As AI-assisted development becomes standard practice, engineering teams face a critical challenge: token consumption monitoring and cost optimization. Whether you're running a solo startup or managing enterprise-scale development workflows, understanding how to track, analyze, and control your AI API spending directly impacts your bottom line.
In this hands-on guide, I walk you through building a comprehensive token monitoring system that integrates seamlessly with HolySheep AI — delivering rate parity of ¥1=$1 (saving 85%+ compared to ¥7.3 per dollar), sub-50ms latency, and payment flexibility through WeChat and Alipay.
Comparison: HolySheep AI vs Official API vs Relay Services
Before diving into implementation, let's establish the cost landscape for AI API consumption in 2026:
| Provider | Rate ($/M tokens output) | Setup Complexity | Payment Methods | Latency | Free Tier |
|---|---|---|---|---|---|
| HolySheep AI | $1.00 (¥1) | Low | WeChat, Alipay, Cards | <50ms | Free credits on signup |
| Official OpenAI | $7.30 | Medium | Credit Card Only | 60-120ms | $5 trial credit |
| Official Anthropic | $15.00 | Medium | Credit Card Only | 80-150ms | None |
| Standard Relay Service A | $4.50 | High | Wire Transfer | 100-200ms | None |
| Standard Relay Service B | $5.80 | Medium | Credit Card | 90-180ms | $2 trial |
The math is compelling: using HolySheep AI at $1.00 per million tokens represents an 86% cost reduction compared to official OpenAI pricing and a 93% reduction versus Anthropic's Claude Sonnet 4.5 at $15/M tokens. For development teams running hundreds of thousands of tokens daily, this translates to thousands of dollars in monthly savings.
Understanding Token Consumption Patterns
Before implementing monitoring, you need to understand where tokens actually go in AI-assisted development:
- Code Generation: Autocomplete suggestions and code block generation
- Code Review: Analysis of existing codebases for bugs and improvements
- Context Loading: Reading and embedding your codebase context
- Debugging Sessions: Error analysis and fix suggestions
- Documentation: Auto-generating README files and API docs
Building a Token Monitoring System
I implemented this exact monitoring infrastructure for my team's development workflow, and the insights transformed how we approach AI-assisted coding. Here's the complete implementation:
Step 1: Token Tracking Middleware
"""
Token Consumption Monitor for AI API Calls
Integrates with HolySheep AI for cost-effective monitoring
"""
import time
import json
import sqlite3
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from contextlib import contextmanager
@dataclass
class TokenRecord:
"""Individual token consumption record"""
timestamp: str
model: str
input_tokens: int
output_tokens: int
total_cost: float # in USD
request_id: str
endpoint: str
response_time_ms: float
status: str
class TokenMonitor:
"""
Comprehensive token consumption monitoring system.
Stores data locally and provides analytics capabilities.
"""
# HolySheep AI 2026 Pricing (USD per million tokens)
HOLYSHEEP_PRICING = {
"gpt-4.1": {"input": 2.00, "output": 8.00},
"gpt-4.1-mini": {"input": 0.30, "output": 1.20},
"claude-sonnet-4.5": {"input": 3.00, "output": 15.00},
"claude-haiku-3.5": {"input": 0.80, "output": 4.00},
"gemini-2.5-flash": {"input": 0.10, "output": 2.50},
"gemini-2.5-pro": {"input": 1.00, "output": 5.00},
"deepseek-v3.2": {"input": 0.10, "output": 0.42},
}
def __init__(self, db_path: str = "token_monitor.db"):
self.db_path = db_path
self._init_database()
def _init_database(self):
"""Initialize SQLite database for token tracking"""
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS token_records (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT NOT NULL,
model TEXT NOT NULL,
input_tokens INTEGER,
output_tokens INTEGER,
total_cost REAL,
request_id TEXT UNIQUE,
endpoint TEXT,
response_time_ms REAL,
status TEXT
)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_timestamp ON token_records(timestamp)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_model ON token_records(model)
""")
conn.commit()
conn.close()
def calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Calculate cost in USD using HolySheep pricing"""
pricing = self.HOLYSHEEP_PRICING.get(model, {"input": 2.00, "output": 8.00})
input_cost = (input_tokens / 1_000_000) * pricing["input"]
output_cost = (output_tokens / 1_000_000) * pricing["output"]
return round(input_cost + output_cost, 6)
def record_usage(
self,
model: str,
input_tokens: int,
output_tokens: int,
request_id: str,
endpoint: str = "chat/completions",
response_time_ms: float = 0.0,
status: str = "success"
):
"""Record a token consumption event"""
cost = self.calculate_cost(model, input_tokens, output_tokens)
record = TokenRecord(
timestamp=datetime.utcnow().isoformat(),
model=model,
input_tokens=input_tokens,
output_tokens=output_tokens,
total_cost=cost,
request_id=request_id,
endpoint=endpoint,
response_time_ms=response_time_ms,
status=status
)
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute("""
INSERT OR REPLACE INTO token_records
(timestamp, model, input_tokens, output_tokens, total_cost,
request_id, endpoint, response_time_ms, status)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
""", (
record.timestamp, record.model, record.input_tokens,
record.output_tokens, record.total_cost, record.request_id,
record.endpoint, record.response_time_ms, record.status
))
conn.commit()
conn.close()
return record
def get_daily_summary(self, days: int = 30) -> Dict:
"""Get daily consumption summary"""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
start_date = (datetime.utcnow() - timedelta(days=days)).isoformat()
cursor.execute("""
SELECT
DATE(timestamp) as date,
COUNT(*) as request_count,
SUM(input_tokens) as total_input,
SUM(output_tokens) as total_output,
SUM(total_cost) as total_cost
FROM token_records
WHERE timestamp >= ? AND status = 'success'
GROUP BY DATE(timestamp)
ORDER BY date DESC
""", (start_date,))
results = [dict(row) for row in cursor.fetchall()]
conn.close()
return results
def get_model_breakdown(self, days: int = 30) -> Dict:
"""Get consumption breakdown by model"""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
start_date = (datetime.utcnow() - timedelta(days=days)).isoformat()
cursor.execute("""
SELECT
model,
COUNT(*) as request_count,
SUM(input_tokens) as total_input,
SUM(output_tokens) as total_output,
SUM(total_cost) as total_cost,
AVG(response_time_ms) as avg_latency
FROM token_records
WHERE timestamp >= ? AND status = 'success'
GROUP BY model
ORDER BY total_cost DESC
""", (start_date,))
results = [dict(row) for row in cursor.fetchall()]
conn.close()
return results
Usage Example
monitor = TokenMonitor("/path/to/your/database.db")
print("Token Monitor initialized successfully!")
print(f"Available models: {list(monitor.HOLYSHEEP_PRICING.keys())}")Step 2: HolySheep AI Integration Client
"""
HolySheep AI Integration Client with Automatic Token Tracking
base_url: https://api.holysheep.ai/v1
"""
import os
import json
import time
import uuid
import httpx
from typing import Dict, List, Optional, Any, Union
from openai import OpenAI, APIResponse
from openai.types.chat import ChatCompletion
class HolySheepAIClient:
"""
Production-ready client for HolySheep AI with built-in token monitoring.
Never uses api.openai.com or api.anthropic.com - all requests go through
the cost-effective HolySheep infrastructure.
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(
self,
api_key: str,
monitor: 'TokenMonitor',
organization: Optional[str] = None,
timeout: float = 60.0
):
"""
Initialize HolySheep AI client with monitoring.
Args:
api_key: Your HolySheep API key (get one at https://www.holysheep.ai/register)
monitor: TokenMonitor instance for tracking consumption
organization: Optional organization ID
timeout: Request timeout in seconds
"""
if not api_key or api_key == "YOUR_HOLYSHEEP_API_KEY":
raise ValueError(
"Invalid API key. Please set your HolySheep API key. "
"Sign up at https://www.holysheep.ai/register"
)
self.monitor = monitor
self.client = OpenAI(
api_key=api_key,
base_url=self.BASE_URL,
organization=organization,
timeout=httpx.Timeout(timeout, connect=10.0)
)
# Rate limiting configuration
self._rate_limit = {"requests_per_minute": 60, "tokens_per_minute": 150_000}
self._request_timestamps = []
def chat_completion(
self,
model: str,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: Optional[int] = None,
top_p: float = 1.0,
stream: bool = False,
**kwargs
) -> Union[ChatCompletion, Iterator[ChatCompletion]]:
"""
Send a chat completion request with automatic token tracking.
Supported models:
- gpt-4.1: $2.00/M input, $8.00/M output
- gpt-4.1-mini: $0.30/M input, $1.20/M output
- claude-sonnet-4.5: $3.00/M input, $15.00/M output
- gemini-2.5-flash: $0.10/M input, $2.50/M output
- deepseek-v3.2: $0.10/M input, $0.42/M output
"""
request_id = str(uuid.uuid4())
start_time = time.time()
try:
response = self.client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stream=stream,
**kwargs
)
if not stream:
# Extract token usage from non-streaming response
usage = response.usage
input_tokens = usage.prompt_tokens
output_tokens = usage.completion_tokens
response_time = (time.time() - start_time) * 1000
# Record usage in monitor
self.monitor.record_usage(
model=model,
input_tokens=input_tokens,
output_tokens=output_tokens,
request_id=request_id,
response_time_ms=response_time,
status="success"
)
return response
else:
# For streaming, we'll track after completion
# This is a simplified version - production would use generators
return response
except Exception as e:
# Record failed request
self.monitor.record_usage(
model=model,
input_tokens=0,
output_tokens=0,
request_id=request_id,
response_time_ms=(time.time() - start_time) * 1000,
status=f"error: {str(e)}"
)
raise
def code_completion(
self,
prompt: str,
model: str = "gpt-4.1-mini",
max_tokens: int = 500
) -> str:
"""
Specialized code completion with optimized settings.
Uses mini model for cost efficiency on straightforward tasks.
"""
messages = [
{"role": "system", "content": "You are an expert programmer. Generate clean, efficient code."},
{"role": "user", "content": prompt}
]
response = self.chat_completion(
model=model,
messages=messages,
temperature=0.3, # Lower temperature for code
max_tokens=max_tokens
)
return response.choices[0].message.content
def batch_process(
self,
prompts: List[str],
model: str = "gpt-4.1-mini",
delay: float = 0.5
) -> List[str]:
"""
Process multiple prompts with rate limiting.
Includes automatic delays to prevent throttling.
"""
results = []
for i, prompt in enumerate(prompts):
try:
result = self.code_completion(prompt, model=model)
results.append(result)
# Rate limiting delay
if i < len(prompts) - 1:
time.sleep(delay)
except Exception as e:
print(f"Error processing prompt {i}: {e}")
results.append("")
return results
Example Usage
if __name__ == "__main__":
# Initialize monitor
monitor = TokenMonitor("/data/token_monitor.db")
# Initialize client with your HolySheep API key
client = HolySheepAIClient(
api_key="YOUR_HOLYSHEEP_API_KEY", # Replace with your key
monitor=monitor
)
# Example: Generate code with cost tracking
response = client.chat_completion(
model="deepseek-v3.2", # Most cost-effective at $0.42/M output
messages=[
{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
],
max_tokens=200
)
print(f"Generated code: {response.choices[0].message.content}")
print(f"Total cost recorded: ${monitor.calculate_cost('deepseek-v3.2', response.usage.prompt_tokens, response.usage.completion_tokens):.6f}")Step 3: Real-Time Dashboard and Alerting
"""
Real-time Token Consumption Dashboard and Cost Alerting System
Generates reports and sends alerts when spending exceeds thresholds
"""
import smtplib
import sqlite3
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
class CostAlertSystem:
"""
Intelligent alerting system for token consumption monitoring.
Triggers notifications when spending approaches or exceeds budget limits.
"""
def __init__(
self,
monitor: 'TokenMonitor',
daily_budget_usd: float = 50.0,
monthly_budget_usd: float = 500.0
):
self.monitor = monitor
self.daily_budget = daily_budget_usd
self.monthly_budget = monthly_budget_usd
# Alert thresholds (percentage of budget)
self.warning_threshold = 0.75 # 75%
self.critical_threshold = 0.90 # 90%
self.exceeded_threshold = 1.00 # 100%
def get_current_spending(self) -> Dict:
"""Calculate current daily and monthly spending"""
today = datetime.utcnow().date()
month_start = today.replace(day=1)
conn = sqlite3.connect(self.monitor.db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
# Daily spending
cursor.execute("""
SELECT COALESCE(SUM(total_cost), 0) as daily_spending
FROM token_records
WHERE DATE(timestamp) = DATE(?) AND status = 'success'
""", (today.isoformat(),))
daily = dict(cursor.fetchone())
# Monthly spending
cursor.execute("""
SELECT COALESCE(SUM(total_cost), 0) as monthly_spending
FROM token_records
WHERE DATE(timestamp) >= DATE(?) AND status = 'success'
""", (month_start.isoformat(),))
monthly = dict(cursor.fetchone())
conn.close()
return {
"daily_spending": daily["daily_spending"],
"daily_budget": self.daily_budget,
"daily_percentage": (daily["daily_spending"] / self.daily_budget) * 100,
"monthly_spending": monthly["monthly_spending"],
"monthly_budget": self.monthly_budget,
"monthly_percentage": (monthly["monthly_spending"] / self.monthly_budget) * 100
}
def check_alerts(self) -> List[Dict]:
"""Check current spending against budgets and generate alerts"""
spending = self.get_current_spending()
alerts = []
# Daily alerts
if spending["daily_percentage"] >= self.exceeded_threshold * 100:
alerts.append({
"level": "critical",
"scope": "daily",
"message": f"DAILY BUDGET EXCEEDED: ${spending['daily_spending']:.2f} spent (${spending['daily_budget']:.2f} budget)",
"action_required": True
})
elif spending["daily_percentage"] >= self.critical_threshold * 100:
alerts.append({
"level": "warning",
"scope": "daily",
"message": f"Daily spending at {spending['daily_percentage']:.1f}%: ${spending['daily_spending']:.2f}/${spending['daily_budget']:.2f}",
"action_required": True
})
elif spending["daily_percentage"] >= self.warning_threshold * 100:
alerts.append({
"level": "info",
"scope": "daily",
"message": f"Daily spending approaching limit: {spending['daily_percentage']:.1f}%",
"action_required": False
})
# Monthly alerts
if spending["monthly_percentage"] >= self.exceeded_threshold * 100:
alerts.append({
"level": "critical",
"scope": "monthly",
"message": f"MONTHLY BUDGET EXCEEDED: ${spending['monthly_spending']:.2f} spent",
"action_required": True
})
return alerts
def generate_spending_report(self, days: int = 7) -> str:
"""Generate HTML spending report"""
spending = self.get_current_spending()
daily_summary = self.monitor.get_daily_summary(days)
model_breakdown = self.monitor.get_model_breakdown(days)
alerts = self.check_alerts()
html = f"""
Token Consumption Report
Generated: {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S UTC')}
Budget Status
Daily Spending
${spending['daily_spending']:.2f} / ${spending['daily_budget']:.2f}
{spending['daily_percentage']:.1f}%
Monthly Spending
${spending['monthly_spending']:.2f} / ${spending['monthly_budget']:.2f}
{spending['monthly_percentage']:.1f}%
Alerts
{"".join(f'- {a["message"]}
' for a in alerts)}
Model Breakdown (Last {days} Days)
Model
Requests
Input Tokens
Output Tokens
Cost
Avg Latency
{"".join(f'''
{m['model']}
{m['request_count']:,}
{m['total_input']:,}
{m['total_output']:,}
${m['total_cost']:.2f}
{m['avg_latency']:.1f}ms
''' for m in model_breakdown)}
"""
return html
def send_alert_email(
self,
alerts: List[Dict],
smtp_server: str,
smtp_port: int,
sender_email: str,
sender_password: str,
recipient_email: str
):
"""Send alert email via SMTP"""
msg = MIMEMultipart('alternative')
msg['Subject'] = f"[HolySheep AI] Cost Alert: {len(alerts)} issue(s) detected"
msg['From'] = sender_email
msg['To'] = recipient_email
# Create text and HTML versions
text_content = "\n".join(f"- {a['level'].upper()}: {a['message']}" for a in alerts)
html_content = f"""
HolySheep AI Cost Alerts
The following spending alerts have been triggered:
{"".join(f'- {a["level"].upper()}: {a["message"]}
' for a in alerts)}
Monitor your consumption at: HolySheep AI Dashboard
"""
msg.attach(MIMEText(text_content, 'plain'))
msg.attach(MIMEText(html_content, 'html'))
with smtplib.SMTP_SSL(smtp_server, smtp_port) as server:
server.login(sender_email, sender_password)
server.sendmail(sender_email, recipient_email, msg.as_string())
Example Usage
if __name__ == "__main__":
monitor = TokenMonitor("/data/token_monitor.db")
alert_system = CostAlertSystem(
monitor,
daily_budget_usd=50.0,
monthly_budget_usd=500.0
)
# Check for alerts
alerts = alert_system.check_alerts()
for alert in alerts:
print(f"[{alert['level'].upper()}] {alert['message']}")
# Generate report
report = alert_system.generate_spending_report(days=7)
with open("/data/spending_report.html", "w") as f:
f.write(report)
print("Report generated: /data/spending_report.html")Cost Optimization Strategies
After monitoring my team's token consumption for three months, I identified several patterns that dramatically reduced our costs:
Strategy 1: Model Selection Based on Task Complexity
"""
Intelligent Model Router - Selects optimal model based on task complexity
Balances cost efficiency with output quality requirements
"""
from enum import Enum
from typing import List, Dict, Callable
class TaskComplexity(Enum):
SIMPLE = "simple" # Formatting, simple transformations
MODERATE = "moderate" # Code completion, documentation
COMPLEX = "complex" # Architecture decisions, debugging
ADVANCED = "advanced" # Novel solutions, complex algorithms
class ModelRouter:
"""
Routes requests to optimal models based on task analysis.
Saves costs by reserving expensive models for complex tasks only.
"""
# Model configurations with pricing (per million tokens output)
MODELS = {
"deepseek-v3.2": {
"input_cost": 0.10,
"output_cost": 0.42,
"latency_ms": 45,
"capabilities": ["code", "reasoning", "math", "multilingual"]
},
"gpt-4.1-mini": {
"input_cost": 0.30,
"output_cost": 1.20,
"latency_ms": 35,
"capabilities": ["code", "reasoning", "creativity"]
},
"gemini-2.5-flash": {
"input_cost": 0.10,
"output_cost": 2.50,
"latency_ms": 30,
"capabilities": ["code", "reasoning", "multimodal", "fast"]
},
"gpt-4.1": {
"input_cost": 2.00,
"output_cost": 8.00,
"latency_ms": 60,
"capabilities": ["code", "reasoning", "analysis", "creativity", "complex"]
},
"claude-sonnet-4.5": {
"input_cost": 3.00,
"output_cost": 15.00,
"latency_ms": 70,
"capabilities": ["code", "reasoning", "analysis", "writing", "long_context"]
}
}
# Complexity-based routing
ROUTING_TABLE = {
TaskComplexity.SIMPLE: ["deepseek-v3.2", "gpt-4.1-mini"],
TaskComplexity.MODERATE: ["gemini-2.5-flash", "deepseek-v3.2", "gpt-4.1-mini"],
TaskComplexity.COMPLEX: ["gpt-4.1", "gemini-2.5-flash"],
TaskComplexity.ADVANCED: ["gpt-4.1", "claude-sonnet-4.5"]
}
def estimate_complexity(self, prompt: str, context_lines: int = 0) -> TaskComplexity:
"""
Estimate task complexity from prompt analysis.
Uses heuristics based on keywords and context length.
"""
prompt_lower = prompt.lower()
# Simple task indicators
simple_keywords = ["format", "convert", "translate", "simple", "quick"]
if any(kw in prompt_lower for kw in simple_keywords) and context_lines < 20:
return TaskComplexity.SIMPLE
# Complex task indicators
complex_keywords = [
"architecture", "design pattern", "optimize", "refactor",
"debug", "complex", "performance", "security", "explain why"
]
if any(kw in prompt_lower for kw in complex_keywords) or context_lines > 100:
return TaskComplexity.COMPLEX
# Advanced task indicators
advanced_keywords = [
"invent", "create new", "novel approach", "research",
"theoretical", "algorithm from scratch", "prove"
]
if any(kw in prompt_lower for kw in advanced_keywords):
return TaskComplexity.ADVANCED
return TaskComplexity.MODERATE
def select_model(
self,
prompt: str,
context_lines: int = 0,
prefer_speed: bool = False,
prefer_quality: bool = False
) -> str:
"""
Select optimal model based on task analysis.
Args:
prompt: User's request text
context_lines: Lines of code context provided
prefer_speed: Prioritize low latency
prefer_quality: Prioritize output quality over cost
Returns:
Optimal model name for the task
"""
complexity = self.estimate_complexity(prompt, context_lines)
candidates = self.ROUTING_TABLE[complexity]
if prefer_quality:
# Use most capable (expensive) model
return candidates[-1]
if prefer_speed:
# Sort by latency
sorted_candidates = sorted(
candidates,
key=lambda m: self.MODELS[m]["latency_ms"]
)
return sorted_candidates[0]
# Default: balance cost and capability
# Prefer cheaper models that can handle the task
return candidates[0]
def calculate_potential_savings(
self,
daily_requests: int,
current_model: str,
prompt_sample: List[str]
) -> Dict:
"""
Calculate potential cost savings from optimized routing.
"""
current_cost_per_request = self._estimate_request_cost(
current_model,
avg_input=500,
avg_output=200
)
optimized_costs = []
for prompt in prompt_sample:
model = self.select_model(prompt)
cost = self._estimate_request_cost(model, avg_input=500, avg_output=200)
optimized_costs.append(cost)
avg_optimized_cost = sum(optimized_costs) / len(optimized_costs)
current_daily_cost = daily_requests * current_cost_per_request
optimized_daily_cost = daily_requests * avg_optimized_cost
return {
"current_model": current_model,
"current_daily_cost": current_daily_cost,
"optimized_daily_cost": optimized_daily_cost,
"monthly_savings": (current_daily_cost - optimized_daily_cost) * 30,
"annual_savings": (current_daily_cost - optimized_daily_cost) * 365
}
def _estimate_request_cost(
self,
model: str,
avg_input: int,
avg_output: int
) -> float:
"""Estimate cost per request in USD"""
model_config = self.MODELS.get(model, self.MODELS["gpt-4.1"])
input_cost = (avg_input / 1_000_000) * model_config["input_cost"]
output_cost = (avg_output / 1_000_000) * model_config["output_cost"]
return input_cost + output_cost
Example Usage
router = ModelRouter()
Test cases
test_cases = [
("Format this JSON data", 5),
("Refactor this function to use a design pattern", 50),
("Debug why my API returns 500 errors", 200),
("Create a novel sorting algorithm for specific data types", 30),
]
for prompt, context in test_cases:
complexity = router.estimate_complexity(prompt, context)
selected = router.select_model(prompt, context)
model_info = router.MODELS[selected]
print(f"""
Task: {prompt[:50]}...
Complexity: {complexity.value}
Selected Model: {selected}
Output Cost: ${model_info['output_cost']}/M tokens
Estimated Latency: {model_info['latency_ms']}ms
""")
Calculate potential savings
savings = router.calculate_potential_savings(
daily_requests=500,
current_model="gpt-4.1",
prompt_sample=[tc[0] for tc in test_cases] * 100
)
print(f"""
=== POTENTIAL SAVINGS ANALYSIS ===
Current daily cost (GPT-4.1): ${savings['current_daily_cost']:.2f}
Optimized daily cost: ${savings['optimized_daily_cost']:.2f}
Monthly savings: ${savings['monthly_savings']:.2f}
Annual savings: ${savings['annual_savings']:.2f}
""")Strategy 2: Context Window Optimization
"""
Context Compression and Optimization for Token Efficiency
Reduces input token costs by intelligently managing context
"""
import re
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
@dataclass
class CompressionResult:
original_tokens: int
compressed_tokens: int
compression_ratio: float
preserved_content: Dict
class ContextOptimizer:
"""
Reduces token consumption through intelligent context management.
Implements multiple compression strategies for maximum efficiency