The Problem
On Monday you saw the 3-prompt framework for LLM governance. Great for understanding the concepts. But here's reality: manually tracking model performance across 50 API calls per day takes 3+ hours. Your team spends $60K/year just copying metrics into spreadsheets. Miss one cost spike and you blow your quarterly budget. Compliance audits require 2 weeks of manual log aggregation. One engineer doing this full-time? That's $120K/year in labor costs alone. Plus the errors from manual data entry and the delayed insights that cost you real money.
See It Work
Watch the 3-step governance framework run automatically. This is what you'll build.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple, add reliability, then scale to production. Pick where you are.
When to Level Up
- Log calls to JSON files
- Calculate costs manually
- Basic anomaly detection
- Manual report generation
- PostgreSQL for persistence
- Automated email alerts
- Hourly cost summaries
- Error rate tracking
- Model comparison reports
- Real-time Prometheus metrics
- Grafana dashboards
- Redis for caching
- Materialized views
- Automated optimization reports
- Cost forecasting
- Multi-region deployment
- ML-based anomaly detection
- Custom alerting rules
- A/B testing framework
- Cost allocation by team
- Compliance reporting
- SLA monitoring
- Auto-scaling
Enterprise Strategy Gotchas
Real challenges you'll hit when automating LLM operations. Here's how to handle them.
Rate Limits Aren't Consistent
Implement adaptive rate limiting with exponential backoff. Track rate limit headers and adjust request frequency dynamically.
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
import anthropic
import openai
class RateLimitError(Exception):
pass
@retry(Token Counting Is Tricky
Use model-specific tokenizers and cache token counts. Don't estimate—measure.
import tiktoken
from anthropic import Anthropic
class TokenCounter:
def __init__(self):
self.gpt_encoder = tiktoken.encoding_for_model("gpt-4")
self.anthropic = Anthropic()
Costs Spike Without Warning
Implement cost circuit breakers that pause requests when thresholds are hit. Alert before you hit the limit, not after.
import asyncio
from datetime import datetime, timedelta
class CostCircuitBreaker:
def __init__(self, daily_limit: float = 100.0, hourly_limit: float = 10.0):
self.daily_limit = daily_limit
self.hourly_limit = hourly_limit
self.daily_spent = 0.0Latency Varies Wildly
Track P50, P95, P99 latencies. Alert on P95 spikes, not average increases.
import numpy as np
from collections import deque
from datetime import datetime, timedelta
class LatencyTracker:
def __init__(self, window_minutes: int = 60):
self.window_minutes = window_minutes
self.latencies = deque() # (timestamp, latency_ms)Model Responses Aren't Deterministic
Use temperature=0 for monitoring calls. Store prompt hashes and track response consistency over time.
import hashlib
import json
from collections import defaultdict
class ResponseConsistencyTracker:
def __init__(self):
self.prompt_responses = defaultdict(list) # prompt_hash -> [responses]
Adjust Your Numbers
❌ Manual Process
✅ AI-Automated
You Save
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