Introduction: The Midnight Alert That Started It All
Last Tuesday at 3:47 AM, I woke up to 47 Slack notifications. Our monitoring dashboard was screaming red: our AI API costs had spiked from the usual $120/day to $3,847 in a single night. The culprit? A runaway batch processing job that was sending the same 5,000-word document to our LLM API 340 times due to a retry loop bug. We burned through $3,600 in tokens before someone manually killed the process.
If we had implemented token consumption anomaly detection from day one, that incident would have cost us $0 and required zero human intervention. In this guide, I will walk you through building a robust anomaly detection system using the HolySheep AI API, complete with real code, pricing calculations, and the hard-won lessons from our own infrastructure.
Understanding Token Consumption Anomalies
Before diving into code, let's establish what constitutes an anomaly. In production AI systems, token consumption anomalies typically fall into three categories:
- Volume Spikes: Sudden increases in token usage beyond 3 standard deviations from your rolling average
- Request Loops: Identical or near-identical requests being sent repeatedly (our 3 AM nightmare)
- Burst Patterns: Unusual request frequency that doesn't match your normal traffic patterns
HolySheep AI offers aggressive pricing that makes anomaly detection even more critical—while other providers charge ¥7.3 per dollar equivalent, HolySheep AI charges just ¥1 per dollar, giving you an 85%+ savings. This means a small anomaly that might cost $50 on other platforms only costs $7.50 here, but undetected anomalies can still compound quickly with high-volume applications.
Building the Anomaly Detection System
Prerequisites
You'll need Python 3.8+, the requests library, and a HolySheep AI API key. At HolySheep AI, you get <50ms latency on most requests and free credits on signup to start experimenting.
Step 1: Basic Token Monitoring Client
import requests
import time
from datetime import datetime, timedelta
from collections import deque
import statistics
class HolySheepTokenMonitor:
"""
Monitor token consumption and detect anomalies in real-time.
Pricing reference (2026):
- DeepSeek V3.2: $0.42/M tokens (input), $0.84/M tokens (output)
- Gemini 2.5 Flash: $2.50/M tokens (input), $10/M tokens (output)
- GPT-4.1: $8/M tokens (input), $24/M tokens (output)
- Claude Sonnet 4.5: $15/M tokens (input), $75/M tokens (output)
"""
def __init__(self, api_key: str, alert_threshold: float = 3.0,
window_size: int = 100):
self.base_url = "https://api.holysheep.ai/v1"
self.api_key = api_key
self.alert_threshold = alert_threshold
self.request_history = deque(maxlen=window_size)
self.anomaly_log = []
self.baseline_cost = 0.0
def calculate_cost(self, usage: dict, model: str) -> float:
"""Calculate cost in USD based on model pricing."""
pricing = {
"deepseek-v3.2": (0.42, 0.84),
"gpt-4.1": (8.0, 24.0),
"gemini-2.5-flash": (2.50, 10.0),
"claude-sonnet-4.5": (15.0, 75.0)
}
if model not in pricing:
return 0.0
input_cost, output_cost = pricing[model]
prompt_tokens = usage.get("prompt_tokens", 0)
completion_tokens = usage.get("completion_tokens", 0)
return (prompt_tokens * input_cost / 1_000_000 +
completion_tokens * output_cost / 1_000_000)
def make_request(self, model: str, messages: list,
max_cost_alert: float = 10.0) -> dict:
"""
Make API request with automatic cost tracking and anomaly detection.
Alerts trigger if single request exceeds max_cost_alert (default: $10)
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages
}
start_time = time.time()
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
latency_ms = (time.time() - start_time) * 1000
if response.status_code != 200:
raise Exception(f"API Error: {response.status_code} - {response.text}")
data = response.json()
usage = data.get("usage", {})
# Calculate and track cost
request_cost = self.calculate_cost(usage, model)
total_tokens = usage.get("total_tokens", 0)
# Record request metadata
request_record = {
"timestamp": datetime.now(),
"model": model,
"prompt_tokens": usage.get("prompt_tokens", 0),
"completion_tokens": usage.get("completion_tokens", 0),
"total_tokens": total_tokens,
"cost_usd": request_cost,
"latency_ms": latency_ms
}
self.request_history.append(request_record)
# Check for anomalies
anomaly = self._detect_anomaly(request_record)
if anomaly:
request_record["anomaly"] = anomaly
self.anomaly_log.append(request_record)
# Cost threshold check
if request_cost > max_cost_alert:
self._trigger_alert("COST_THRESHOLD", request_record)
return data
def _detect_anomaly(self, current_request: dict) -> dict:
"""Detect if current request is anomalous based on historical patterns."""
if len(self.request_history) < 10:
return None
# Calculate statistics from history
costs = [r["cost_usd"] for r in self.request_history]
tokens = [r["total_tokens"] for r in self.request_history]
mean_cost = statistics.mean(costs)
std_cost = statistics.stdev(costs) if len(costs) > 1 else 0
mean_tokens = statistics.mean(tokens)
std_tokens = statistics.stdev(tokens) if len(tokens) > 1 else 0
# Check for statistical anomaly
z_score_cost = (current_request["cost_usd"] - mean_cost) / std_cost if std_cost > 0 else 0
z_score_tokens = (current_request["total_tokens"] - mean_tokens) / std_tokens if std_tokens > 0 else 0
anomaly_types = []
if abs(z_score_cost) > self.alert_threshold:
anomaly_types.append(f"cost_zscore:{z_score_cost:.2f}")
if abs(z_score_tokens) > self.alert_threshold:
anomaly_types.append(f"token_zscore:{z_score_tokens:.2f}")
# Check for request loop (duplicate detection)
if self._check_duplicate_request(current_request):
anomaly_types.append("duplicate_detected")
return {"types": anomaly_types} if anomaly_types else None
def _check_duplicate_request(self, current_request: dict) -> bool:
"""Check if this request is suspiciously similar to recent ones."""
prompt_tokens = current_request["prompt_tokens"]
# Look for exact token count matches in recent history
recent_prompts = [r["prompt_tokens"] for r in list(self.request_history)[-10:]]
duplicates = sum(1 for t in recent_prompts if t == prompt_tokens)
return duplicates >= 3
def _trigger_alert(self, alert_type: str, request_data: dict):
"""Trigger alert for detected anomaly (implement your notification logic)."""
print(f"[ALERT] {alert_type}: {request_data}")
# Implement: send Slack notification, email, webhook, etc.
Usage Example
monitor = HolySheepTokenMonitor(
api_key="YOUR_HOLYSHEEP_API_KEY",
alert_threshold=3.0,
window_size=100
)
try:
response = monitor.make_request(
model="deepseek-v3.2",
messages=[{"role": "user", "content": "Hello, explain token monitoring."}]
)
print(f"Request successful. Cost: ${monitor.request_history[-1]['cost_usd']:.4f}")
except Exception as e:
print(f"Error: {e}")Step 2: Advanced Loop Detection with Hashing
import hashlib
import redis
from typing import Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
@dataclass
class RequestFingerprint:
"""Fingerprint a request for loop detection."""
content_hash: str
timestamp: datetime
request_id: str
occurrence_count: int = 1
class LoopDetectionEngine:
"""
Advanced loop detection using content hashing and rate limiting.
Detects the exact scenario that caused our $3,600 incident.
"""
def __init__(self, redis_client: Optional[redis.Redis] = None,
max_requests_per_minute: int = 60,
loop_threshold: int = 5,
time_window_seconds: int = 300):
self.redis = redis_client
self.max_requests_per_minute = max_requests_per_minute
self.loop_threshold = loop_threshold
self.time_window = time_window_seconds
self.fingerprint_cache: dict[str, RequestFingerprint] = {}
self.blocked_hashes: Set[str] = set()
def generate_fingerprint(self, messages: list) -> str:
"""Generate unique hash for request content."""
content = str(messages)
return hashlib.sha256(content.encode()).hexdigest()[:16]
def check_and_record(self, fingerprint: str, request_id: str) -> dict:
"""
Check if request should be allowed and record it.
Returns: {"allowed": bool, "reason": str, "count": int}
"""
now = datetime.now()
# Check if this hash is blocked
if fingerprint in self.blocked_hashes:
return {
"allowed": False,
"reason": "BLOCKED_LOOP",
"count": self.fingerprint_cache.get(fingerprint, RequestFingerprint("", now, "")).occurrence_count
}
# Check rate limit
if not self._check_rate_limit(request_id):
return {"allowed": False, "reason": "RATE_LIMIT_EXCEEDED", "count": 0}
# Record or increment fingerprint
if fingerprint in self.fingerprint_cache:
fp = self.fingerprint_cache[fingerprint]
fp.occurrence_count += 1
# Auto-block if loop detected
if fp.occurrence_count >= self.loop_threshold:
self.blocked_hashes.add(fingerprint)
return {
"allowed": False,
"reason": f"LOOP_DETECTED ({fp.occurrence_count}x identical requests)",
"count": fp.occurrence_count
}
else:
self.fingerprint_cache[fingerprint] = RequestFingerprint(
content_hash=fingerprint,
timestamp=now,
request_id=request_id,
occurrence_count=1
)
return {"allowed": True, "reason": "OK", "count": self.fingerprint_cache[fingerprint].occurrence_count}
def _check_rate_limit(self, request_id: str) -> bool:
"""Check if request exceeds rate limit."""
if self.redis:
key = f"rate_limit:{request_id}"
current = self.redis.get(key)
if current and int(current) >= self.max_requests_per_minute:
return False
self.redis.incr(key)
self.redis.expire(key, 60)
return True
def unblock_hash(self, fingerprint: str):
"""Manually unblock a hash (for legitimate retried requests)."""
self.blocked_hashes.discard(fingerprint)
if fingerprint in self.fingerprint_cache:
self.fingerprint_cache[fingerprint].occurrence_count = 1
Integration with HolySheep API client
class HolySheepAPIWithLoopProtection:
"""HolySheep API client with built-in loop detection."""
def __init__(self, api_key: str, loop_detector: LoopDetectionEngine):
self.base_url = "https://api.holysheep.ai/v1"
self.api_key = api_key
self.loop_detector = loop_detector
def chat_completions(self, messages: list, model: str = "deepseek-v3.2",
auto_retry: bool = True) -> dict:
"""
Send chat completion request with loop protection.
Auto-retry only if request was allowed but failed due to transient errors.
"""
import uuid
import time
request_id = str(uuid.uuid4())
fingerprint = self.loop_detector.generate_fingerprint(messages)
# Check loop detection
check_result = self.loop_detector.check_and_record(fingerprint, request_id)
if not check_result["allowed"]:
raise LoopDetectedError(
f"Request blocked: {check_result['reason']}. "
f"Occurrence: {check_result['count']}. "
f"Request ID: {request_id}"
)
# Make request
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Request-ID": request_id
}
payload = {
"model": model,
"messages": messages
}
max_retries = 3 if auto_retry else 0
for attempt in range(max_retries + 1):
try:
response = requests.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
if response.status_code == 200:
return response.json()
elif response.status_code == 429:
time.sleep(2 ** attempt) # Exponential backoff
continue
else:
raise APIError(f"HTTP {response.status_code}: {response.text}")
except requests.exceptions.Timeout:
if attempt == max_retries:
raise
time.sleep(2 ** attempt)
raise APIError("Max retries exceeded")
class LoopDetectedError(Exception):
"""Raised when a request loop is detected and blocked."""
pass
class APIError(Exception):
"""Raised for API errors."""
pass
Usage example
loop_detector = LoopDetectionEngine(
loop_threshold=5,
time_window_seconds=300
)
client = HolySheepAPIWithLoopProtection(
api_key="YOUR_HOLYSHEEP_API_KEY",
loop_detector=loop_detector
)
This request would be blocked if sent 5+ times
messages = [{"role": "user", "content": "Process this document for analysis."}]
try:
result = client.chat_completions(messages, model="deepseek-v3.2")
print(f"Success! Tokens used: {result['usage']['total_tokens']}")
except LoopDetectedError as e:
print(f"LOOP BLOCKED: {e}")
# Implement your retry logic or alert hereStep 3: Cost Budget Enforcement
from dataclasses import dataclass
from typing import Callable
import threading
from datetime import datetime, timedelta
@dataclass
class BudgetConfig:
"""Configuration for cost budget enforcement."""
daily_limit_usd: float = 100.0
hourly_limit_usd: float = 25.0
per_request_limit_usd: float = 5.0
cooldown_seconds: int = 300 # 5 minute cooldown when limit exceeded
class BudgetEnforcer:
"""
Enforce spending limits with automatic circuit breaking.
Critical for production systems - prevents runaway costs.
With HolySheep AI pricing (DeepSeek V3.2 at $0.42/M input tokens):
- $100/day = ~238M input tokens per day
- $25/hour = ~59.5M input tokens per hour
- $5/request = ~11.9M input tokens per single request
"""
def __init__(self, config: BudgetConfig):
self.config = config
self._daily_spend = 0.0
self._hourly_spend = 0.0
self._last_reset = datetime.now()
self._hour_reset = datetime.now()
self._locked = False
self._lock_reason = ""
self._lock_until = None
self._callbacks: list[Callable] = []
self._lock = threading.Lock()
def add_alert_callback(self, callback: Callable):
"""Add callback to be notified when budget limits are hit."""
self._callbacks.append(callback)
def check_budget(self, estimated_cost: float = 0.0) -> bool:
"""
Check if request should proceed based on budget limits.
Returns True if request is allowed, False if blocked.
"""
with self._lock:
now = datetime.now()
# Reset daily counter if needed
if now - self._last_reset > timedelta(days=1):
self._daily_spend = 0.0
self._last_reset = now
# Reset hourly counter if needed
if now - self._hour_reset > timedelta(hours=1):
self._hourly_spend = 0.0
self._hour_reset = now
# Check cooldown lock
if self._locked and self._lock_until:
if now < self._lock_until:
return False
else:
self._locked = False
# Check per-request limit
if estimated_cost > self.config.per_request_limit_usd:
self._trigger_lock(
f"Per-request limit exceeded: ${estimated_cost:.2f} > "
f"${self.config.per_request_limit_usd:.2f}"
)
return False
# Check hourly limit
if self._hourly_spend + estimated_cost > self.config.hourly_limit_usd:
self._trigger_lock(
f"Hourly limit would be exceeded: ${self._hourly_spend + estimated_cost:.2f} > "
f"${self.config.hourly_limit_usd:.2f}"
)
return False
# Check daily limit
if self._daily_spend + estimated_cost > self.config.daily_limit_usd:
self._trigger_lock(
f"Daily limit would be exceeded: ${self._daily_spend + estimated_cost:.2f} > "
f"${self.config.daily_limit_usd:.2f}"
)
return False
return True
def record_spend(self, cost: float):
"""Record actual spend after request completes."""
with self._lock:
self._daily_spend += cost
self._hourly_spend += cost
def get_status(self) -> dict:
"""Get current budget status."""
with self._lock:
now = datetime.now()
return {
"daily_spend_usd": self._daily_spend,
"daily_limit_usd": self.config.daily_limit_usd,
"daily_remaining_usd": self.config.daily_limit_usd - self._daily_spend,
"hourly_spend_usd": self._hourly_spend,
"hourly_limit_usd": self.config.hourly_limit_usd,
"hourly_remaining_usd": self.config.hourly_limit_usd - self._hourly_spend,
"is_locked": self._locked,
"lock_reason": self._lock_reason if self._locked else None,
"lock_until": self._lock_until.isoformat() if self._lock_until else None,
"next_reset": (self._hour_reset + timedelta(hours=1)).isoformat()
}
def _trigger_lock(self, reason: str):
"""Trigger circuit breaker lock."""
self._locked = True
self._lock_reason = reason
self._lock_until = datetime.now() + timedelta(seconds=self.config.cooldown_seconds)
for callback in self._callbacks:
try:
callback(self.get_status())
except Exception as e:
print(f"Callback error: {e}")
def unlock(self):
"""Manually unlock (use with caution)."""
with self._lock:
self._locked = False
self._lock_reason = ""
self._lock_until = None
Complete integrated client
class HolySheepProductionClient:
"""
Production-ready HolySheep AI client with:
- Token anomaly detection
- Loop detection
- Budget enforcement
- Automatic alerting
"""
def __init__(self, api_key: str,
budget_config: BudgetConfig = None,
anomaly_threshold: float = 3.0):
self.base_url = "https://api.holysheep.ai/v1"
self.api_key = api_key
self.budget = budget_config or BudgetConfig()
self.monitor = HolySheepTokenMonitor(api_key, anomaly_threshold)
self.loop_detector = LoopDetectionEngine(loop_threshold=5)
# Set up alert callbacks
self.budget.add_alert_callback(self._on_budget_alert)
def _on_budget_alert(self, status: dict):
"""Handle budget limit alerts."""
print(f"[CRITICAL] Budget alert triggered: {status}")
# Implement: Slack, PagerDuty, email notifications
def chat(self, messages: list, model: str = "deepseek-v3.2",
max_cost_per_request: float = 1.0) -> dict:
"""
Send chat request with full protection stack.
Example cost calculation for DeepSeek V3.2:
- 1000 tokens input @ $0.42/M = $0.00042
- 500 tokens output @ $0.84/M = $0.00042
- Total: ~$0.00084 per typical request
"""
import uuid
request_id = str(uuid.uuid4())
fingerprint = self.loop_detector.generate_fingerprint(messages)
# Layer 1: Loop detection
loop_check = self.loop_detector.check_and_record(fingerprint, request_id)
if not loop_check["allowed"]:
raise Exception(f"LOOP_BLOCKED: {loop_check['reason']}")
# Layer 2: Budget check (estimate ~2x actual for safety)
estimated_tokens = sum(len(m.get("content", "")) // 4 for m in messages)
estimated_cost = (estimated_tokens / 1_000_000) * 0.42 * 2
if not self.budget.check_budget(estimated_cost):
raise Exception(f"BUDGET_LIMIT: {self.budget._lock_reason}")
# Layer 3: Make request
try:
result = self.monitor.make_request(
model=model,
messages=messages,
max_cost_alert=max_cost_per_request
)
# Record actual spend
actual_cost = self.monitor.request_history[-1]["cost_usd"]
self.budget.record_spend(actual_cost)
return result
except Exception as e:
print(f"Request failed: {e}")
raise
Production usage
client = HolySheepProductionClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
budget_config=BudgetConfig(
daily_limit_usd=50.0, # $50/day max
hourly_limit_usd=15.0, # $15/hour max
per_request_limit_usd=2.0 # $2 per request max
)
)
try:
response = client.chat([
{"role": "user", "content": "Explain the importance of budget controls."}
])
print("Request successful!")
print(f"Total spent today: ${client.budget.get_status()['daily_spend_usd']:.4f}")
except Exception as e:
print(f"Request blocked: {e}")
print(f"Current status: {client.budget.get_status()}")Common Errors and Fixes
Error 1: 401 Unauthorized - Invalid API Key
# PROBLEM: Getting "401 Unauthorized" or "Invalid API key" errors
ERROR MESSAGE: {"error": {"message": "Invalid API key", "type": "invalid_request_error"}}
CAUSE: The API key is missing, malformed, or has been revoked
SOLUTION: Verify your API key format and source
import os
CORRECT: Set environment variable first
os.environ["HOLYSHEEP_API_KEY"] = "hs_xxxxxxxxxxxxxxxxxxxx"
Then use in your client
client = HolySheepTokenMonitor(
api_key=os.environ.get("HOLYSHEEP_API_KEY") # Never hardcode!
)
Verify key format: HolySheep keys start with "hs_" prefix
Example valid key: hs_a1b2c3d4e5f6g7h8i9j0...
TO OBTAIN A VALID KEY:
1. Sign up at https://www.holysheep.ai/register
2. Navigate to API Keys section
3. Generate new key with appropriate permissions
4. Copy and set as environment variable (NEVER commit to git!)
Error 2: Connection Timeout - Request Hangs or Times Out
# PROBLEM: Requests hang indefinitely or timeout after 30 seconds
ERROR MESSAGE: requests.exceptions.ReadTimeout or ConnectionError
CAUSE: Network issues, rate limiting, or server overload
SOLUTION: Implement proper timeout handling and retry logic
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
def create_session_with_retries() -> requests.Session:
"""Create requests session with automatic retry and timeout."""
session = requests.Session()
# Configure retry strategy
retry_strategy = Retry(
total=3,
backoff_factor=1, # Wait 1s, 2s, 4s between retries
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["POST", "GET"]
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("https://", adapter)
return session
def safe_api_call(messages: list, timeout: int = 30) -> dict:
"""Make API call with proper timeout handling."""
session = create_session_with_retries()
headers = {
"Authorization": f"Bearer {os.environ['HOLYSHEEP_API_KEY']}",
"Content-Type": "application/json"
}
payload = {
"model": "deepseek-v3.2",
"messages": messages,
"max_tokens": 1000 # Always set limits to prevent runaway costs
}
try:
response = session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=timeout # CRITICAL: Always set timeout!
)
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
print("Request timed out. Consider increasing timeout or checking network.")
# Implement fallback or queue for retry
return None
except requests.exceptions.ConnectionError as e:
print(f"Connection error: {e}")
print("HolySheep AI offers <50ms latency - if you're seeing high latency,")
print("check your network or implement circuit breaker pattern.")
return None
Test the safe call
result = safe_api_call([{"role": "user", "content": "Hello"}])
if result:
print(f"Success: {result.get('usage', {}).get('total_tokens', 0)} tokens")Error 3: Cost Explosion - Unexpectedly High Token Usage
# PROBLEM: Token usage much higher than expected, costs escalating rapidly
WARNING SIGNS:
- Tokens per request 10x normal
- Cost alerts triggering frequently
- Batch jobs running much longer than expected
CAUSE: Usually caused by:
1. Missing max_tokens limits
2. System prompts being prepended to every request
3. Retry loops causing duplicate requests
4. Context accumulation in conversation history
SOLUTION: Implement cost guards at multiple levels
class CostGuardedClient:
"""Client with multiple layers of cost protection."""
def __init__(self, api_key: str, max_tokens_per_request: int = 2000):
self.api_key = api_key
self.max_tokens_per_request = max_tokens_per_request
self.total_tokens_this_session = 0
self.max_session_tokens = 100_000 # Safety limit
def _estimate_tokens(self, messages: list) -> int:
"""Rough estimate of tokens in request."""
# Simple approximation: 1 token ≈ 4 characters
total_chars = sum(len(m.get("content", "")) for m in messages)
return total_chars // 4
def chat_with_guard(self, messages: list, model: str = "deepseek-v3.2") -> dict:
"""Send chat request with cost guards."""
estimated = self._estimate_tokens(messages)
# GUARD 1: Check per-request limit
if estimated > self.max_tokens_per_request:
raise ValueError(
f"Request too large: ~{estimated} tokens. "
f"Max allowed: {self.max_tokens_per_request}. "
f"Truncate your input or increase max_tokens_per_request."
)
# GUARD 2: Check session cumulative limit
if self.total_tokens_this_session + estimated > self.max_session_tokens:
raise ValueError(
f"Session token limit reached: {self.total_tokens_this_session + estimated} > "
f"{self.max_session_tokens}. Start a new session."
)
# Make the actual request with explicit limits
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"max_tokens": min(self.max_tokens_per_request, 4000) # Hard cap
}
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=30
)
if response.status_code != 200:
raise Exception(f"API error: {response.status_code} - {response.text}")
result = response.json()
actual_tokens = result.get("usage", {}).get("total_tokens", 0)
self.total_tokens_this_session += actual_tokens
print(f"[COST GUARD] This request: {actual_tokens} tokens")
print(f"[COST GUARD] Session total: {self.total_tokens_this_session} tokens")
return result
def reset_session(self):
"""Reset session counters - call when starting new conversation."""
self.total_tokens_this_session = 0
print("Session reset. Token counters cleared.")
Usage - notice the safety rails built in
guard = CostGuardedClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
max_tokens_per_request=2000
)
try:
# This will be rejected if too large
long_message = "Hello " * 10000 # ~50,000 chars = ~12,500 tokens
result = guard.chat_with_guard([
{"role": "user", "content": long_message}
])
except ValueError as e:
print(f"BLOCKED: {e}")
# Good! We prevented a potentially expensive request
Compare costs across models (2026 pricing):
DeepSeek V3.2: $0.42/M input = $0.005 for 12,500 tokens
GPT-4.1: $8/M input = $0.10 for 12,500 tokens
Claude Sonnet 4.5: $15/M input = $0.19 for 12,500 tokens
Monitoring Dashboard Integration
To visualize your token consumption and anomaly detection results, I recommend integrating with common monitoring tools. Here's a Prometheus-compatible metrics exporter:
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import threading
import time
class HolySheepMetricsExporter:
"""
Export HolySheep AI metrics to Prometheus for visualization.
Tracks: token consumption, costs, anomalies, and latency.
"""
def __init__(self, monitor: HolySheepTokenMonitor,
budget: BudgetEnforcer,
export_port: int = 9090):
self.monitor = monitor
self.budget = budget
# Define Prometheus metrics
self.tokens_total = Counter(
'holysheep_tokens_total',
'Total tokens processed',
['model', 'type'] # type: prompt or completion
)
self.cost_usd = Counter(
'holysheep_cost_usd_total',
'Total cost in USD'
)
self.request_latency = Histogram(
'holysheep_request_latency_seconds',
'Request latency in seconds',
buckets=[0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0]
)
self.anomaly_count = Counter(
'holysheep_anomalies_total',
'Total anomalies detected',
['type']
)
self.budget_remaining = Gauge(
'holysheep_budget_remaining_usd',
'Remaining budget in USD',
['period'] # period: daily or hourly
)
self.start_http_server(export_port)
self._start_update_loop()
def _start_update_loop(self):
"""Background thread to update budget metrics."""
def update_loop():
while True:
status = self.budget.get_status()
self.budget_remaining.labels(period='daily').set(
status['daily_remaining_usd']
)
self.budget_remaining.labels(period='hourly').set(
status['hourly_remaining_usd']
)
time.sleep(10)
thread = threading.Thread(target=update_loop, daemon=True)
thread.start()
def record_request(self, request_data: dict):
"""Record metrics for a completed request."""
self.tokens_total.labels(
model=request_data['model'],
type='prompt'
).inc(request_data['prompt_tokens'])
self.tokens_total.labels(
model=request_data['model'],
type='completion'
).inc(request_data['completion_tokens'])
self.cost_usd.inc(request_data['cost_usd'])
self.request_latency.observe(request_data['latency_ms'] / 1000)
if 'anomaly' in request_data:
for anomaly_type in request_data['anomaly'].get('types', []):
self.anomaly_count.labels(type=anomaly_type).inc()
Start metrics server on port 9090
Then configure Prometheus to scrape:
scrape_configs:
- job_name: 'holysheep-metrics'
static_configs:
- targets: ['your-server:9090']
metrics_path: '/