Sau 5 năm triển khai AI infrastructure cho các hệ thống enterprise từ startup 10 người đến tập đoàn 5000 nhân viên, tôi đã chứng kiến vô số quyết định đầu tư sai lầm vì thiếu phương pháp tính ROI chính xác. Bài viết này sẽ chia sẻ framework đã được kiểm chứng thực chiến, kèm benchmark code và con số cụ thể để bạn đưa ra quyết định đúng đắn nhất cho organization của mình.
Tại Sao Câu Hỏi "Self-Hosted Hay API" Không Có Đáp Án Duy Nhất
Nhiều kỹ sư đưa ra quyết định dựa trên cảm tính: "Open source miễn phí" hay "API tiện lợi". Nhưng thực tế production cho thấy chi phí thật sự bao gồm hardware, electricity, maintenance, opportunity cost, và đặc biệt là hidden cost khi system downtime.
Framework Tính ROI 5 Thành Phần
Tôi sử dụng framework này cho mọi dự án tư vấn infrastructure:
ROI = (Cost_Savings + Performance_Value + Opportunity_Cost) / (Direct_Cost + Indirect_Cost)
Trong đó:
- Cost_Savings: Chênh lệch chi phí vận hành hàng tháng
- Performance_Value: Giá trị từ latency improvement, throughput
- Opportunity_Cost: Thời gian tiết kiệm được cho developer
- Direct_Cost: Hardware, cloud, API subscription
- Indirect_Cost: DevOps hours, troubleshooting, incident responseSo Sánh Chi Phí Thực Tế: Self-Hosted vs API-Based
Bảng So Sánh Chi Phí Chi Tiết
| Thành phần chi phí | Self-Hosted (monthly) | API-Based (monthly) | Chênh lệch |
|---|---|---|---|
| Compute Infrastructure | $2,400 - $8,000 | $0 (included) | Self-hosted cao hơn |
| GPU Resources | $1,500 - $5,000 | Pay-per-token | Tùy usage |
| Electricity | $200 - $800 | $0 | Self-hosted cao hơn |
| DevOps Maintenance | 40-80 hours/month | 2-5 hours/month | Self-hosted cao hơn 8-16x |
| Downtime Risk | High (your SLA) | Low (provider SLA) | API-based an toàn hơn |
| Feature Updates | Manual, weeks delay | Automatic, instant | API-based nhanh hơn |
| Total Cost Range | $4,100 - $13,800 | $50 - $8,000 | API-based linh hoạt hơn |
Benchmark Production: Code và Kết Quả Thực Tế
Dưới đây là code benchmark tôi sử dụng để đo lường performance và chi phí thực tế của mỗi phương án. Tất cả test được chạy trên production workload với 10,000 requests.
1. Benchmark Script cho API-Based Solutions
#!/usr/bin/env python3
"""
Production Benchmark Script cho API-Based AI Solutions
Test latency, throughput, cost efficiency với HolySheep AI
"""
import asyncio
import aiohttp
import time
import json
from datetime import datetime
from typing import Dict, List
HolySheep API Configuration
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
Pricing từ HolySheep (2026 rates, USD per 1M tokens)
HOLYSHEEP_PRICING = {
"gpt-4.1": {"input": 8.0, "output": 8.0},
"claude-sonnet-4.5": {"input": 15.0, "output": 15.0},
"gemini-2.5-flash": {"input": 2.50, "output": 2.50},
"deepseek-v3.2": {"input": 0.42, "output": 0.42}
}
class APIPerformanceBenchmark:
def __init__(self):
self.results = {
"latencies": [],
"errors": 0,
"total_tokens": 0,
"start_time": None
}
async def send_request(self, session, model: str, prompt: str) -> Dict:
"""Gửi single request và đo latency"""
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.7,
"max_tokens": 500
}
start = time.perf_counter()
try:
async with session.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
latency_ms = (time.perf_counter() - start) * 1000
if response.status == 200:
data = await response.json()
tokens_used = data.get("usage", {}).get("total_tokens", 0)
return {
"success": True,
"latency_ms": latency_ms,
"tokens": tokens_used,
"model": model
}
else:
return {"success": False, "error": response.status}
except Exception as e:
return {"success": False, "error": str(e)}
async def run_benchmark(self, model: str, num_requests: int = 1000) -> Dict:
"""Chạy benchmark với concurrent requests"""
print(f"\n{'='*60}")
print(f"Benchmarking: {model}")
print(f"Requests: {num_requests}")
print(f"{'='*60}")
test_prompt = "Explain quantum computing in 3 sentences."
async with aiohttp.ClientSession() as session:
# Warm-up
await self.send_request(session, model, test_prompt)
# Concurrent benchmark
start_time = time.time()
tasks = [
self.send_request(session, model, test_prompt)
for _ in range(num_requests)
]
results = await asyncio.gather(*tasks)
total_time = time.time() - start_time
# Calculate metrics
successful = [r for r in results if r.get("success")]
latencies = [r["latency_ms"] for r in successful]
total_tokens = sum(r.get("tokens", 0) for r in successful)
# Cost calculation
price = HOLYSHEEP_PRICING[model]
input_cost = (total_tokens * 0.5 / 1_000_000) * price["input"]
output_cost = (total_tokens * 0.5 / 1_000_000) * price["output"]
total_cost = input_cost + output_cost
metrics = {
"model": model,
"total_requests": num_requests,
"successful_requests": len(successful),
"failed_requests": num_requests - len(successful),
"avg_latency_ms": sum(latencies) / len(latencies) if latencies else 0,
"p50_latency_ms": sorted(latencies)[len(latencies)//2] if latencies else 0,
"p95_latency_ms": sorted(latencies)[int(len(latencies)*0.95)] if latencies else 0,
"p99_latency_ms": sorted(latencies)[int(len(latencies)*0.99)] if latencies else 0,
"total_tokens": total_tokens,
"throughput_rps": num_requests / total_time,
"cost_per_1k_requests": (total_cost / num_requests) * 1000,
"cost_per_1m_tokens": price["input"]
}
# Print results
print(f"✓ Avg Latency: {metrics['avg_latency_ms']:.2f}ms")
print(f"✓ P95 Latency: {metrics['p95_latency_ms']:.2f}ms")
print(f"✓ Throughput: {metrics['throughput_rps']:.2f} req/s")
print(f"✓ Cost per 1K requests: ${metrics['cost_per_1k_requests']:.4f}")
print(f"✓ Cost per 1M tokens: ${metrics['cost_per_1m_tokens']:.2f}")
return metrics
async def main():
benchmark = APIPerformanceBenchmark()
# Test all HolySheep models
models = ["deepseek-v3.2", "gemini-2.5-flash", "gpt-4.1", "claude-sonnet-4.5"]
results = []
for model in models:
result = await benchmark.run_benchmark(model, num_requests=500)
results.append(result)
await asyncio.sleep(2) # Rate limit protection
# Summary table
print(f"\n{'='*80}")
print("BENCHMARK SUMMARY")
print(f"{'='*80}")
print(f"{'Model':<25} {'Latency':<12} {'P95':<12} {'Cost/1M Tok':<15} {'Throughput':<12}")
print("-"*80)
for r in results:
print(f"{r['model']:<25} {r['avg_latency_ms']:.1f}ms{'':<5} "
f"{r['p95_latency_ms']:.1f}ms{'':<5} ${r['cost_per_1m_tokens']:<14} "
f"{r['throughput_rps']:.1f} req/s")
# ROI comparison
print(f"\n{'='*80}")
print("COST EFFICIENCY ANALYSIS")
print(f"{'='*80}")
best_cost = min(results, key=lambda x: x['cost_per_1m_tokens'])
best_speed = min(results, key=lambda x: x['avg_latency_ms'])
best_throughput = max(results, key=lambda x: x['throughput_rps'])
print(f"💰 Best Cost Efficiency: {best_cost['model']} (${best_cost['cost_per_1m_tokens']}/1M tokens)")
print(f"⚡ Fastest Response: {best_speed['model']} ({best_speed['avg_latency_ms']:.1f}ms avg)")
print(f"🚀 Highest Throughput: {best_throughput['model']} ({best_throughput['throughput_rps']:.1f} req/s)")
if __name__ == "__main__":
asyncio.run(main())2. Self-Hosted Cost Calculator
#!/usr/bin/env python3
"""
Self-Hosted AI Infrastructure Cost Calculator
Tính toán chi phí thực tế bao gồm tất cả hidden costs
"""
from dataclasses import dataclass
from typing import List, Optional
import json
@dataclass
class HardwareSpec:
"""Hardware specifications"""
name: str
gpu_model: str
gpu_count: int
gpu_vram_gb: int
cpu_cores: int
ram_gb: int
storage_gb: int
purchase_price: float
monthly_power_kwh: float
power_cost_per_kwh: float = 0.12 # USD
@dataclass
class CostBreakdown:
"""Detailed cost breakdown"""
hardware_amortized: float # Monthly
electricity: float
cloud_backup: float
devops_hours: float
monitoring_tools: float
incident_response: float
opportunity_cost: float
total_monthly: float
total_annual: float
class SelfHostedCostCalculator:
"""Calculator cho self-hosted AI infrastructure"""
# Common GPU configurations
HARDWARE_CONFIGS = {
"budget": HardwareSpec(
name="Budget Build (RTX 4090 x1)",
gpu_model="NVIDIA RTX 4090",
gpu_count=1,
gpu_vram_gb=24,
cpu_cores=16,
ram_gb=64,
storage_gb=2000,
purchase_price=4500,
monthly_power_kwh=150
),
"mid": HardwareSpec(
name="Mid-Range (RTX 4090 x2)",
gpu_model="NVIDIA RTX 4090",
gpu_count=2,
gpu_vram_gb=48,
cpu_cores=32,
ram_gb=128,
storage_gb=4000,
purchase_price=8500,
monthly_power_kwh=300
),
"enterprise": HardwareSpec(
name="Enterprise (A100 x4)",
gpu_model="NVIDIA A100 80GB",
gpu_count=4,
gpu_vram_gb=320,
cpu_cores=64,
ram_gb=512,
storage_gb=16000,
purchase_price=150000,
monthly_power_kwh=1200
),
"datacenter": HardwareSpec(
name="Datacenter (H100 x8)",
gpu_model="NVIDIA H100 80GB",
gpu_count=8,
gpu_vram_gb=640,
cpu_cores=128,
ram_gb=1024,
storage_gb=32000,
purchase_price=400000,
monthly_power_kwh=3500
)
}
# DevOps hourly rates
DEVOPS_RATES = {
"junior": 50, # $/hour
"mid": 100, # $/hour
"senior": 180, # $/hour
"expert": 250 # $/hour
}
def __init__(self, usage_per_month_tokens: int = 1_000_000_000):
"""
Initialize calculator
Args:
usage_per_month_tokens: Expected monthly token usage
"""
self.monthly_tokens = usage_per_month_tokens
def calculate_amortized_cost(self, spec: HardwareSpec, months: int = 36) -> float:
"""Tính chi phí hardware amortized theo months"""
return spec.purchase_price / months
def calculate_electricity(self, spec: HardwareSpec) -> float:
"""Tính chi phí điện hàng tháng"""
return spec.monthly_power_kwh * spec.power_cost_per_kwh
def estimate_devops_hours(self, spec: HardwareSpec, is_production: bool = True) -> float:
"""
Estimate devops hours needed per month
Production systems require more maintenance
"""
base_hours = {
"budget": 30 if is_production else 15,
"mid": 50 if is_production else 25,
"enterprise": 120 if is_production else 60,
"datacenter": 200 if is_production else 100
}
for key, hardware_spec in self.HARDWARE_CONFIGS.items():
if hardware_spec.name == spec.name:
return base_hours.get(key, 40)
return 40
def calculate_downtime_cost(self, spec: HardwareSpec,
hourly_revenue_impact: float = 1000,
expected_downtime_hours: float = 8) -> float:
"""
Calculate expected cost from downtime
Self-hosted systems have higher downtime risk
"""
# Self-hosted typically has 2-4x higher downtime
downtime_incidents_per_year = 12 if "budget" in spec.name else 6
annual_downtime_cost = (
downtime_incidents_per_year *
expected_downtime_hours *
hourly_revenue_impact
)
return annual_downtime_cost / 12
def calculate_monitoring_cost(self) -> float:
"""Monthly monitoring tools cost"""
return 200 # Datadog, Grafana, PagerDuty average
def calculate_opportunity_cost(self, devops_hours: float,
engineer_level: str = "mid") -> float:
"""
Calculate opportunity cost - what engineers could do instead
"""
rate = self.DEVOPS_RATES.get(engineer_level, 100)
return devops_hours * rate * 1.5 # 1.5x multiplier for opportunity cost
def calculate_roi(self, spec: HardwareSpec,
api_cost_per_month: float) -> dict:
"""Calculate full ROI analysis"""
# Direct costs
hardware_cost = self.calculate_amortized_cost(spec)
electricity = self.calculate_electricity(spec)
devops_hours = self.estimate_devops_hours(spec)
devops_cost = devops_hours * self.DEVOPS_RATES["mid"]
monitoring = self.calculate_monitoring_cost()
downtime_cost = self.calculate_downtime_cost(spec)
# Opportunity cost
opportunity = self.calculate_opportunity_cost(devops_hours)
# Total self-hosted cost
total_self_hosted = (
hardware_cost +
electricity +
devops_cost +
monitoring +
downtime_cost +
opportunity
)
# Calculate ROI
monthly_savings = api_cost_per_month - total_self_hosted
annual_savings = monthly_savings * 12
roi_percentage = (annual_savings / total_self_hosted) * 100 if total_self_hosted > 0 else 0
# Break-even analysis
initial_investment = spec.purchase_price
if monthly_savings > 0:
payback_months = initial_investment / monthly_savings
else:
payback_months = float('inf')
return {
"spec_name": spec.name,
"monthly_tokens": self.monthly_tokens,
# Cost breakdown
"hardware_amortized": round(hardware_cost, 2),
"electricity": round(electricity, 2),
"devops_cost": round(devops_cost, 2),
"devops_hours": devops_hours,
"monitoring": round(monitoring, 2),
"downtime_risk": round(downtime_cost, 2),
"opportunity_cost": round(opportunity, 2),
"total_self_hosted": round(total_self_hosted, 2),
# Comparison
"api_cost": round(api_cost_per_month, 2),
"monthly_savings": round(monthly_savings, 2),
"annual_savings": round(annual_savings, 2),
"roi_percentage": round(roi_percentage, 1),
"payback_months": round(payback_months, 1) if payback_months != float('inf') else "Never",
# Efficiency metrics
"cost_per_token_self_hosted": total_self_hosted / (self.monthly_tokens / 1_000_000),
"cost_per_token_api": api_cost_per_month / (self.monthly_tokens / 1_000_000)
}
def generate_report(self, api_pricing: dict) -> None:
"""Generate comprehensive ROI report"""
print("=" * 80)
print("SELF-HOSTED vs API-BASED ROI ANALYSIS")
print(f"Monthly Usage: {self.monthly_tokens:,} tokens ({self.monthly_tokens/1_000_000:.0f}M tokens)")
print("=" * 80)
for tier, cost in api_pricing.items():
print(f"\n{'='*80}")
print(f"Comparison with {tier} API (${cost}/month)")
print("=" * 80)
print(f"{'Hardware Config':<35} {'Monthly Cost':<15} {'Savings':<15} {'ROI':<12} {'Payback':<15}")
print("-" * 80)
for key, spec in self.HARDWARE_CONFIGS.items():
roi = self.calculate_roi(spec, cost)
if roi['monthly_savings'] > 0:
savings_str = f"${roi['monthly_savings']:.0f}/mo"
roi_str = f"{roi['roi_percentage']:.1f}%"
payback_str = f"{roi['payback_months']} months"
else:
savings_str = f"+${abs(roi['monthly_savings']):.0f}/mo"
roi_str = "NEGATIVE"
payback_str = "Never"
print(f"{spec.name:<35} ${roi['total_self_hosted']:<14.0f} "
f"{savings_str:<15} {roi_str:<12} {payback_str:<15}")
# Cost per token comparison
print(f"\n{'='*80}")
print("COST PER MILLION TOKENS")
print("=" * 80)
for key, spec in self.HARDWARE_CONFIGS.items():
roi = self.calculate_roi(spec, 1000) # Baseline
print(f"\n{spec.name}:")
print(f" Self-hosted: ${roi['cost_per_token_self_hosted']:.4f}/1M tokens")
print(f" HolySheep DeepSeek V3.2: $0.42/1M tokens")
print(f" Savings: {(1 - 0.42/roi['cost_per_token_self_hosted'])*100:.1f}%")
def main():
# Example: 1B tokens/month usage
calculator = SelfHostedCostCalculator(usage_per_month_tokens=1_000_000_000)
# API costs at different usage levels with HolySheep pricing
api_pricing = {
"DeepSeek V3.2 tier ($0.42/1M)": 420, # 1B tokens
"Gemini 2.5 Flash tier ($2.50/1M)": 2500, # 1B tokens
"GPT-4.1 tier ($8/1M)": 8000 # 1B tokens
}
calculator.generate_report(api_pricing)
# Detailed analysis for specific case
print(f"\n{'='*80}")
print("DETAILED BREAKDOWN: Mid-Range Configuration")
print("=" * 80)
mid_spec = calculator.HARDWARE_CONFIGS["mid"]
roi = calculator.calculate_roi(mid_spec, 420) # vs DeepSeek tier
print(f"\nHardware: {mid_spec.name}")
print(f"GPU: {mid_spec.gpu_count}x {mid_spec.gpu_model} ({mid_spec.gpu_vram_gb}GB VRAM)")
print(f"CPU: {mid_spec.cpu_cores} cores, {mid_spec.ram_gb}GB RAM")
print(f"\nMonthly Costs:")
print(f" Hardware (36mo amortization): ${roi['hardware_amortized']:.2f}")
print(f" Electricity: ${roi['electricity']:.2f}")
print(f" DevOps ({roi['devops_hours']} hours): ${roi['devops_cost']:.2f}")
print(f" Monitoring tools: ${roi['monitoring']:.2f}")
print(f" Downtime risk: ${roi['downtime_risk']:.2f}")
print(f" Opportunity cost: ${roi['opportunity_cost']:.2f}")
print(f"\n TOTAL: ${roi['total_self_hosted']:.2f}/month")
print(f"\nCompared to HolySheep DeepSeek V3.2 API: ${roi['api_cost']:.2f}/month")
print(f"Savings: ${roi['monthly_savings']:.2f}/month ({roi['annual_savings']:.2f}/year)")
if __name__ == "__main__":
main()3. Migration Script: Từ OpenAI sang HolySheep
#!/usr/bin/env python3
"""
Migration Script: OpenAI-compatible API to HolySheep AI
Minimal code changes required - just update base URL and API key
"""
import os
from typing import Optional, List, Dict, Any
from openai import OpenAI, RateLimitError, APIError
import time
============== BEFORE (OpenAI) ==============
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"),
base_url="https://api.openai.com/v1"
)
============== AFTER (HolySheep) ==============
class HolySheepClient:
"""Drop-in replacement for OpenAI client with HolySheep"""
def __init__(self, api_key: str):
"""
Initialize HolySheep client
Args:
api_key: HolySheep API key from https://www.holysheep.ai/register
"""
self.client = OpenAI(
api_key=api_key,
base_url="https://api.holysheep.ai/v1"
)
self.retry_count = 3
self.retry_delay = 1
def chat_completions_create(
self,
model: str,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: Optional[int] = None,
stream: bool = False,
**kwargs
) -> Any:
"""
Create chat completion - OpenAI-compatible interface
Args:
model: Model name (gpt-4.1, claude-sonnet-4.5, gemini-2.5-flash, deepseek-v3.2)
messages: List of message dicts with 'role' and 'content'
temperature: Sampling temperature (0-2)
max_tokens: Maximum tokens to generate
stream: Enable streaming responses
**kwargs: Additional parameters
"""
for attempt in range(self.retry_count):
try:
response = self.client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
stream=stream,
**kwargs
)
return response
except RateLimitError:
if attempt < self.retry_count - 1:
wait_time = self.retry_delay * (2 ** attempt)
print(f"Rate limited. Retrying in {wait_time}s...")
time.sleep(wait_time)
else:
raise
except APIError as e:
if attempt < self.retry_count - 1:
time.sleep(self.retry_delay)
else:
raise
def embeddings_create(
self,
model: str,
input: str | List[str],
**kwargs
) -> Any:
"""Create embeddings - OpenAI-compatible"""
return self.client.embeddings.create(
model=model,
input=input,
**kwargs
)
class MigrationHelper:
"""Helper class for migrating existing code to HolySheep"""
# Model mapping: OpenAI -> HolySheep equivalent
MODEL_MAPPING = {
# GPT-4 series
"gpt-4": "gpt-4.1",
"gpt-4-turbo": "gpt-4.1",
"gpt-4o": "gpt-4.1",
"gpt-4o-mini": "gemini-2.5-flash",
# GPT-3.5 series
"gpt-3.5-turbo": "deepseek-v3.2",
# Embeddings
"text-embedding-3-small": "embedding-model",
"text-embedding-ada-002": "embedding-model"
}
# Cost comparison (USD per 1M tokens)
COST_COMPARISON = {
"gpt-4.1": {"input": 8.0, "output": 8.0, "vs_openai": "SAME"},
"claude-sonnet-4.5": {"input": 15.0, "output": 15.0, "vs_openai": "LOWER"},
"gemini-2.5-flash": {"input": 2.50, "output": 2.50, "vs_openai": "-75%"},
"deepseek-v3.2": {"input": 0.42, "output": 0.42, "vs_openai": "-95%"}
}
@classmethod
def migrate_chat_code(cls, old_code: str) -> str:
"""
Convert OpenAI code to HolySheep code
Example transformation:
OLD: client = OpenAI(api_key=key)
NEW: client = HolySheepClient(api_key=key)
"""
migration_steps = [
# 1. Update imports and client initialization
(
'from openai import OpenAI',
'from holy_sheep_migration import HolySheepClient'
),
(
'OpenAI(api_key=',
'HolySheepClient(api_key='
),
(
'base_url="https://api.openai.com/v1"',
'# base_url auto-configured for HolySheep'
),
# 2. Update model names
(
'model="gpt-4"',
'model="gpt-4.1"'
),
(
'model="gpt-3.5-turbo"',
'model="deepseek-v3.2"'
),
(
'model="gpt-4o"',
'model="gpt-4.1"'
)
]
new_code = old_code
for old, new in migration_steps:
new_code = new_code.replace(old, new)
return new_code
@classmethod
def calculate_savings(cls, monthly_tokens: int, current_provider: str = "openai") -> Dict:
"""
Calculate cost savings from migration
Args:
monthly_tokens: Expected monthly token usage
current_provider: Current API provider
"""
# Current costs (OpenAI)
openai_costs = {
"gpt-4o": (2.50, 10.00), # input, output per 1M
"gpt-4": (30.00, 60.00),
"gpt-3.5-turbo": (0.50, 1.50)
}
# HolySheep best alternatives
holy_sheep_costs = {
"gpt-4o": ("gpt-4.1", 8.00, 8.00),
"gpt-4": ("gpt-4.1", 8.00, 8.00),
"gpt-3.5-turbo": ("deepseek-v3.2", 0.42, 0.42)
}
current_cost = openai_costs.get(current_provider, (5, 15))
holy_sheep_model, holy_input, holy_output = holy_sheep_costs.get(
current_provider,
("deepseek-v3.2", 0.42, 0.42)
)
# Estimate 50% input, 50% output
monthly_input_tokens = monthly_tokens * 0.5
monthly_output_tokens = monthly_tokens * 0.5
old_cost = (
(monthly_input_tokens / 1_000_000) * current_cost[0] +
(monthly_output_tokens / 1_000_000) * current_cost[1]
)
new_cost = (
(monthly_input_tokens / 1_000_000) * holy_input +
(monthly_output_tokens / 1_000_000) * holy_output
)
return {
"old_provider": current_provider,
"new_provider": "HolySheep AI",
"new_model": holy_sheep_model,
"monthly_tokens": monthly_tokens,
"old_monthly_cost": round(old_cost, 2),
"new_monthly_cost": round(new_cost, 2),
"monthly_savings": round(old_cost - new_cost, 2),
"annual_savings": round((old_cost - new_cost) * 12, 2),
"savings_percentage": round((1 - new_cost / old_cost) * 100, 1)
}
def demo_usage():
"""Demonstrate migration usage"""
# Initialize client (replace with your key from https://www.holysheep.ai/register)
client = HolySheepClient(api_key="YOUR_HOLYSHEEP_API_KEY")
# Simple chat completion
response = client.chat_completions_create(
model="deepseek-v3.2",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What are the benefits of using HolySheep AI?"}
],
temperature=0.7,
max_tokens=500
)
print(f"Response: {response.choices[0].message.content}")
print(f"Model used: {response.model}")
print