Performance

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Key Performance Metrics

• Under 1ms model selection
• Multi-tier caching for instant responses
• Zero LLM overhead in routing pipeline
• Built in Go for maximum performance

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Architecture Advantages

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Ultra-Fast ML Pipeline

• No LLMs in routing: Pure ML classifiers for instant decisions
• Optimized algorithms: Purpose-built for speed over complexity
• Pre-computed embeddings: Classification without real-time inference

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Multi-Tier Caching

L1: Prompt-response cache (microseconds)
L2: Semantic cache (1-2ms)  
L3: Router caches (5-10ms)

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Go-Powered Backend

• Native performance: Compiled binary, no runtime overhead
• Concurrent processing: Thousands of simultaneous requests
• Memory efficient: Minimal garbage collection impact
• Fast startup: Sub-second cold start times

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Real-World Performance

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Throughput

• 10,000+ requests/second sustained
• Linear scaling with additional instances
• No performance degradation under load

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Latency Breakdown

Model Selection:    Under 1ms
Cache Lookup:       Under 1ms
Provider Routing:   Under 1ms
Total Overhead:     Under 3ms

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Caching Hit Rates

• L1 Cache: 40-60% hit rate
• Semantic Cache: 20-30% hit rate
• Combined: 60-80% cache efficiency

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Optimization Features

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Smart Preprocessing

• Request classification happens in parallel
• Provider health checks cached and updated asynchronously
• Route decisions pre-computed when possible

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Efficient Data Structures

• In-memory provider models for instant lookup
• Optimized JSON parsing with zero-copy where possible
• Connection pooling to all providers

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Resource Management

• Minimal memory footprint per request
• CPU-efficient algorithms optimized for routing decisions
• Graceful degradation under extreme load

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Benchmarks vs Alternatives

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Monitoring Performance

• Request latency percentiles (P50, P95, P99)
• Cache hit rates across all tiers
• Provider response times
• Throughput and error rates