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Performance Optimization

AI Controller includes various features and capabilities designed to optimize performance, reduce latency, and improve overall system efficiency.

Performance Optimization Overview

AI Controller's performance optimization capabilities focus on several key areas:

  • Latency Reduction: Minimizing response times for LLM requests
  • Throughput Maximization: Handling more concurrent requests
  • Resource Efficiency: Optimizing CPU, memory, and network usage
  • Scalability: Maintaining performance as usage grows
  • Reliability: Ensuring consistent performance under load

Understanding Performance Factors

Several factors affect AI Controller performance:

Request Flow Latency Components

The total latency for an AI Controller request includes:

Component Typical Range Contributing Factors
Request processing 5-50ms Request size, validation complexity
Authentication 5-50ms Auth method, caching effectiveness
Rules evaluation 10-100ms Number of rules, complexity
Provider selection 1-10ms Routing complexity
Cache lookup 5-50ms Cache size, SQL database performance
Provider API call 500-5000ms Provider speed, model size, request complexity
Response processing 5-50ms Response size, transformations

The external provider API call typically accounts for 80-95% of the total latency, making caching one of the most effective optimization strategies. For a detailed view of how requests flow through the system, see Data Flow.

System Resource Requirements

AI Controller resource usage varies based on deployment size:

Deployment Size Concurrent Requests CPU Cores Memory Redis Cache Disk Space
Small 1-10 2-4 4-8 GB 2-4 GB 10-50 GB
Medium 10-50 4-8 8-16 GB 4-16 GB 50-200 GB
Large 50-200 8-16 16-32 GB 16-64 GB 200-500 GB
Enterprise 200+ 16+ 32+ GB 64+ GB 500+ GB

Performance Optimization Features

Caching System

The caching system is AI Controller's primary performance optimization feature. For details on how caching fits into the overall architecture, see Architecture Overview.

  • Reduced Latency: Cache hits bypass slow external API calls
  • Improved Throughput: More requests can be handled concurrently
  • Cost Reduction: Fewer provider API calls mean lower costs
  • Consistency: Same request always yields same response
  • Reliability: System continues functioning during provider outages

For more information, see Response Caching.

Load Distribution

AI Controller can distribute load across multiple LLM providers:

  • Provider Redundancy: Continue operation if one provider is down
  • Cost Optimization: Route requests to most cost-effective provider
  • Performance Balancing: Utilize fastest available provider
  • Capability Matching: Select provider based on request requirements

Scalable Architecture

AI Controller is designed for scalability:

  • Horizontal Scaling: Add more instances to handle increased load
  • Resource Optimization: Efficient use of system resources
  • Caching Tier: Separate, scalable Redis-based caching layer
  • Database Optimization: Performance-tuned database queries

Network Optimization

AI Controller implements various network optimizations:

  • Connection Pooling: Reuse connections to providers
  • Keep-Alive: Maintain persistent connections
  • Timeout Management: Intelligent handling of slow responses
  • Retry Logic: Automatic retry of failed requests with backoff

Performance Benchmarks

The table below shows typical performance improvements with AI Controller caching:

Metric Without Cache With Cache (50% Hit Rate) With Cache (80% Hit Rate)
Average Response Time 1,500ms 775ms 310ms
Requests per Second 20 38 71
API Costs $100/day $50/day $20/day
Provider API Calls 100,000/day 50,000/day 20,000/day

Note: Actual performance will vary based on hardware, network configuration, and request patterns.

Updated: 2025-05-15