Load Balancing

Overview

Who is this for? Developers building high-scale AI applications who need to optimize performance, manage costs, and distribute load efficiently across multiple AI providers and models.

What you'll achieve: Implement intelligent load balancing strategies that automatically distribute requests across providers based on performance, cost, availability, and custom business rules for optimal resource utilization.

The AI Proxy provides sophisticated load balancing mechanisms that distribute requests across multiple providers and models based on configurable strategies, ensuring optimal performance, cost efficiency, and resource utilization.

Load Balancing Strategies

Round Robin

Distributes requests evenly across all available providers in rotation.

Use Case: Equal distribution when all providers have similar performance characteristics.

Benefits:

Simple and predictable distribution
Even utilization across providers
Good for testing and development

Weighted Round Robin

Distributes requests based on assigned weights to each provider.

Use Case: When providers have different capacities or performance characteristics.

Benefits:

Proportional load distribution
Accounts for provider differences
Flexible capacity management

Least Connections

Routes requests to the provider currently handling the fewest active requests.

Use Case: When request processing times vary significantly.

Benefits:

Prevents overloading busy providers
Adaptive to real-time conditions
Optimal for varying workloads

Performance-Based Routing

Routes based on real-time performance metrics like latency and success rates.

Use Case: When performance optimization is critical.

Benefits:

Automatically routes to fastest providers
Adapts to performance changes
Maintains optimal user experience

Cost-Optimized Routing

Prioritizes providers based on cost efficiency and budget constraints.

Use Case: When cost optimization is the primary concern.

Benefits:

Minimizes operational costs
Respects budget limitations
Balances cost with quality

Configuration Examples

Basic Round Robin

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Weighted Distribution

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Performance-Based Routing

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Cost-Optimized Balancing

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Advanced Load Balancing

Multi-Dimensional Routing

Combine multiple factors for intelligent routing decisions.

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Geographic Load Balancing

Route based on user location and provider regions.

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Time-Based Routing

Adjust routing based on time zones and provider availability.

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Implementation Examples

Node.js Load Balancer

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Python Load Balancing Client

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React Load Balancing Hook

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Load Balancing Algorithms

Consistent Hashing

Ensures requests from the same user/session consistently route to the same provider.

Benefits:

Session affinity maintained
Reduces context switching overhead
Predictable routing for debugging

Adaptive Weighted Routing

Dynamically adjusts weights based on real-time performance metrics.

Algorithm:

Monitor provider response times and error rates
Calculate performance scores
Adjust routing weights automatically
Re-evaluate and update periodically

Health-Based Routing

Excludes unhealthy providers from load balancing pool.

Health Checks:

Response time thresholds
Error rate monitoring
Availability verification
Custom health metrics

Performance Optimization

Latency-Based Routing

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Throughput Optimization

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Quality Score Routing

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Cost Management

Budget-Aware Routing

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Cost Per Token Optimization

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Provider Tier Management

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Monitoring and Analytics

Load Balancing Metrics

Track key performance indicators for load balancing effectiveness:

Request Distribution: Percentage of requests per provider
Response Times: Average and percentile response times by provider
Success Rates: Success/failure rates across providers
Cost Efficiency: Cost per request/token by provider
Utilization: Provider capacity utilization metrics

Real-Time Dashboard

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Performance Analytics

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Cost Analytics

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Provider Management

Dynamic Provider Pool

Automatically manage provider availability and health.

Features:

Automatic provider discovery
Health status monitoring
Dynamic pool updates
Graceful provider removal

Provider Scoring

Rate providers based on multiple criteria:

Performance Score: Based on latency and throughput
Reliability Score: Based on uptime and error rates
Cost Score: Based on pricing efficiency
Quality Score: Based on output quality metrics

Capacity Management

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High Availability Patterns

Multi-Region Load Balancing

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Circuit Breaker Integration

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Graceful Degradation

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Enterprise Features

Custom Routing Logic

Implement business-specific routing rules:

Tenant-Based Routing: Route based on customer tiers
Content-Type Routing: Route based on request content
SLA-Based Routing: Route based on service level agreements
Compliance Routing: Route based on regulatory requirements

A/B Testing Support

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Blue-Green Deployments

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Best Practices

Configuration Guidelines

Start Simple: Begin with round robin and add complexity as needed
Monitor Continuously: Track metrics to optimize routing decisions
Plan for Failure: Include fallback strategies in load balancing
Test Thoroughly: Validate load balancing under various conditions

Performance Tuning

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Security Considerations

API Key Management: Secure provider credentials
Request Isolation: Prevent cross-contamination between requests
Audit Logging: Log routing decisions for compliance
Rate Limiting: Implement per-provider rate limits

Troubleshooting

Common Issues

Uneven Distribution
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Performance Degradation
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Cost Overruns
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Debugging Tools

Request Tracing: Track request routing decisions
Performance Profiling: Identify bottlenecks in routing
Load Testing: Validate load balancing under stress
Metric Analysis: Analyze historical routing patterns

Integration Patterns

API Gateway Integration

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Microservices Architecture

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Kubernetes Deployment

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Scaling Considerations

Horizontal Scaling

Multi-Instance Load Balancing: Coordinate across multiple proxy instances
Shared State Management: Synchronize routing decisions
Distributed Metrics: Aggregate metrics across instances

Vertical Scaling

Resource Optimization: Optimize memory and CPU usage
Connection Pooling: Manage provider connections efficiently
Caching: Cache routing decisions and provider metadata

Next Steps

Fallbacks: Combine with fallback strategies
Monitoring: Monitor load balancing performance
Retries & Error Handling: Handle errors in load-balanced requests