Khi triển khai AI API vào production, điều tồi tệ nhất không phải là code lỗi — mà là system im lặng nhưng users không nhận được phản hồi. Sau 3 năm vận hành các hệ thống AI tại HolySheep AI, tôi đã xây dựng một bộ monitoring framework giúp phát hiện exception chỉ trong 30 giây thay vì 30 phút. Bài viết này sẽ chia sẻ toàn bộ config, kèm theo mã nguồn có thể chạy ngay và những lỗi thường gặp mà team tôi đã đau đầu giải quyết.
Tại sao Auto-Alert cho AI API lại quan trọng?
Khác với REST API truyền thống, AI API có những đặc thù riêng:
- Latency không đoán trước được: Một request có thể 200ms hoặc 30 giây tùy độ dài output
- Rate limit phức tạp: Không chỉ theo số request mà còn theo tokens
- Error format đa dạng: 429, 500, 502, 503 — mỗi error code lại cần xử lý khác nhau
- Chi phí leo thang nhanh: Một infinite loop với AI API có thể tiêu tốn $1000/giờ
HolySheheep AI cung cấp dashboard monitoring tích hợp với latency trung bình <50ms khi check status, giúp bạn không phải lo lắng về việc "mù thông tin" khi system gặp sự cố.
Kiến trúc Auto-Alert System
Trước khi đi vào code, hãy hiểu kiến trúc tổng thể:
┌─────────────────────────────────────────────────────────────────┐
│ AUTO-ALERT ARCHITECTURE │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────────┐ ┌────────────────────────┐ │
│ │ Client │───▶│ Proxy Layer │───▶│ HolySheep API Gateway │ │
│ │ (App) │ │ (Interceptor)│ │ api.holysheep.ai/v1 │ │
│ └──────────┘ └──────┬───────┘ └────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────┐ │
│ │ Alert Service │ │
│ │ - Prometheus │ │
│ │ - Grafana │ │
│ │ - Slack/Pager│ │
│ └───────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘Cấu hình với Python — Client-Side Monitoring
Đây là cách tôi triển khai monitoring cho tất cả production services. Code này đã được test thực tế với hơn 10 triệu requests mỗi ngày.
#!/usr/bin/env python3
"""
HolySheep AI Auto-Alert Configuration
Production-ready monitoring system với sub-second alert response
"""
import time
import json
import asyncio
import httpx
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from collections import defaultdict
from enum import Enum
============================================================================
CONFIGURATION — Thay đổi các giá trị này theo nhu cầu của bạn
============================================================================
HOLYSHEEEP_CONFIG = {
"base_url": "https://api.holysheep.ai/v1",
"api_key": "YOUR_HOLYSHEEP_API_KEY", # Thay bằng API key thật
"timeout": 30, # Timeout cho mỗi request (giây)
"max_retries": 3,
}
Alert thresholds — Tối ưu dựa trên kinh nghiệm vận hành thực tế
ALERT_THRESHOLDS = {
"latency_p99_ms": 5000, # Alert nếu P99 > 5 giây
"latency_p95_ms": 3000, # Alert nếu P95 > 3 giây
"error_rate_percent": 5.0, # Alert nếu error rate > 5%
"rate_limit_per_minute": 60, # Alert khi sắp chạm rate limit
"cost_per_hour_usd": 100.0, # Alert nếu chi phí vượt $100/giờ
"queue_depth": 100, # Alert nếu queue > 100 requests
}
Alert channels
ALERT_CHANNELS = {
"slack_webhook": "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK",
"email_to": ["[email protected]"],
"pagerduty_key": "YOUR_PAGERDUTY_INTEGRATION_KEY",
}
============================================================================
DATA CLASSES
============================================================================
class AlertSeverity(Enum):
INFO = "info"
WARNING = "warning"
ERROR = "error"
CRITICAL = "critical"
@dataclass
class APIRequest:
request_id: str
timestamp: datetime
model: str
prompt_tokens: int
completion_tokens: int
latency_ms: float
status_code: int
error_message: Optional[str] = None
cost_usd: float = 0.0
@dataclass
class AlertEvent:
severity: AlertSeverity
title: str
description: str
metric_name: str
current_value: float
threshold_value: float
timestamp: datetime
metadata: Dict[str, Any] = field(default_factory=dict)
============================================================================
METRICS COLLECTOR
============================================================================
class MetricsCollector:
"""Thu thập và phân tích metrics từ HolySheep API calls"""
def __init__(self, window_minutes: int = 5):
self.window_minutes = window_minutes
self.requests: List[APIRequest] = []
self._metrics_cache = {}
self._last_cache_update = datetime.min
def add_request(self, request: APIRequest):
"""Thêm một request vào collection"""
self.requests.append(request)
self._cleanup_old_requests()
def _cleanup_old_requests(self):
"""Xóa requests cũ hơn window"""
cutoff = datetime.now() - timedelta(minutes=self.window_minutes)
self.requests = [r for r in self.requests if r.timestamp > cutoff]
def get_latency_stats(self) -> Dict[str, float]:
"""Tính toán latency statistics (P50, P95, P99)"""
if not self.requests:
return {"p50": 0, "p95": 0, "p99": 0, "avg": 0}
latencies = sorted([r.latency_ms for r in self.requests])
n = len(latencies)
return {
"p50": latencies[int(n * 0.50)] if n > 0 else 0,
"p95": latencies[int(n * 0.95)] if n > 0 else 0,
"p99": latencies[int(n * 0.99)] if n > 0 else 0,
"avg": sum(latencies) / n if n > 0 else 0,
"max": max(latencies) if latencies else 0,
}
def get_error_rate(self) -> float:
"""Tính error rate percentage"""
if not self.requests:
return 0.0
error_count = sum(1 for r in self.requests if r.status_code >= 400)
return (error_count / len(self.requests)) * 100
def get_cost_per_minute(self) -> float:
"""Tính chi phí trung bình mỗi phút"""
if not self.requests:
return 0.0
total_cost = sum(r.cost_usd for r in self.requests)
elapsed_minutes = (datetime.now() - self.requests[0].timestamp).total_seconds() / 60
if elapsed_minutes <= 0:
return 0.0
return total_cost / elapsed_minutes
def get_error_breakdown(self) -> Dict[int, int]:
"""Phân tích error theo status code"""
breakdown = defaultdict(int)
for r in self.requests:
if r.status_code >= 400:
breakdown[r.status_code] += 1
return dict(breakdown)
def get_model_usage(self) -> Dict[str, Dict[str, int]]:
"""Thống kê usage theo model"""
models = defaultdict(lambda: {"requests": 0, "tokens": 0})
for r in self.requests:
models[r.model]["requests"] += 1
models[r.model]["tokens"] += r.prompt_tokens + r.completion_tokens
return dict(models)
============================================================================
ALERT ENGINE
============================================================================
class AlertEngine:
"""Engine xử lý alert logic — Non-blocking, async-first"""
def __init__(self, thresholds: Dict[str, float], channels: Dict[str, str]):
self.thresholds = thresholds
self.channels = channels
self.alert_history: List[AlertEvent] = []
self.cooldown_period = timedelta(minutes=5) # Tránh alert spam
self._last_alerts: Dict[str, datetime] = {}
def should_alert(self, metric_name: str) -> bool:
"""Kiểm tra cooldown — tránh alert spam trong 5 phút"""
if metric_name not in self._last_alerts:
return True
time_since_last = datetime.now() - self._last_alerts[metric_name]
return time_since_last > self.cooldown_period
def record_alert(self, metric_name: str):
"""Ghi nhận alert đã được gửi"""
self._last_alerts[metric_name] = datetime.now()
async def check_and_alert(
self,
metrics: MetricsCollector
) -> List[AlertEvent]:
"""Kiểm tra tất cả thresholds và trigger alerts nếu cần"""
alerts = []
# 1. Check Latency P99
latency_stats = metrics.get_latency_stats()
if (latency_stats["p99"] > self.thresholds["latency_p99_ms"] and
self.should_alert("latency_p99")):
alert = AlertEvent(
severity=AlertSeverity.WARNING,
title=f"AI API Latency cao: P99 = {latency_stats['p99']:.0f}ms",
description=f"P95: {latency_stats['p95']:.0f}ms, Avg: {latency_stats['avg']:.0f}ms",
metric_name="latency_p99_ms",
current_value=latency_stats["p99"],
threshold_value=self.thresholds["latency_p99_ms"],
timestamp=datetime.now(),
)
alerts.append(alert)
self.record_alert("latency_p99")
# 2. Check Error Rate
error_rate = metrics.get_error_rate()
if (error_rate > self.thresholds["error_rate_percent"] and
self.should_alert("error_rate")):
error_breakdown = metrics.get_error_breakdown()
alert = AlertEvent(
severity=AlertSeverity.ERROR,
title=f"Error Rate cao: {error_rate:.1f}%",
description=f"Error breakdown: {json.dumps(error_breakdown)}",
metric_name="error_rate_percent",
current_value=error_rate,
threshold_value=self.thresholds["error_rate_percent"],
timestamp=datetime.now(),
metadata={"error_breakdown": error_breakdown},
)
alerts.append(alert)
self.record_alert("error_rate")
# 3. Check Cost Burn Rate
cost_rate = metrics.get_cost_per_minute()
projected_hourly_cost = cost_rate * 60
if (projected_hourly_cost > self.thresholds["cost_per_hour_usd"] and
self.should_alert("cost_burn")):
alert = AlertEvent(
severity=AlertSeverity.CRITICAL,
title=f"Cost Burn Rate cao: ${projected_hourly_cost:.2f}/giờ",
description=f"Current rate: ${cost_rate:.4f}/phút — Có thể có infinite loop!",
metric_name="cost_per_hour_usd",
current_value=projected_hourly_cost,
threshold_value=self.thresholds["cost_per_hour_usd"],
timestamp=datetime.now(),
)
alerts.append(alert)
self.record_alert("cost_burn")
# Send alerts
for alert in alerts:
await self._send_alert(alert)
self.alert_history.append(alert)
return alerts
async def _send_alert(self, alert: AlertEvent):
"""Gửi alert đến các channels — Slack primary, backup email"""
color_map = {
AlertSeverity.INFO: "#36a64f",
AlertSeverity.WARNING: "#ff9800",
AlertSeverity.ERROR: "#f44336",
AlertSeverity.CRITICAL: "#9c27b0",
}
payload = {
"text": f"🚨 [{alert.severity.value.upper()}] {alert.title}",
"attachments": [{
"color": color_map[alert.severity],
"fields": [
{"title": "Metric", "value": alert.metric_name, "short": True},
{"title": "Current", "value": f"{alert.current_value:.2f}", "short": True},
{"title": "Threshold", "value": f"{alert.threshold_value:.2f}", "short": True},
{"title": "Description", "value": alert.description, "short": False},
],
"footer": f"HolySheep AI Monitor | {alert.timestamp.isoformat()}",
}]
}
async with httpx.AsyncClient() as client:
try:
response = await client.post(
self.channels["slack_webhook"],
json=payload,
timeout=5.0,
)
response.raise_for_status()
print(f"✅ Alert sent: {alert.title}")
except Exception as e:
print(f"❌ Failed to send Slack alert: {e}")
============================================================================
HOLYSHEEP API CLIENT VỚI BUILT-IN MONITORING
============================================================================
class HolySheepAIMonitoredClient:
"""
HolySheep AI Client với tích hợp auto-alert
Production-ready, zero-dependency (chỉ cần httpx)
"""
# Pricing reference: GPT-4.1 $8/MTok, Claude Sonnet 4.5 $15/MTok,
# Gemini 2.5 Flash $2.50/MTok, DeepSeek V3.2 $0.42/MTok
PRICING = {
"gpt-4.1": 8.0, # $/million tokens
"claude-sonnet-4.5": 15.0,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42,
}
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
auto_alert: bool = True,
):
self.api_key = api_key
self.base_url = base_url
self.client = httpx.AsyncClient(
base_url=base_url,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
timeout=30.0,
)
# Monitoring setup
self.metrics = MetricsCollector(window_minutes=5)
self.alert_engine = AlertEngine(ALERT_THRESHOLDS, ALERT_CHANNELS) if auto_alert else None
self._request_counter = 0
def _calculate_cost(self, model: str, prompt_tokens: int, completion_tokens: int) -> float:
"""Tính chi phí dựa trên model pricing"""
price_per_million = self.PRICING.get(model, 8.0) # Default to GPT-4.1 price
total_tokens = prompt_tokens + completion_tokens
return (total_tokens / 1_000_000) * price_per_million
async def chat_completion(
self,
model: str,
messages: List[Dict],
temperature: float = 0.7,
max_tokens: int = 2048,
) -> Dict[str, Any]:
"""
Gọi HolySheep AI Chat Completion API với auto-monitoring
"""
self._request_counter += 1
request_id = f"req_{self._request_counter}_{int(time.time())}"
start_time = time.perf_counter()
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
}
try:
response = await self.client.post("/chat/completions", json=payload)
latency_ms = (time.perf_counter() - start_time) * 1000
if response.status_code == 200:
data = response.json()
usage = data.get("usage", {})
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
cost = self._calculate_cost(model, prompt_tokens, completion_tokens)
request = APIRequest(
request_id=request_id,
timestamp=datetime.now(),
model=model,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
latency_ms=latency_ms,
status_code=200,
cost_usd=cost,
)
self.metrics.add_request(request)
# Check alerts (async, non-blocking)
if self.alert_engine:
asyncio.create_task(
self.alert_engine.check_and_alert(self.metrics)
)
return {
"success": True,
"data": data,
"metrics": {
"latency_ms": latency_ms,
"cost_usd": cost,
"total_tokens": prompt_tokens + completion_tokens,
}
}
else:
# Handle error
error_data = response.json() if response.content else {}
request = APIRequest(
request_id=request_id,
timestamp=datetime.now(),
model=model,
prompt_tokens=0,
completion_tokens=0,
latency_ms=latency_ms,
status_code=response.status_code,
error_message=error_data.get("error", {}).get("message", "Unknown error"),
)
self.metrics.add_request(request)
return {
"success": False,
"error": error_data.get("error", {}),
"status_code": response.status_code,
}
except httpx.TimeoutException as e:
latency_ms = (time.perf_counter() - start_time) * 1000
request = APIRequest(
request_id=request_id,
timestamp=datetime.now(),
model=model,
prompt_tokens=0,
completion_tokens=0,
latency_ms=latency_ms,
status_code=408,
error_message=f"Timeout after {latency_ms:.0f}ms",
)
self.metrics.add_request(request)
return {
"success": False,
"error": {"message": "Request timeout", "code": "TIMEOUT"},
"status_code": 408,
}
def get_metrics_summary(self) -> Dict[str, Any]:
"""Lấy tóm tắt metrics hiện tại"""
return {
"total_requests": len(self.metrics.requests),
"latency_stats": self.metrics.get_latency_stats(),
"error_rate_percent": self.metrics.get_error_rate(),
"cost_per_minute": self.metrics.get_cost_per_minute(),
"projected_hourly_cost": self.metrics.get_cost_per_minute() * 60,
"error_breakdown": self.metrics.get_error_breakdown(),
"model_usage": self.metrics.get_model_usage(),
}
async def close(self):
"""Cleanup connections"""
await self.client.aclose()
============================================================================
USAGE EXAMPLE
============================================================================
async def main():
"""Ví dụ sử dụng HolySheep AI Monitored Client"""
client = HolySheepAIMonitoredClient(
api_key="YOUR_HOLYSHEEP_API_KEY", # Thay bằng API key thật
base_url="https://api.holysheep.ai/v1",
auto_alert=True,
)
try:
# Gọi API như bình thường — monitoring tự động chạy ngầm
response = await client.chat_completion(
model="gpt-4.1",
messages=[
{"role": "system", "content": "Bạn là trợ lý AI hữu ích."},
{"role": "user", "content": "Xin chào, hãy giới thiệu về HolySheep AI"},
],
temperature=0.7,
max_tokens=500,
)
if response["success"]:
print(f"✅ Response received in {response['metrics']['latency_ms']:.0f}ms")
print(f"💰 Cost: ${response['metrics']['cost_usd']:.6f}")
print(f"📊 Response: {response['data']['choices'][0]['message']['content'][:100]}...")
else:
print(f"❌ Error: {response['error']}")
# Check metrics summary
print("\n📈 Metrics Summary:")
summary = client.get_metrics_summary()
print(json.dumps(summary, indent=2, default=str))
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())Cấu hình Prometheus + Grafana Dashboard
Để có visualization chuyên nghiệp và alerting linh hoạt hơn, tôi khuyên dùng Prometheus exporter này:
#!/usr/bin/env python3
"""
HolySheep AI Prometheus Exporter
Expose metrics ở endpoint /metrics để Prometheus scrape
"""
from prometheus_client import (
Counter, Histogram, Gauge, Summary,
generate_latest, CONTENT_TYPE_LATEST, CollectorRegistry, REGISTRY
)
from flask import Flask, Response
import threading
import time
import httpx
from typing import Dict, List
============================================================================
PROMETHEUS METRICS DEFINITIONS
============================================================================
Request counters by model and status
REQUEST_COUNTER = Counter(
'holysheep_api_requests_total',
'Total API requests',
['model', 'status_code']
)
Latency histogram (buckets optimized for AI API latency)
LATENCY_HISTOGRAM = Histogram(
'holysheep_api_request_latency_seconds',
'Request latency in seconds',
['model'],
buckets=(0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0, 60.0, 120.0)
)
Token usage
PROMPT_TOKENS = Counter(
'holysheep_api_prompt_tokens_total',
'Total prompt tokens',
['model']
)
COMPLETION_TOKENS = Counter(
'holysheep_api_completion_tokens_total',
'Total completion tokens',
['model']
)
Cost tracking
COST_GAUGE = Gauge(
'holysheep_api_cost_usd',
'Total cost in USD (cumulative)',
['model']
)
Error tracking
ERROR_COUNTER = Counter(
'holysheep_api_errors_total',
'Total API errors',
['model', 'error_type']
)
Rate limit status
RATE_LIMIT_REMAINING = Gauge(
'holysheep_api_rate_limit_remaining',
'Remaining rate limit quota',
['model']
)
Active requests
ACTIVE_REQUESTS = Gauge(
'holysheep_api_active_requests',
'Number of currently active requests'
)
============================================================================
HOLYSHEEP API CALLER WITH METRICS
============================================================================
class HolySheepPrometheusExporter:
"""Wrapper để track tất cả metrics khi gọi HolySheep API"""
PRICING = {
"gpt-4.1": 8.0,
"claude-sonnet-4.5": 15.0,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42,
}
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.AsyncClient(
base_url=self.base_url,
headers={"Authorization": f"Bearer {api_key}"},
timeout=60.0,
)
self._lock = threading.Lock()
self._total_cost = {}
async def call_with_metrics(
self,
model: str,
messages: List[Dict],
max_tokens: int = 2048,
) -> Dict:
"""Gọi API và track metrics"""
ACTIVE_REQUESTS.inc()
start_time = time.perf_counter()
try:
response = await self.client.post(
"/chat/completions",
json={
"model": model,
"messages": messages,
"max_tokens": max_tokens,
}
)
latency = time.perf_counter() - start_time
status_code = str(response.status_code)
# Record metrics
REQUEST_COUNTER.labels(model=model, status_code=status_code).inc()
LATENCY_HISTOGRAM.labels(model=model).observe(latency)
if response.status_code == 200:
data = response.json()
usage = data.get("usage", {})
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
# Track tokens
PROMPT_TOKENS.labels(model=model).inc(prompt_tokens)
COMPLETION_TOKENS.labels(model=model).inc(completion_tokens)
# Track cost
price_per_million = self.PRICING.get(model, 8.0)
cost = ((prompt_tokens + completion_tokens) / 1_000_000) * price_per_million
with self._lock:
self._total_cost[model] = self._total_cost.get(model, 0) + cost
COST_GAUGE.labels(model=model).set(self._total_cost[model])
return {"success": True, "data": data, "cost": cost}
elif response.status_code == 429:
# Rate limit — track remaining from headers
error_data = response.json()
ERROR_COUNTER.labels(model=model, error_type="rate_limit").inc()
if "x-ratelimit-remaining" in response.headers:
remaining = int(response.headers["x-ratelimit-remaining"])
RATE_LIMIT_REMAINING.labels(model=model).set(remaining)
return {"success": False, "error": "Rate limited", "retry_after": response.headers.get("retry-after")}
else:
ERROR_COUNTER.labels(model=model, error_type="http_error").inc()
return {"success": False, "error": f"HTTP {response.status_code}"}
except httpx.TimeoutException:
ERROR_COUNTER.labels(model=model, error_type="timeout").inc()
return {"success": False, "error": "Timeout"}
except Exception as e:
ERROR_COUNTER.labels(model=model, error_type="exception").inc()
return {"success": False, "error": str(e)}
finally:
ACTIVE_REQUESTS.dec()
============================================================================
FLASK APP FOR /metrics ENDPOINT
============================================================================
app = Flask(__name__)
exporter = HolySheepPrometheusExporter("YOUR_HOLYSHEEP_API_KEY")
@app.route('/metrics')
def metrics():
"""Prometheus scrape endpoint"""
return Response(
generate_latest(REGISTRY),
mimetype=CONTENT_TYPE_LATEST
)
@app.route('/health')
def health():
"""Health check endpoint"""
return {"status": "healthy", "active_requests": ACTIVE_REQUESTS._value._value}
============================================================================
PROMETHEUS ALERT RULES (prometheus.yml)
============================================================================
ALERT_RULES = """
groups:
- name: holysheep_alerts
rules:
# Alert khi error rate > 5%
- alert: HolySheepHighErrorRate
expr: |
rate(holysheep_api_requests_total{status_code=~"5.."}[5m])
/ rate(holysheep_api_requests_total[5m]) > 0.05
for: 2m
labels:
severity: warning
annotations:
summary: "HolySheep API error rate cao"
description: "Error rate {{ $value | humanizePercentage }} trong 5 phút qua"
# Alert khi latency P99 > 10 giây
- alert: HolySheepHighLatency
expr: |
histogram_quantile(0.99,
rate(holysheep_api_request_latency_seconds_bucket[5m])
) > 10
for: 5m
labels:
severity: warning
annotations:
summary: "HolySheep API latency cao"
description: "P99 latency: {{ $value | humanizeDuration }}"
# Alert khi cost rate > $100/giờ
- alert: HolySheepHighCostRate
expr: |
holysheep_api_cost_usd / (time() - process_start_time_seconds) * 3600 > 100
for: 10m
labels:
severity: critical
annotations:
summary: "HolySheep AI cost burn rate CAO!"
description: "Dự đoán chi phí ${{ $value }}/giờ — Kiểm tra ngay!"
# Alert khi rate limit sắp hết
- alert: HolySheheepRateLimitLow
expr: holysheep_api_rate_limit_remaining < 10
for: 1m
labels:
severity: warning
annotations:
summary: "HolySheep rate limit sắp hết"
description: "Chỉ còn {{ $value }} requests có thể gửi"
# Alert khi timeout rate cao
- alert: HolySheepHighTimeoutRate
expr: |
rate(holysheep_api_errors_total{error_type="timeout"}[5m]) > 0.1
for: 3m
labels:
severity: error
annotations:
summary: "HolySheep API timeout rate cao"
description: "Có thể API đang quá tải hoặc network issue"
"""
============================================================================
GRAFANA DASHBOARD JSON
============================================================================
GRAFANA_DASHBOARD = {
"title": "HolySheep AI API Monitoring",
"panels": [
{
"title": "Request Rate (RPM)",
"type": "graph",
"targets": [
{
"expr": "rate(holysheep_api_requests_total[1m])",
"legendFormat": "{{model}} - {{status_code}}"
}
]
},
{
"title": "Latency P50/P95/P99",
"type": "graph",
"targets": [
{
"expr": "histogram_quantile(0.50, rate(holysheep_api_request_latency_seconds_bucket[5m]))",
"legendFormat": "P50"
},
{
"expr": "histogram_quantile(0.95, rate(holysheep_api_request_latency_seconds_bucket[5m]))",
"legendFormat": "P95"
},
{
"expr": "histogram_quantile(0.99, rate(holysheep_api_request_latency_seconds_bucket[5m]))",
"legendFormat": "P99"
}
]
},
{
"title": "Error Rate %",
"type": "gauge",
"targets": [
{
"expr": "rate(holysheep_api_requests_total{status_code=~'5..'}[5m]) / rate(holysheep_api_requests_total[5m]) * 100"
}
],
"fieldConfig": {
"defaults": {
"thresholds": {
"steps": [
{"color": "green", "value": None},
{"color": "yellow", "value": 2},
{"color": "red", "value": 5}
]
},
"unit": "percent"
}
}
},
{
"title": "Total Cost ($)",
"type": "stat",
"targets": [
{
"expr": "sum(holysheep_api_cost_usd)"
}
],
"fieldConfig": {
"defaults": {
"unit": "currencyUSD"
}
}
},
{
"title": "Token Usage",
"type": "graph",
"targets": [
{
"expr": "rate(holysheep_api_prompt_tokens_total[1m])",
"legendFormat": "Prompt"
},
{
"expr": "rate(holysheep_api_completion_tokens_total[1m])",
"legendFormat": "Completion"
}
]
}
]
}
if __name__ == "__main__":
print("Starting HolySheep Prometheus Exporter on :8000...")
print(f"Metrics endpoint: http://localhost:8000/metrics")
print(f"