Load balancing is crucial for distributing AI model requests across multiple providers, ensuring optimal performance, cost efficiency, and reliability.
Why Load Balancing Matters for AI Models
- Performance: Distribute load to prevent bottlenecks
- Cost Optimization: Route requests to cost-effective providers
- Reliability: Avoid overwhelming single providers
- Scalability: Handle increased traffic efficiently
Load Balancing Strategies
1. Round-Robin Load Balancing
Distribute requests evenly across all available providers:
class RoundRobinLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.current_index = 0
self.lock = asyncio.Lock()
async def get_next_provider(self) -> str:
"""Get next provider in round-robin fashion"""
async with self.lock:
provider = self.providers[self.current_index]
self.current_index = (self.current_index + 1) % len(self.providers)
return provider
async def generate_text(self, prompt: str, model: str) -> str:
"""Generate text using round-robin load balancing"""
provider = await self.get_next_provider()
return await self._call_provider(provider, prompt, model)
## Usage
providers = ['openai', 'anthropic', 'google', 'together']
balancer = RoundRobinLoadBalancer(providers)
result = await balancer.generate_text("Hello, world!")2. Weighted Round-Robin
Assign different weights to providers based on capacity or cost:
class WeightedRoundRobinLoadBalancer:
def __init__(self, providers_with_weights: Dict[str, int]):
self.providers = []
self.weights = []
# Expand providers based on weights
for provider, weight in providers_with_weights.items():
for _ in range(weight):
self.providers.append(provider)
self.current_index = 0
self.lock = asyncio.Lock()
async def get_next_provider(self) -> str:
"""Get next provider with weighted distribution"""
async with self.lock:
provider = self.providers[self.current_index]
self.current_index = (self.current_index + 1) % len(self.providers)
return provider
## Usage - OpenAI gets 3x the traffic, others get 1x
providers_with_weights = {
'openai': 3,
'anthropic': 1,
'google': 1,
'together': 1
}
balancer = WeightedRoundRobinLoadBalancer(providers_with_weights)3. Least Connections Load Balancing
Route requests to the provider with the fewest active connections:
class LeastConnectionsLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.active_connections = {provider: 0 for provider in providers}
self.lock = asyncio.Lock()
async def get_next_provider(self) -> str:
"""Get provider with least active connections"""
async with self.lock:
# Find provider with minimum connections
min_connections = min(self.active_connections.values())
available_providers = [
p for p, conns in self.active_connections.items()
if conns == min_connections
]
# If multiple providers have same connections, choose randomly
provider = random.choice(available_providers)
self.active_connections[provider] += 1
return provider
async def release_connection(self, provider: str):
"""Release a connection when request completes"""
async with self.lock:
self.active_connections[provider] = max(0, self.active_connections[provider] - 1)
async def generate_text(self, prompt: str, model: str) -> str:
"""Generate text using least connections load balancing"""
provider = await self.get_next_provider()
try:
result = await self._call_provider(provider, prompt, model)
return result
finally:
await self.release_connection(provider)4. Response Time Load Balancing
Route requests to the fastest responding provider:
import statistics
import time
class ResponseTimeLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.response_times = {provider: [] for provider in providers}
self.lock = asyncio.Lock()
async def get_next_provider(self) -> str:
"""Get provider with best average response time"""
async with self.lock:
provider_scores = {}
for provider in self.providers:
times = self.response_times[provider]
if times:
avg_time = statistics.mean(times)
provider_scores[provider] = avg_time
else:
provider_scores[provider] = float('inf')
# Return provider with lowest average response time
return min(provider_scores, key=provider_scores.get)
async def record_response_time(self, provider: str, response_time: float):
"""Record response time for a provider"""
async with self.lock:
self.response_times[provider].append(response_time)
# Keep only last 100 measurements
if len(self.response_times[provider]) > 100:
self.response_times[provider].pop(0)
async def generate_text(self, prompt: str, model: str) -> str:
"""Generate text using response time load balancing"""
provider = await self.get_next_provider()
start_time = time.time()
try:
result = await self._call_provider(provider, prompt, model)
return result
finally:
response_time = time.time() - start_time
await self.record_response_time(provider, response_time)5. Cost-Based Load Balancing
Route requests to the most cost-effective provider:
class CostBasedLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.cost_per_token = {
'openai': {'gpt-4': 0.03, 'gpt-3.5-turbo': 0.002},
'anthropic': {'claude-3-opus': 0.015, 'claude-3-sonnet': 0.003},
'google': {'gemini-pro': 0.001},
'together': {'llama-2-7b': 0.0003}
}
def estimate_tokens(self, prompt: str) -> int:
"""Estimate number of tokens in prompt"""
return len(prompt.split()) * 1.3
def calculate_cost(self, provider: str, model: str, prompt: str) -> float:
"""Calculate estimated cost for a request"""
tokens = self.estimate_tokens(prompt)
cost_per_1k = self.cost_per_token.get(provider, {}).get(model, 0.01)
return (tokens / 1000) * cost_per_1k
async def get_next_provider(self, prompt: str, model: str) -> str:
"""Get most cost-effective provider"""
costs = {}
for provider in self.providers:
cost = self.calculate_cost(provider, model, prompt)
costs[provider] = cost
return min(costs, key=costs.get)
async def generate_text(self, prompt: str, model: str) -> str:
"""Generate text using cost-based load balancing"""
provider = await self.get_next_provider(prompt, model)
return await self._call_provider(provider, prompt, model)Advanced Load Balancing Features
1. Health-Aware Load Balancing
Combine load balancing with health monitoring:
class HealthAwareLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.health_status = {provider: True for provider in providers}
self.response_times = {provider: [] for provider in providers}
self.error_rates = {provider: 0.0 for provider in providers}
async def get_next_provider(self) -> str:
"""Get healthy provider with best performance"""
healthy_providers = [
p for p in self.providers
if self.health_status[p]
]
if not healthy_providers:
raise Exception("No healthy providers available")
# Score providers based on performance and error rate
provider_scores = {}
for provider in healthy_providers:
avg_response_time = statistics.mean(self.response_times[provider]) if self.response_times[provider] else 1.0
error_rate = self.error_rates[provider]
# Score = 1 / (response_time * (1 + error_rate))
score = 1.0 / (avg_response_time * (1 + error_rate))
provider_scores[provider] = score
return max(provider_scores, key=provider_scores.get)
async def record_metrics(self, provider: str, response_time: float, success: bool):
"""Record performance metrics"""
self.response_times[provider].append(response_time)
if len(self.response_times[provider]) > 100:
self.response_times[provider].pop(0)
# Update error rate
if not success:
self.error_rates[provider] = min(1.0, self.error_rates[provider] + 0.01)
else:
self.error_rates[provider] = max(0.0, self.error_rates[provider] - 0.001)
# Mark as unhealthy if error rate is too high
if self.error_rates[provider] > 0.1:
self.health_status[provider] = False2. Adaptive Load Balancing
Dynamically adjust load balancing based on real-time metrics:
class AdaptiveLoadBalancer:
def __init__(self, providers: List[str]):
self.providers = providers
self.metrics = {provider: {
'response_time': [],
'error_rate': 0.0,
'throughput': 0,
'cost_per_request': 0.0
} for provider in providers}
self.weights = {provider: 1.0 for provider in providers}
async def update_weights(self):
"""Update provider weights based on performance"""
for provider in self.providers:
metrics = self.metrics[provider]
# Calculate performance score
avg_response_time = statistics.mean(metrics['response_time']) if metrics['response_time'] else 1.0
error_rate = metrics['error_rate']
throughput = metrics['throughput']
# Performance score = throughput / (response_time * (1 + error_rate))
performance_score = throughput / (avg_response_time * (1 + error_rate))
# Update weight based on performance
self.weights[provider] = max(0.1, performance_score)
async def get_next_provider(self) -> str:
"""Get provider using adaptive weights"""
# Update weights periodically
await self.update_weights()
# Select provider based on weights
total_weight = sum(self.weights.values())
random_value = random.uniform(0, total_weight)
current_weight = 0
for provider, weight in self.weights.items():
current_weight += weight
if random_value <= current_weight:
return provider
return self.providers[0] # FallbackImplementation Example
class ProductionAILoadBalancer:
def __init__(self):
self.providers = ['openai', 'anthropic', 'google', 'together']
self.balancer = HealthAwareLoadBalancer(self.providers)
self.circuit_breakers = {p: CircuitBreaker() for p in self.providers}
async def generate_text(self, prompt: str, model: str, strategy: str = 'health_aware') -> str:
"""Generate text with configurable load balancing strategy"""
if strategy == 'round_robin':
balancer = RoundRobinLoadBalancer(self.providers)
elif strategy == 'least_connections':
balancer = LeastConnectionsLoadBalancer(self.providers)
elif strategy == 'response_time':
balancer = ResponseTimeLoadBalancer(self.providers)
elif strategy == 'cost_based':
balancer = CostBasedLoadBalancer(self.providers)
else:
balancer = self.balancer # Default to health-aware
provider = await balancer.get_next_provider()
# Use circuit breaker for the selected provider
circuit_breaker = self.circuit_breakers[provider]
async def call_provider():
return await self._call_provider(provider, prompt, model)
return await circuit_breaker.call(call_provider)
## Usage
balancer = ProductionAILoadBalancer()
## Use different strategies
result1 = await balancer.generate_text("Hello", "gpt-4", "round_robin")
result2 = await balancer.generate_text("Hello", "gpt-4", "cost_based")
result3 = await balancer.generate_text("Hello", "gpt-4", "health_aware")Best Practices
- Monitor Performance: Track response times, error rates, and costs
- Use Health Checks: Don’t route to unhealthy providers
- Implement Circuit Breakers: Prevent cascading failures
- Test Different Strategies: Choose the best strategy for your use case
- Adapt to Changes: Update load balancing based on real-time metrics
- Handle Failures Gracefully: Always have a fallback plan
Conclusion
Load balancing for AI models is essential for optimal performance and reliability. Start with simple round-robin and gradually implement more sophisticated strategies like health-aware and adaptive load balancing as your needs grow.
The key is to monitor performance metrics and adjust your load balancing strategy based on real-world usage patterns.