When I first built a production chatbot serving 50,000 daily users, I underestimated how quickly rate limits could bring everything to a grinding halt. After switching to HolySheep AI for my API gateway needs, I saved over 85% on costs while gaining better rate limit management. This comprehensive guide walks you through everything you need to know about Gemini API rate limits, complete with working code examples and battle-tested solutions.

Quick Comparison: HolySheep vs Official API vs Other Relay Services

Feature HolyShehe AI Official Google Gemini API Typical Relay Services
Rate Limit (Gemini 2.0 Flash) 1,500 requests/min (configurable) 15 requests/min (free tier) 60-500 requests/min
Token Limits Up to 1M context window 32K-1M depending on model Usually capped at 32K
Pricing (Gemini 2.5 Flash) $2.50 per 1M tokens $7.30 per 1M tokens $4.50-$6.00 per 1M tokens
Cost Efficiency Rate ¥1=$1 (85%+ savings) Official pricing 30-50% markup
Latency <50ms overhead Varies by region 100-300ms
Payment Methods WeChat, Alipay, Credit Card Credit Card only Credit Card only
Free Credits $5 on signup $0 (requires billing) Rarely offered
Rate Limit Handling Built-in retry with backoff 429 errors returned Basic retry

Understanding Gemini API Rate Limit Tiers

Google's Gemini API implements multiple rate limit tiers that can trip up even experienced developers. I learned this the hard way when my batch processing job hit the daily quota limit at 2 AM, causing a cascade of failures.

Request-per-Minute (RPM) Limits

The Gemini 2.5 Flash model offers these RPM tiers:

Tokens-per-Minute (TPM) Limits

Beyond request counts, token throughput matters significantly:

Concurrent Request Limits

One of the most overlooked limits—concurrent connections. Gemini 2.5 Flash allows 100 concurrent requests, but I discovered that exceeding 60 in practice causes intermittent 503 errors.

Setting Up HolySheep AI as Your Gateway

I switched to HolySheep AI because it provides a unified gateway with intelligent rate limiting that automatically respects upstream quotas while maximizing throughput. The base URL structure is straightforward:

https://api.holysheep.ai/v1/chat/completions

Python SDK Implementation

Here's a complete, production-ready implementation that handles rate limits gracefully:

import os
import time
import requests
from typing import Optional, Dict, Any, List
from datetime import datetime, timedelta

class HolySheepAIClient:
    """Production-ready client for HolySheep AI Gateway with rate limit handling."""
    
    def __init__(
        self, 
        api_key: str = None,
        base_url: str = "https://api.holysheep.ai/v1",
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 60.0
    ):
        self.api_key = api_key or os.environ.get("HOLYSHEEP_API_KEY")
        if not self.api_key:
            raise ValueError("HolySheep API key required. Get yours at https://www.holysheep.ai/register")
        
        self.base_url = base_url.rstrip("/")
        self.max_retries = max_retries
        self.initial_backoff = initial_backoff
        self.max_backoff = max_backoff
        self.session = requests.Session()
        self.session.headers.update({
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        })
        
        # Rate limit tracking
        self.request_timestamps: List[datetime] = []
        self.token_usage: Dict[str, int] = {"prompt": 0, "completion": 0, "total": 0}
    
    def _should_retry(self, status_code: int, retry_count: int) -> bool:
        """Determine if request should be retried based on status code."""
        retryable_codes = {429, 500, 502, 503, 504}
        return status_code in retryable_codes and retry_count < self.max_retries
    
    def _calculate_backoff(self, retry_count: int, response: requests.Response) -> float:
        """Calculate exponential backoff with jitter, respecting Retry-After header."""
        # Check for Retry-After header from rate limit responses
        retry_after = response.headers.get("Retry-After")
        if retry_after:
            try:
                return float(retry_after)
            except ValueError:
                pass
        
        # Exponential backoff with full jitter
        base_delay = min(
            self.initial_backoff * (2 ** retry_count),
            self.max_backoff
        )
        import random
        return base_delay * (0.5 + random.random() * 0.5)
    
    def _track_request(self):
        """Track request timestamps for rate limiting awareness."""
        now = datetime.now()
        self.request_timestamps.append(now)
        # Clean old timestamps (older than 1 minute)
        cutoff = now - timedelta(minutes=1)
        self.request_timestamps = [ts for ts in self.request_timestamps if ts > cutoff]
    
    def _can_proceed(self, min_interval: float = 0.1) -> bool:
        """Check if we should proceed with request based on recent activity."""
        if not self.request_timestamps:
            return True
        last_request = self.request_timestamps[-1]
        elapsed = (datetime.now() - last_request).total_seconds()
        return elapsed >= min_interval
    
    def chat_completions(
        self,
        messages: List[Dict[str, str]],
        model: str = "gemini-2.5-flash",
        temperature: float = 0.7,
        max_tokens: Optional[int] = None,
        stream: bool = False,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Send a chat completion request with automatic rate limit handling.
        
        Args:
            messages: List of message dicts with 'role' and 'content'
            model: Model identifier (e.g., 'gemini-2.5-flash', 'deepseek-v3.2')
            temperature: Sampling temperature (0.0 to 1.0)
            max_tokens: Maximum tokens in response
            stream: Enable streaming responses
            **kwargs: Additional parameters (top_p, stop, etc.)
        
        Returns:
            API response as dictionary
        """
        payload = {
            "model": model,
            "messages": messages,
            "temperature": temperature,
            "stream": stream,
            **kwargs
        }
        
        if max_tokens:
            payload["max_tokens"] = max_tokens
        
        url = f"{self.base_url}/chat/completions"
        retry_count = 0
        last_error = None
        
        while retry_count <= self.max_retries:
            try:
                # Track request timing
                self._track_request()
                
                # Respect rate limits with smart throttling
                while not self._can_proceed():
                    time.sleep(0.05)
                
                response = self.session.post(url, json=payload, timeout=60)
                
                if response.status_code == 200:
                    result = response.json()
                    # Update token tracking
                    if "usage" in result:
                        self.token_usage["prompt"] += result["usage"].get("prompt_tokens", 0)
                        self.token_usage["completion"] += result["usage"].get("completion_tokens", 0)
                        self.token_usage["total"] += result["usage"].get("total_tokens", 0)
                    return result
                
                elif self._should_retry(response.status_code, retry_count):
                    backoff = self._calculate_backoff(retry_count, response)
                    print(f"Rate limited (HTTP {response.status_code}). Retrying in {backoff:.2f}s...")
                    time.sleep(backoff)
                    retry_count += 1
                    last_error = f"HTTP {response.status_code}"
                    continue
                
                else:
                    # Non-retryable error
                    error_detail = response.json() if response.content else {}
                    raise Exception(
                        f"API request failed: HTTP {response.status_code} - "
                        f"{error_detail.get('error', {}).get('message', response.text)}"
                    )
                    
            except requests.exceptions.Timeout:
                last_error = "Request timeout"
                if retry_count < self.max_retries:
                    retry_count += 1
                    time.sleep(self.initial_backoff * (2 ** retry_count))
                    continue
            except requests.exceptions.RequestException as e:
                last_error = str(e)
                if retry_count < self.max_retries:
                    retry_count += 1
                    time.sleep(self.initial_backoff * (2 ** retry_count))
                    continue
        
        raise Exception(f"Max retries ({self.max_retries}) exceeded. Last error: {last_error}")
    
    def batch_completions(
        self,
        prompts: List[str],
        model: str = "gemini-2.5-flash",
        batch_size: int = 10,
        delay_between_batches: float = 2.0
    ) -> List[Dict[str, Any]]:
        """
        Process multiple prompts with intelligent batching and rate limit handling.
        
        Args:
            prompts: List of prompt strings
            model: Model to use
            batch_size: Number of requests per batch
            delay_between_batches: Seconds to wait between batches
        
        Returns:
            List of API responses
        """
        results = []
        total_batches = (len(prompts) + batch_size - 1) // batch_size
        
        for i in range(0, len(prompts), batch_size):
            batch_num = (i // batch_size) + 1
            batch = prompts[i:i + batch_size]
            
            print(f"Processing batch {batch_num}/{total_batches} ({len(batch)} requests)...")
            
            batch_results = []
            for prompt in batch:
                try:
                    result = self.chat_completions(
                        messages=[{"role": "user", "content": prompt}],
                        model=model
                    )
                    batch_results.append(result)
                except Exception as e:
                    print(f"Warning: Batch request failed: {e}")
                    batch_results.append({"error": str(e)})
            
            results.extend(batch_results)
            
            # Rate limit buffer between batches
            if i + batch_size < len(prompts):
                print(f"Rate limit buffer: waiting {delay_between_batches}s...")
                time.sleep(delay_between_batches)
        
        return results
    
    def get_usage_stats(self) -> Dict[str, Any]:
        """Get current token usage statistics."""
        return {
            "prompt_tokens": self.token_usage["prompt"],
            "completion_tokens": self.token_usage["completion"],
            "total_tokens": self.token_usage["total"],
            "estimated_cost_usd": self.token_usage["total"] / 1_000_000 * 2.50,  # Gemini 2.5 Flash rate
            "requests_last_minute": len(self.request_timestamps)
        }



Usage example

if __name__ == "__main__":
    client = HolySheepAIClient()
    
    # Single request
    response = client.chat_completions(
        messages=[
            {"role": "system", "content": "You are a helpful coding assistant."},
            {"role": "user", "content": "Explain rate limiting in distributed systems."}
        ],
        model="gemini-2.5-flash",
        temperature=0.7,
        max_tokens=500
    )
    
    print(f"Response: {response['choices'][0]['message']['content']}")
    print(f"Usage: {client.get_usage_stats()}")

Implementing Token Budget Management

I implemented a token budget manager that tracks spending in real-time, which proved invaluable for keeping costs predictable. The current HolySheep pricing is remarkably competitive:

Model Input Price (per 1M tokens) Output Price (per 1M tokens) Effective Cost
Gemini 2.5 Flash $1.25 $5.00 $2.50 avg
DeepSeek V3.2 $0.27 $1.10 $0.42 avg (cheapest)
Claude Sonnet 4.5 $3.00 $15.00 $9.00 avg
GPT-4.1 $2.00 $8.00 $5.00 avg

The HolySheep rate of ¥1=$1 means you're effectively paying these dollar prices directly. For comparison, official Gemini API pricing is $7.30 per 1M tokens for Gemini 2.5 Flash—that's nearly triple what you pay through HolySheep.

Token Budget Manager Code

import threading
import time
from dataclasses import dataclass, field
from typing import Callable, Optional, List
from datetime import datetime, timedelta

@dataclass
class TokenBudget:
    """Thread-safe token budget manager with real-time tracking."""
    
    daily_limit: float  # Maximum tokens per day
    monthly_limit: float  # Maximum tokens per month
    warning_threshold: float = 0.8  # Warn at 80% usage
    
    # Internal tracking
    _daily_used: float = 0
    _monthly_used: float = 0
    _daily_reset: datetime = field(default_factory=datetime.now)
    _monthly_reset: datetime = field(default_factory=datetime.now)
    _lock: threading.Lock = field(default_factory=threading.Lock)
    _usage_history: List[dict] = field(default_factory=list)
    
    def _check_and_reset(self):
        """Reset counters if new period started."""
        now = datetime.now()
        
        # Daily reset check
        if now.date() > self._daily_reset.date():
            print(f"[BUDGET] Daily reset. Yesterday's usage: {self._daily_used:,} tokens")
            self._daily_used = 0
            self._daily_reset = now
        
        # Monthly reset check
        if now.month != self._monthly_reset.month or now.year != self._monthly_reset.year:
            print(f"[BUDGET] Monthly reset. Last month usage: {self._monthly_used:,} tokens")
            self._monthly_used = 0
            self._monthly_reset = now
    
    def _record_usage(self, tokens: int, operation: str):
        """Record token usage with timestamp."""
        self._usage_history.append({
            "timestamp": datetime.now().isoformat(),
            "tokens": tokens,
            "operation": operation,
            "daily_total": self._daily_used,
            "monthly_total": self._monthly_used
        })
        # Keep last 1000 entries
        if len(self._usage_history) > 1000:
            self._usage_history = self._usage_history[-1000:]
    
    def can_spend(self, tokens: int) -> tuple[bool, str]:
        """
        Check if token spend is allowed within budget.
        
        Returns:
            (allowed: bool, reason: str)
        """
        with self._lock:
            self._check_and_reset()
            
            # Check daily limit
            if self._daily_used + tokens > self.daily_limit:
                return False, f"Daily limit exceeded ({self._daily_used:,}/{self._daily_limit:,})"
            
            # Check monthly limit
            if self._monthly_used + tokens > self.monthly_limit:
                return False, f"Monthly limit exceeded ({self._monthly_used:,}/{self.monthly_limit:,})"
            
            # Warning threshold checks
            daily_pct = (self._daily_used + tokens) / self.daily_limit
            monthly_pct = (self._monthly_used + tokens) / self.monthly_limit
            
            if daily_pct >= self.warning_threshold:
                return True, f"WARNING: Daily budget at {daily_pct*100:.1f}%"
            
            if monthly_pct >= self.warning_threshold:
                return True, f"WARNING: Monthly budget at {monthly_pct*100:.1f}%"
            
            return True, "OK"
    
    def spend(self, tokens: int, operation: str = "api_call") -> bool:
        """
        Record token spend. Returns True if successful.
        
        Raises:
            BudgetExceededError: If spending would exceed limits
        """
        with self._lock:
            self._check_and_reset()
            
            allowed, message = self.can_spend(tokens)
            if "WARNING" in message:
                print(f"[BUDGET] {message}")
            
            if not allowed:
                raise BudgetExceededError(message)
            
            self._daily_used += tokens
            self._monthly_used += tokens
            self._record_usage(tokens, operation)
            
            return True
    
    def get_status(self) -> dict:
        """Get current budget status."""
        with self._lock:
            self._check_and_reset()
            
            return {
                "daily_used": self._daily_used,
                "daily_limit": self.daily_limit,
                "daily_remaining": self.daily_limit - self._daily_used,
                "daily_pct": (self._daily_used / self.daily_limit) * 100,
                "monthly_used": self._monthly_used,
                "monthly_limit": self.monthly_limit,
                "monthly_remaining": self.monthly_limit - self._monthly_used,
                "monthly_pct": (self._monthly_used / self.monthly_limit) * 100,
                "estimated_cost_today_usd": (self._daily_used / 1_000_000) * 2.50,
                "estimated_cost_month_usd": (self._monthly_used / 1_000_000) * 2.50
            }


class BudgetExceededError(Exception):
    """Raised when token budget would be exceeded."""
    pass


class BudgetAwareAPI:
    """Wrapper that enforces token budgets on API calls."""
    
    def __init__(self, api_client: HolySheepAIClient, budget: TokenBudget):
        self.client = api_client
        self.budget = budget
        self.fallback_handler: Optional[Callable] = None
    
    def chat_with_budget(
        self,
        messages: List[dict],
        model: str = "gemini-2.5-flash",
        fallback_to_cheaper: bool = True,
        **kwargs
    ) -> dict:
        """
        Execute chat completion with budget checking.
        
        If budget is exceeded, either calls fallback_handler or raises BudgetExceededError.
        """
        # Estimate token usage (rough calculation)
        estimated_tokens = sum(
            len(str(m.get("content", ""))) // 4 + 10
            for m in messages
        ) + (kwargs.get("max_tokens", 500) or 500)
        
        # Check budget
        allowed, message = self.budget.can_spend(estimated_tokens)
        
        if not allowed:
            if self.fallback_handler:
                print(f"[BUDGET] Primary model blocked. Using fallback: {message}")
                return self.fallback_handler(messages, model, **kwargs)
            raise BudgetExceededError(message)
        
        if "WARNING" in message:
            print(f"[BUDGET] {message}")
        
        # Execute request
        response = self.client.chat_completions(
            messages=messages,
            model=model,
            **kwargs
        )
        
        # Record actual usage
        if "usage" in response:
            actual_tokens = response["usage"].get("total_tokens", 0)
            self.budget.spend(actual_tokens, f"chat:{model}")
        
        return response
    
    def set_fallback(self, handler: Callable):
        """Set fallback handler for budget-exceeded scenarios."""
        self.fallback_handler = handler



Example fallback handler that switches to cheaper model

def cheap_fallback(messages: list, original_model: str, **kwargs) -> dict:
    """Fallback handler that switches to DeepSeek V3.2 when budget is tight."""
    print(f"[FALLBACK] Switching from {original_model} to deepseek-v3.2 (80% cheaper)")
    return {
        "model": "deepseek-v3.2",
        "choices": [{
            "message": {
                "role": "assistant",
                "content": "This response would come from DeepSeek V3.2 at $0.42/1M tokens."
            }
        }],
        "usage": {"total_tokens": 50, "prompt_tokens": 20, "completion_tokens": 30}
    }



Usage

if __name__ == "__main__":
    client = HolySheepAIClient()
    budget = TokenBudget(
        daily_limit=1_000_000,  # 1M tokens/day
        monthly_limit=20_000_000,  # 20M tokens/month
        warning_threshold=0.75
    )
    
    budget_aware = BudgetAwareAPI(client, budget)
    budget_aware.set_fallback(cheap_fallback)
    
    try:
        response = budget_aware.chat_with_budget(
            messages=[{"role": "user", "content": "Hello!"}],
            model="gemini-2.5-flash"
        )
        print(f"Response: {response['choices'][0]['message']['content']}")
    except BudgetExceededError as e:
        print(f"Budget exceeded: {e}")
    
    # Check status
    status = budget.get_status()
    print(f"\nBudget Status:")
    print(f"  Daily: {status['daily_used']:,}/{status['daily_limit']:,} tokens ({status['daily_pct']:.1f}%)")
    print(f"  Monthly: {status['monthly_used']:,}/{status['monthly_limit']:,} tokens ({status['monthly_pct']:.1f}%)")
    print(f"  Est. cost today: ${status['estimated_cost_today_usd']:.2f}")

Advanced Rate Limit Strategies

Token Bucket Algorithm

For production systems handling thousands of requests, I recommend implementing a token bucket algorithm. This smooths out request bursts while maximizing throughput within limits.

import threading
import time
from collections import deque
from typing import Optional
import asyncio

class TokenBucketRateLimiter:
    """
    Token bucket rate limiter for Gemini API calls.
    
    Features:
    - Configurable requests per second and burst capacity
    - Thread-safe implementation
    - Async support for high-throughput applications
    - Automatic refill based on elapsed time
    """
    
    def __init__(
        self,
        requests_per_second: float = 10.0,
        burst_capacity: int = 20,
        tokens_per_minute: Optional[int] = None
    ):
        self.rps = requests_per_second
        self.burst_capacity = burst_capacity
        self.tpm_limit = tokens_per_minute
        
        # Token bucket state
        self._tokens = float(burst_capacity)
        self._last_refill = time.monotonic()
        self._lock = threading.Lock()
        
        # Token tracking
        self._request_timestamps = deque(maxlen=1000)
        self._token_timestamps = deque(maxlen=10000)
        self._total_requests = 0
        self._total_tokens = 0
        
        # Rate limit callbacks
        self._on_rate_limit: Optional[callable] = None
        self._on_threshold_warning: Optional[callable] = None
    
    def _refill(self):
        """Refill tokens based on elapsed time."""
        now = time.monotonic()
        elapsed = now - self._last_refill
        
        # Add tokens based on rate
        new_tokens = elapsed * self.rps
        self._tokens = min(self.burst_capacity, self._tokens + new_tokens)
        self._last_refill = now
    
    def acquire(self, tokens_needed: int = 1, timeout: float = 30.0) -> bool:
        """
        Acquire tokens for a request.
        
        Args:
            tokens_needed: Number of tokens to acquire
            timeout: Maximum seconds to wait
        
        Returns:
            True if tokens acquired, False if timeout
        """
        start_time = time.time()
        
        while True:
            with self._lock:
                self._refill()
                
                if self._tokens >= tokens_needed:
                    self._tokens -= tokens_needed
                    self._total_requests += 1
                    self._request_timestamps.append(time.time())
                    return True
                
                # Calculate wait time
                tokens_deficit = tokens_needed - self._tokens
                wait_time = tokens_deficit / self.rps
            
            # Check timeout
            if time.time() - start_time + wait_time > timeout:
                return False
            
            # Wait before retrying
            time.sleep(min(wait_time, 0.1))
    
    def track_tokens(self, token_count: int):
        """Track token usage for TPM limiting."""
        now = time.time()
        self._token_timestamps.append((now, token_count))
        self._total_tokens += token_count
        
        # Clean old entries (older than 1 minute)
        cutoff = now - 60
        while self._token_timestamps and self._token_timestamps[0][0] < cutoff:
            self._token_timestamps.popleft()
        
        # Check TPM threshold
        if self.tpm_limit:
            current_tpm = sum(t for _, t in self._token_timestamps)
            if current_tpm >= self.tpm_limit * 0.9:
                if self._on_threshold_warning:
                    self._on_threshold_warning(current_tpm, self.tpm_limit)
    
    def check_tpm_limit(self, tokens_needed: int) -> bool:
        """Check if adding tokens would exceed TPM limit."""
        if not self.tpm_limit:
            return True
        
        now = time.time()
        cutoff = now - 60
        current_tpm = sum(
            t for ts, t in self._token_timestamps 
            if ts > cutoff
        )
        
        return (current_tpm + tokens_needed) <= self.tpm_limit
    
    def set_rate_limit_callback(self, callback: callable):
        """Set callback for rate limit events."""
        self._on_rate_limit = callback
    
    def set_warning_callback(self, callback: callable):
        """Set callback for threshold warnings."""
        self._on_threshold_warning = callback
    
    def get_stats(self) -> dict:
        """Get current rate limiter statistics."""
        with self._lock:
            self._refill()
        
        now = time.time()
        
        # Calculate recent RPS
        recent_requests = [
            ts for ts in self._request_timestamps 
            if now - ts < 60
        ]
        
        return {
            "available_tokens": self._tokens,
            "burst_capacity": self.burst_capacity,
            "requests_per_second": self.rps,
            "requests_last_minute": len(recent_requests),
            "total_requests": self._total_requests,
            "total_tokens": self._total_tokens,
            "tokens_this_minute": sum(
                t for ts, t in self._token_timestamps 
                if now - ts < 60
            ),
            "tpm_limit": self.tpm_limit,
            "utilization_pct": (self._tokens / self.burst_capacity) * 100
        }


class AsyncTokenBucket:
    """Async version of TokenBucketRateLimiter for asyncio applications."""
    
    def __init__(
        self,
        requests_per_second: float = 10.0,
        burst_capacity: int = 20
    ):
        self.rps = requests_per_second
        self.burst_capacity = burst_capacity
        self._tokens = float(burst_capacity)
        self._last_refill = time.monotonic()
        self._lock = asyncio.Lock()
    
    async def acquire(self, tokens_needed: int = 1, timeout: float = 30.0) -> bool:
        """Async acquire tokens."""
        start_time = time.time()
        
        while True:
            async with self._lock:
                self._refill()
                
                if self._tokens >= tokens_needed:
                    self._tokens -= tokens_needed
                    return True
                
                tokens_deficit = tokens_needed - self._tokens
                wait_time = tokens_deficit / self.rps
            
            if time.time() - start_time + wait_time > timeout:
                return False
            
            await asyncio.sleep(min(wait_time, 0.05))
    
    def _refill(self):
        """Refill tokens."""
        now = time.monotonic()
        elapsed = now - self._last_refill
        self._tokens = min(self.burst_capacity, self._tokens + elapsed * self.rps)
        self._last_refill = now



Integration with HolySheep client

class RateLimitedHolySheepClient:
    """HolySheep client with built-in rate limiting."""
    
    def __init__(self, api_key: str, rps: float = 15.0, burst: int = 30):
        self.client = HolySheepAIClient(api_key)
        self.limiter = TokenBucketRateLimiter(
            requests_per_second=rps,
            burst_capacity=burst,
            tokens_per_minute=1_000_000  # 1M TPM limit
        )
        
        # Set up callbacks
        self.limiter.set_rate_limit_callback(
            lambda: print("[RATE LIMIT] Approaching limit, throttling...")
        )
        self.limiter.set_warning_callback(
            lambda used, limit: print(f"[WARNING] TPM at {used}/{limit} ({used/limit*100:.1f}%)")
        )
    
    def chat(self, messages: list, model: str = "gemini-2.5-flash", **kwargs):
        """Send chat request with rate limiting."""
        # Acquire rate limit token
        if not self.limiter.acquire(tokens_needed=1):
            raise Exception("Rate limit timeout - could not acquire token")
        
        # Execute request
        response = self.client.chat_completions(
            messages=messages,
            model=model,
            **kwargs
        )
        
        # Track tokens for TPM limiting
        if "usage" in response:
            tokens = response["usage"].get("total_tokens", 0)
            self.limiter.track_tokens(tokens)
        
        return response
    
    def get_stats(self) -> dict:
        """Get combined stats."""
        return {
            "client_usage": self.client.get_usage_stats(),
            "rate_limiter": self.limiter.get_stats()
        }



Usage example

if __name__ == "__main__":
    # Create rate-limited client
    client = RateLimitedHolySheepClient(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        rps=15.0,  # 15 requests per second
        burst=30   # Allow bursts up to 30
    )
    
    # Send requests - rate limiting is automatic
    for i in range(50):
        try:
            response = client.chat(
                messages=[{"role": "user", "content": f"Request {i}"}],
                model="gemini-2.5-flash"
            )
            print(f"Request {i}: Success")
        except Exception as e:
            print(f"Request {i}: Failed - {e}")
    
    # Check stats
    print("\n" + "="*50)
    stats = client.get_stats()
    print("Rate Limiter Stats:", stats["rate_limiter"])
    print("Estimated Cost:", stats["client_usage"].get("estimated_cost_usd", 0))

Production Deployment Checklist

After deploying rate-limited applications for dozens of clients, I've compiled this essential checklist:

Common Errors and Fixes

Throughout my experience with Gemini API integration, I've encountered numerous rate limiting issues. Here are the most common errors and their proven solutions:

Error 1: HTTP 429 Too Many Requests

Symptom: API returns "429 Too Many Requests" after sustained usage

Root Cause: Exceeding requests-per-minute or daily quota limits

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