I remember the night our e-commerce platform nearly collapsed during a flash sale. Our AI customer service chatbot—powered by a RAG system running on top of the HolySheep AI API relay—was processing 15,000 requests per minute when latency spiked to 3.2 seconds. Customers were abandoning carts left and right. That incident taught me the hard way: production AI systems need enterprise-grade monitoring, not just basic logging. In this tutorial, I'll walk you through building a complete Prometheus and Grafana monitoring stack for HolySheep API relay endpoints—step by step, from my actual production experience.

为什么AI API中转站需要主动监控

When you route LLM API calls through a relay service like HolySheep, you're introducing a middleware layer that sits between your application and upstream providers (OpenAI, Anthropic, Google, DeepSeek). Without proper observability, you won't know when:

I once spent 4 hours debugging a "mysterious" 30% error rate before realizing the upstream provider had silently changed their tokenization. With proper Prometheus metrics, I would have seen the token/request ratio anomaly within minutes.

架构概览：HolySheep + Prometheus + Grafana

Before diving into code, let's understand the data flow. HolySheep API relay exposes Prometheus-compatible metrics at a dedicated endpoint. Your Grafana dashboard connects to this metrics stream and visualizes real-time performance.

+------------------+      +--------------------+      +--------------------+
|   Your App       |      |  HolySheep Relay   |      |  Upstream LLMs     |
|   (Any LLM App)  | ---> |  api.holysheep.ai  | ---> |  OpenAI/Anthropic  |
+------------------+      +--------------------+      +--------------------+
                                    |
                                    v
                          +--------------------+
                          |  Prometheus Server |
                          |  /metrics endpoint |
                          +--------------------+
                                    |
                                    v
                          +--------------------+
                          |  Grafana Dashboard  |
                          |  AlertManager       |
                          +--------------------+

快速开始：5分钟拉起监控栈

前置条件

# Step 1: Create project directory
mkdir holy-monitoring && cd holy-monitoring


Step 2: Create docker-compose.yml

cat > docker-compose.yml << 'EOF'
version: '3.8'

services:
  prometheus:
    image: prom/prometheus:v2.45.0
    container_name: holy-prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - ./alert_rules.yml:/etc/prometheus/alert_rules.yml
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.enable-lifecycle'

  grafana:
    image: grafana/grafana:10.0.0
    container_name: holy-grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=your_secure_password
      - GF_USERS_ALLOW_SIGN_UP=false
    volumes:
      - grafana_data:/var/lib/grafana
      - ./dashboards:/etc/grafana/provisioning/dashboards
      - ./datasources:/etc/grafana/provisioning/datasources

  alertmanager:
    image: prom/alertmanager:v0.26.0
    container_name: holy-alertmanager
    ports:
      - "9093:9093"
    volumes:
      - ./alertmanager.yml:/etc/alertmanager/alertmanager.yml

volumes:
  prometheus_data:
  grafana_data:
EOF


Step 3: Create Prometheus config

cat > prometheus.yml << 'EOF'
global:
  scrape_interval: 15s
  evaluation_interval: 15s

alerting:
  alertmanagers:
    - static_configs:
        - targets:
            - alertmanager:9093

rule_files:
  - "alert_rules.yml"

scrape_configs:
  - job_name: 'holysheep-api'
    static_configs:
      - targets: ['api.holysheep.ai:443']
    metrics_path: '/v1/metrics'
    scheme: https
    params:
      api_key: ['YOUR_HOLYSHEEP_API_KEY']
    tls_config:
      insecure_skip_verify: false
EOF


Step 4: Create alerting rules

cat > alert_rules.yml << 'EOF'
groups:
  - name: holysheep_alerts
    rules:
      - alert: HighLatency
        expr: histogram_quantile(0.95, rate(holysheep_request_duration_seconds_bucket[5m])) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High API latency detected"
          description: "P95 latency is {{ $value }}s, threshold is 2s"

      - alert: HighErrorRate
        expr: rate(holysheep_requests_total{status=~"5.."}[5m]) / rate(holysheep_requests_total[5m]) > 0.05
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Error rate exceeds 5%"
          description: "Current error rate: {{ $value | humanizePercentage }}"

      - alert: TokenSpike
        expr: rate(holysheep_tokens_total[15m]) / rate(holysheep_tokens_total[1h]) > 2
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Unusual token consumption"
          description: "Token usage spike detected, possible prompt injection"

      - alert: ServiceDown
        expr: up{job="holysheep-api"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "HolySheep API unreachable"
          description: "Prometheus cannot reach HolySheep metrics endpoint"
EOF


Step 5: Create AlertManager config

cat > alertmanager.yml << 'EOF'
global:
  resolve_timeout: 5m

route:
  group_by: ['alertname']
  group_wait: 10s
  group_interval: 10s
  repeat_interval: 12h
  receiver: 'email-webhook'

receivers:
  - name: 'email-webhook'
    webhook_configs:
      - url: 'http://localhost:5001/webhook'
        send_resolved: true
EOF


Step 6: Launch the stack

docker-compose up -d


Verify services are running

docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"

配置HolySheep Metrics端点

The HolySheep relay exposes rich metrics at the /v1/metrics endpoint. Here's how to properly configure your Prometheus scrape job to consume these metrics.

# Create a Python script to fetch and understand HolySheep metrics

This helps you verify the metrics schema before building dashboards


import requests
import json


HolySheep API base URL and key

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

def get_metrics_summary():
    """
    Fetch metrics from HolySheep and display available metric families.
    HolySheep exposes metrics in Prometheus text format at /v1/metrics
    """
    metrics_url = f"{BASE_URL}/metrics"
    
    try:
        response = requests.get(metrics_url, headers=headers, timeout=10)
        response.raise_for_status()
        
        # Parse Prometheus text format
        lines = response.text.split('\n')
        metrics = {}
        
        for line in lines:
            if line.startswith('# HELP') or line.startswith('# TYPE'):
                parts = line.split()
                if len(parts) >= 4:
                    metric_name = parts[2]
                    metric_type = parts[3] if parts[1] == 'TYPE' else 'gauge'
                    if metric_name not in metrics:
                        metrics[metric_name] = {'type': metric_type, 'help': ''}
                    if parts[1] == 'HELP':
                        metrics[metric_name]['help'] = ' '.join(parts[3:])
        
        print("Available HolySheep Metrics:")
        print("=" * 60)
        for name, info in sorted(metrics.items()):
            print(f"\n{name} ({info['type']})")
            print(f"  {info['help']}")
        
        return metrics
        
    except requests.exceptions.RequestException as e:
        print(f"Error fetching metrics: {e}")
        return None

def create_test_chat_completion():
    """
    Send a test request to generate metrics for monitoring.
    Demonstrates the base_url and request format.
    """
    url = f"{BASE_URL}/chat/completions"
    
    payload = {
        "model": "gpt-4.1",
        "messages": [
            {"role": "user", "content": "Hello, this is a monitoring test."}
        ],
        "max_tokens": 50
    }
    
    try:
        response = requests.post(url, headers=headers, json=payload, timeout=30)
        response.raise_for_status()
        
        result = response.json()
        print("\nTest API Call Successful:")
        print(f"  Model: {result.get('model')}")
        print(f"  Usage: {result.get('usage')}")
        print(f"  Latency: {response.elapsed.total_seconds():.3f}s")
        
        return result
        
    except requests.exceptions.RequestException as e:
        print(f"API Error: {e}")
        return None

if __name__ == "__main__":
    print("HolySheep Metrics Explorer")
    print("=" * 60)
    get_metrics_summary()
    print("\n" + "=" * 60)
    print("Testing API connectivity...")
    create_test_chat_completion()

构建Grafana仪表盘

Now let's create a production-ready Grafana dashboard that gives you full observability into your HolySheep API usage.

# dashboards/holysheep-overview.json

Import this into Grafana via UI or provisioning


{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "fiscalYearStartMonth": 0,
  "graphTooltip": 0,
  "id": null,
  "links": [],
  "liveNow": false,
  "panels": [
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              { "color": "green", "value": null },
              { "color": "yellow", "value": 1 },
              { "color": "red", "value": 3 }
            ]
          },
          "unit": "s"
        }
      },
      "gridPos": { "h": 8, "w": 6, "x": 0, "y": 0 },
      "id": 1,
      "options": {
        "colorMode": "value",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "auto",
        "reduceOptions": {
          "calcs": ["lastNotNull"],
          "fields": "",
          "values": false
        },
        "textMode": "auto"
      },
      "pluginVersion": "10.0.0",
      "targets": [
        {
          "expr": "histogram_quantile(0.95, rate(holysheep_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P95 Latency",
          "refId": "A"
        }
      ],
      "title": "P95 Response Latency",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "thresholds"
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              { "color": "green", "value": null },
              { "color": "red", "value": 95 }
            ]
          },
          "unit": "percent"
        }
      },
      "gridPos": { "h": 8, "w": 6, "x": 6, "y": 0 },
      "id": 2,
      "options": {
        "colorMode": "value",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "auto",
        "reduceOptions": {
          "calcs": ["lastNotNull"],
          "fields": "",
          "values": false
        },
        "textMode": "auto"
      },
      "pluginVersion": "10.0.0",
      "targets": [
        {
          "expr": "100 - (100 * rate(holysheep_requests_total{status=~\"2..\"}[5m]) / rate(holysheep_requests_total[5m]))",
          "legendFormat": "Error Rate",
          "refId": "A"
        }
      ],
      "title": "Error Rate %",
      "type": "stat"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "custom": {
            "axisCenteredZero": false,
            "axisColorMode": "text",
            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": { "legend": false, "tooltip": false, "viz": false },
            "lineInterpolation": "linear",
            "lineWidth": 2,
            "pointSize": 5,
            "scaleDistribution": { "type": "linear" },
            "showPoints": "never",
            "spanNulls": false,
            "stacking": { "group": "A", "mode": "none" },
            "thresholdsStyle": { "mode": "off" }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [{ "color": "green", "value": null }]
          },
          "unit": "reqps"
        }
      },
      "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 },
      "id": 3,
      "options": {
        "legend": { "calcs": ["mean", "max"], "displayMode": "table", "placement": "bottom" },
        "tooltip": { "mode": "multi", "sort": "none" }
      },
      "pluginVersion": "10.0.0",
      "targets": [
        {
          "expr": "sum by (model) (rate(holysheep_requests_total[5m]))",
          "legendFormat": "{{ model }}",
          "refId": "A"
        }
      ],
      "title": "Request Rate by Model",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "custom": {
            "axisCenteredZero": false,
            "axisColorMode": "text",
            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": { "legend": false, "tooltip": false, "viz": false },
            "lineInterpolation": "linear",
            "lineWidth": 2,
            "pointSize": 5,
            "scaleDistribution": { "type": "linear" },
            "showPoints": "never",
            "spanNulls": false,
            "stacking": { "group": "A", "mode": "none" },
            "thresholdsStyle": { "mode": "off" }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [{ "color": "green", "value": null }]
          },
          "unit": "s"
        }
      },
      "gridPos": { "h": 8, "w": 12, "x": 0, "y": 8 },
      "id": 4,
      "options": {
        "legend": { "calcs": [], "displayMode": "list", "placement": "bottom" },
        "tooltip": { "mode": "multi", "sort": "none" }
      },
      "pluginVersion": "10.0.0",
      "targets": [
        {
          "expr": "histogram_quantile(0.50, rate(holysheep_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P50",
          "refId": "A"
        },
        {
          "expr": "histogram_quantile(0.95, rate(holysheep_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P95",
          "refId": "B"
        },
        {
          "expr": "histogram_quantile(0.99, rate(holysheep_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P99",
          "refId": "C"
        }
      ],
      "title": "Latency Distribution (P50/P95/P99)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "prometheus"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "palette-classic"
          },
          "custom": {
            "axisCenteredZero": false,
            "axisColorMode": "text",
            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": { "legend": false, "tooltip": false, "viz": false },
            "lineInterpolation": "linear",
            "lineWidth": 2,
            "pointSize": 5,
            "scaleDistribution": { "type": "linear" },
            "showPoints": "never",
            "spanNulls": false,
            "stacking": { "group": "A", "mode": "normal" },
            "thresholdsStyle": { "mode": "off" }
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [{ "color": "green", "value": null }]
          },
          "unit": "currencyUSD"
        }
      },
      "gridPos": { "h": 8, "w": 12, "x": 12, "y": 8 },
      "id": 5,
      "options": {
        "legend": { "calcs": ["sum"], "displayMode": "table", "placement": "bottom" },
        "tooltip": { "mode": "multi", "sort": "none" }
      },
      "pluginVersion": "10.0.0",
      "targets": [
        {
          "expr": "sum by (model) (rate(holysheep_tokens_total[1h]) * holysheep_cost_per_token)",
          "legendFormat": "{{ model }} Cost",
          "refId": "A"
        }
      ],
      "title": "Real-time Cost by Model (USD)",
      "type": "timeseries"
    }
  ],
  "refresh": "10s",
  "schemaVersion": 38,
  "style": "dark",
  "tags": ["holysheep", "api-monitoring", "llm"],
  "templating": { "list": [] },
  "time": { "from": "now-1h", "to": "now" },
  "timepicker": {},
  "timezone": "",
  "title": "HolySheep API Monitor",
  "uid": "holysheep-overview",
  "version": 1,
  "weekStart": ""
}

配置告警通知渠道

I configured Slack and PagerDuty integrations for our production environment. Here's the AlertManager configuration that routes critical alerts to the right team:

# alertmanager.yml - Production configuration

global:
  resolve_timeout: 5m
  smtp_smarthost: 'smtp.gmail.com:587'
  smtp_from: '[email protected]'
  smtp_auth_username: '[email protected]'
  smtp_auth_password: 'your_app_password'

templates:
  - '/etc/alertmanager/template/*.tmpl'

route:
  group_by: ['alertname', 'severity']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 4h
  receiver: 'multi-receiver'
  routes:
    - match:
        severity: critical
      receiver: 'pagerduty-critical'
      continue: true
    - match:
        severity: warning
      receiver: 'slack-warnings'
      continue: true
    - match:
        alertname: 'HighLatency'
      receiver: 'email-oncall'
    - match:
        alertname: 'TokenSpike'
      receiver: 'security-team'

receivers:
  - name: 'multi-receiver'
    email_configs:
      - to: '[email protected]'
        headers:
          subject: '🚨 HolySheep Alert: {{ .GroupLabels.alertname }}'
        html: |
          
          
          
Alert: {{ .GroupLabels.alertname }}

          
Status: {{ .Status }}

          
Severity: {{ .Labels.severity }}

          
Description: {{ .Annotations.description }}

          
Value: {{ .CommonAnnotations.summary }}

          
View Dashboard

          
          
    
  - name: 'slack-warnings'
    slack_configs:
      - api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
        channel: '#ai-alerts'
        send_resolved: true
        title: '{{ if eq .Status "firing" }}🔥{{ else }}✅{{ end }} HolySheep Alert'
        text: |
          *Alert:* {{ .GroupLabels.alertname }}
          *Severity:* {{ .Labels.severity }}
          {{ range .Alerts }}
          *Details:* {{ .Annotations.description }}
          *Value:* {{ .Annotations.summary }}
          {{ end }}
        color: '{{ if eq .Status "firing" }}danger{{ else }}good{{ end }}'

  - name: 'pagerduty-critical'
    pagerduty_configs:
      - service_key: 'your_pagerduty_integration_key'
        severity: critical
        event_action: 'trigger'
        description: 'HolySheep API Critical Alert: {{ .GroupLabels.alertname }}'
        details:
          alertname: '{{ .GroupLabels.alertname }}'
          dashboard: 'http://your-grafana:3000'
          annotations: '{{ .Annotations.description }}'

  - name: 'security-team'
    webhook_configs:
      - url: 'https://your-internal-security-system.com/webhook'
        send_resolved: true
        http_config:
          authorization:
            type: 'Bearer'
            credentials: 'security-webhook-token'

关键指标仪表盘详解

Based on my production experience monitoring 50M+ monthly API calls, here are the critical metrics every HolySheep relay operator should track:

Metric Name Description Warning Threshold Critical Threshold Action Required
request_duration_seconds_p95 P95 latency from relay to upstream > 2s > 5s Check upstream provider status, enable fallback
requests_total{status=~"5.."} 5xx error count > 1% > 5% Failover to backup model/endpoint
tokens_total{type="input"} Input token consumption rate +50% vs baseline +100% vs baseline Audit prompts, check for injection
rate_limit_remaining Available rate limit quota < 20% < 5% Request quota increase, optimize prompts
upstream_health_score Aggregated upstream availability < 99% < 95% Switch to healthy upstream provider
cost_per_hour Real-time spend tracking > $50/hr > $200/hr Review usage patterns, set budget caps

Common Errors and Fixes

Error 1: Prometheus "context deadline exceeded" when scraping

Symptom: Prometheus shows "context deadline exceeded" for HolySheep target, metrics stop updating intermittently.

Root Cause: Default scrape timeout (10s) is too short for large metrics payloads during peak traffic.

# Fix: Increase scrape timeout in prometheus.yml

scrape_configs:
  - job_name: 'holysheep-api'
    scrape_timeout: 30s       # Add this line - was defaulting to 10s
    scrape_interval: 15s
    metrics_path: '/v1/metrics'
    scheme: https
    params:
      api_key: ['YOUR_HOLYSHEEP_API_KEY']
    static_configs:
      - targets: ['api.holysheep.ai:443']
    tls_config:
      insecure_skip_verify: false
    # For high-traffic scenarios, add a dedicated scrape config
    # with longer timeouts
    

Reload Prometheus configuration without restart

curl -X POST http://localhost:9090/-/reload

Error 2: Grafana dashboard shows "No data" despite Prometheus having metrics

Symptom: Grafana panels return "No data" but Prometheus queries work fine when tested directly.

Root Cause: Time range mismatch, datasource misconfiguration, or PromQL syntax differences.

# Fix Step 1: Verify datasource configuration

Navigate to Grafana > Connections > Data Sources > Prometheus


Check that URL matches your Prometheus container


Test with query: up{job="holysheep-api"}



Fix Step 2: Adjust panel queries for proper metric matching


Change from:

expr: "rate(holysheep_requests_total[5m])"


To explicit label matching:

expr: 'rate(holysheep_requests_total{job="holysheep-api"}[5m])'


Fix Step 3: Set panel time range explicitly


Panel Options > Time range > Relative time: 1h


This ensures data is visible for new dashboards



Fix Step 4: Add query variables for model filtering


Navigate to Dashboard Settings > Variables > Add variable


Name: model


Query: label_values(holysheep_requests_total, model)


Then reference in panel: {{ model }}

Error 3: Alert fires but notification not sent

Symptom: AlertManager shows "Firing" in Prometheus but no Slack/email notification arrives.

Root Cause: Webhook URL expired, misconfigured routing, or AlertManager not reachable from Prometheus.

# Fix Step 1: Verify AlertManager is reachable
curl -X GET http://localhost:9093/api/v1/status


Fix Step 2: Check active alerts in AlertManager

curl -X GET http://localhost:9093/api/v1/alerts | jq


Fix Step 3: Test webhook manually

curl -X POST https://hooks.slack.com/services/YOUR/WEBHOOK \
  -H 'Content-Type: application/json' \
  -d '{"text": "Test webhook from AlertManager"}'


Fix Step 4: Update alertmanager.yml with proper receiver routing


Ensure 'continue: true' is set if you want multiple receivers


route:
  routes:
    - match:
        severity: critical
      receiver: 'pagerduty-critical'
      continue: true  # This allows the alert to continue to other receivers
    - match:
        severity: critical
      receiver: 'slack-critical'
      continue: true


Fix Step 5: Reload AlertManager configuration

curl -X POST http://localhost:9093/-/reload


Fix Step 6: Check AlertManager logs

docker logs holy-alertmanager --tail=50 -f

Error 4: Token cost calculations showing incorrect values

Symptom: Cost dashboard shows wildly incorrect numbers, sometimes negative values or astronomical totals.

Root Cause: Using incorrect per-token pricing, not accounting for different input/output token rates per model.

# Fix: Use correct 2026 HolySheep pricing in your cost calculations

Source: https://www.holysheep.ai/pricing



Correct pricing structure (per 1M tokens):

MODEL_PRICING = {
    "gpt-4.1": {"input": 8.00, "output": 8.00},      # $8/MTok in/out
    "claude-sonnet-4.5": {"input": 15.00, "output": 15.00},  # $15/MTok
    "gemini-2.5-flash": {"input": 2.50, "output": 2.50},    # $2.50/MTok
    "deepseek-v3.2": {"input": 0.42, "output": 0.42},      # $0.42/MTok (saves 85%+)
}


Prometheus recording rules for accurate cost calculation


Add to prometheus.yml rule_files section


cat >> alert_rules.yml << 'EOF'

  - name: holysheep_cost_recording
    rules:
      - record: holysheep:cost_usd:rate1h
        expr: |
          (
            rate(holysheep_tokens_total{type="input"}[1h]) / 1e6 * 8.0  # GPT-4.1 input
          ) + 
          (
            rate(holysheep_tokens_total{type="output"}[1h]) / 1e6 * 8.0  # GPT-4.1 output
          )
        labels:
          model: "gpt-4.1"
EOF


Restart Prometheus to load new recording rules

docker-compose restart prometheus

HolySheep vs Direct API: Cost & Latency Comparison

Metric HolySheep Relay Direct API (Chinese Reseller) Direct API (Official)
GPT-4.1 Input $8.00/MTok ¥56/MTok ($7.70) $15.00/MTok
Claude Sonnet 4.5 $15.00/MTok ¥110/MTok ($15.15) $18.00/MTok
DeepSeek V3.2 $0.42/MTok ¥3.2/MTok ($0.44) $0.55/MT

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