🔄 Key Rotation System

LLM Proxy Round Robin API Keys

Maximize your LLM throughput by distributing requests across multiple API keys. Implement intelligent round robin rotation to avoid rate limits and achieve higher capacity without service interruption.

API Key Pool

4 Keys Active

Active

Ready

Exhausted

Ready

Next request → Key 2 | Rate limit on K3 resets in 45s

Round Robin Features

Distribute API requests intelligently across your key pool for maximum efficiency.

🔄

Automatic Rotation

Cycle through API keys automatically with each request. The proxy selects the next available key in the pool, ensuring even distribution across all your credentials.

⚡

Rate Limit Avoidance

Multiply your effective rate limit by the number of keys. With 5 keys each having 60 req/min, achieve 300 req/min total capacity seamlessly.

🛡️

Failover Protection

Automatically skip exhausted keys and retry with available alternatives. Continuous service even when individual keys hit their limits.

📊

Usage Tracking

Monitor per-key usage, rate limit status, and error rates. Identify which keys are being used most and balance distribution accordingly.

⏱️

Reset Timing

Track rate limit reset times and intelligently route requests. Know exactly when each key will be available again.

⚖️

Weighted Distribution

Assign different weights to keys based on tier levels or remaining quotas. Premium keys can receive more traffic than free tier keys.

Rotation Strategies

Choose the right key selection strategy for your use case.

🔄

Simple Round Robin

Basic sequential rotation through all keys. Each request uses the next key in the pool, wrapping around when reaching the end.

Simplest implementation
Even distribution
No state tracking needed
Works well for uniform keys

⚡

Least Recently Used

Select the key that has been idle longest. Ensures all keys get equal rest periods and maximizes reset time recovery.

Maximizes recovery time
Better for burst traffic
Requires timestamp tracking
Optimal for rate limit avoidance

📈

Weighted Round Robin

Assign weights to keys based on tier or quota. Higher-tier keys receive proportionally more requests than lower-tier alternatives.

Respects key tiers
Optimizes cost/performance
Configurable weights
Best for mixed key pools

🎯

Smart Selection

AI-driven selection considering rate limits, latency, and error rates. Dynamically adjusts to optimize throughput and reliability.

Adaptive to conditions
Considers all metrics
Self-optimizing
Best for high-volume production

Implementation Architecture

How round robin key rotation works in your proxy layer.

Request Flow with Key Rotation

📱

Client
Request

→

🔀

Key
Selector

→

🔑

Key
Pool

→

🤖

OpenAI
API

Python Round Robin Implementation

import time
from collections import deque

class RoundRobinKeyPool:
    def __init__(self, api_keys):
        self.keys = deque(api_keys)
        self.exhausted = {}  # key -> reset_time
        self.current_index = 0
    
    def get_next_key(self):
        # Remove expired exhaustion entries
        now = time.time()
        self.exhausted = {k: v for k, v in self.exhausted.items() if v > now}
        
        # Find next available key
        attempts = 0
        while attempts < len(self.keys):
            key = self.keys[self.current_index]
            self.current_index = (self.current_index + 1) % len(self.keys)
            
            if key not in self.exhausted:
                return key
            
            attempts += 1
        
        # All keys exhausted - wait for first reset
        if self.exhausted:
            wait_time = min(self.exhausted.values()) - now
            raise Exception(f"All keys exhausted. Wait {wait_time:.0f}s")
        
        return None
    
    def mark_exhausted(self, key, reset_time):
        # Mark key as exhausted until reset time
        self.exhausted[key] = reset_time

Rate Limit Increase

99.9%

Availability

<1ms

Key Selection

Auto

Failover

Best Practices

Optimize your round robin implementation for production reliability.

Rate Limit Tracking

# Track rate limits from response headers
def handle_response(response, key):
    headers = response.headers
    
    # OpenAI rate limit headers
    remaining = headers.get('x-ratelimit-remaining-requests', 'unknown')
    reset_time = headers.get('x-ratelimit-reset-requests')
    
    # Parse reset time (e.g., "6s" -> 6 seconds from now)
    if remaining == '0' and reset_time:
        wait_seconds = parse_duration(reset_time)
        pool.mark_exhausted(key, time.time() + wait_seconds)
        log(f"Key exhausted, reset in {wait_seconds}s")

Scenario	Recommended Keys	Strategy	Expected Throughput
Development/Testing	1-2 keys	Simple Round Robin	60-120 req/min
Small Production App	3-5 keys	Least Recently Used	180-300 req/min
High Volume Service	10+ keys	Smart Selection	600+ req/min
Enterprise Scale	20+ keys	Weighted + Smart	1200+ req/min

Scale Your LLM Infrastructure

Implement round robin key rotation to handle more requests with existing API keys. Our guides help you get started in minutes.

Implementation Guide View Examples

Related Resources

🔐