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

1. Cache aggressively: Identical queries cached
2. Model selection: Use cheaper models when possible
3. Prompt optimization: Reduce unnecessary tokens
4. Batching: Parallel execution for throughput
5. Streaming: Faster perceived latency
6. Monitor costs: Track per-user, per-feature
7. 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