.5-turbo for simple queries",
            "impact": "60% cost reduction",
        })

# High latency
if latency_analysis["llm_call"]["avg"] > 3:
    recs.append({
        "issue": "High LLM latency",
        "recommendation": "Batch parallel calls, use streaming",
        "impact": "50% latency reduction",
    })

return recs

Streaming for Faster TTFB async def streaming_llm(prompt: str): """Stream tokens as they're generated""" async for chunk in llm_stream(prompt): yield chunk # User sees partial response immediately

Time to First Byte: ~200ms (streaming) vs ~2s (waiting for full response)

Best Practices Cache aggressively: Identical queries cached Model selection: Use cheaper models when possible Prompt optimization: Reduce unnecessary tokens Batching: Parallel execution for throughput Streaming: Faster perceived latency Monitor costs: Track per-user, per-feature Set budgets: Alert on anomalies Output Checklist Cost tracking implementation Caching layer Model selection logic Prompt optimization Batching for parallel calls Latency tracking Hotspot analysis Optimization recommendations Budget alerts Performance dashboard