Inference Performance

When deploying Language Models (LLMs) in production, performance is a critical consideration. UsageGuard is designed to provide powerful features while maintaining high performance and low latency. This guide explores the performance characteristics and optimization strategies for LLM inference with UsageGuard.

Performance Characteristics

UsageGuard is engineered for optimal performance while providing advanced features. Our benchmarks demonstrate the system's efficiency:

First Request (ms):   1,643
Requests:             18,694
Bad responses:        0
Mean latency (us):    80,288
Max latency (us):     2,516,643
Requests/sec:         1,262
Requests/sec (max):   5,649

The average latency introduced by UsageGuard is minimal, typically ranging from 50-100ms. For most applications, this slight increase in latency is negligible compared to the total inference time of LLM requests.

Note: The first request may have higher latency due to connection establishment and potential cold starts. Subsequent requests are significantly faster.

Performance Optimization

Understanding how different features affect performance can help you optimize your implementation:

Core Features (Minimal Impact)

• Unified API Access: Negligible overhead
• Model Switching: No additional latency
• Basic Request Routing: Microsecond-level processing

Advanced Features (Variable Impact)

• Request Logging: Can add 10-50ms when storing full request/response bodies
• Content Moderation: Processing time varies with input size
• PII Detection: Scales with content complexity

Optimization Strategies

To achieve optimal performance with UsageGuard:

1. Selective Logging: Enable detailed logging only when necessary
2. Request Streaming: Use streaming for large responses to reduce latency
3. Connection Pooling: Maintain persistent connections for better performance
4. Batch Processing: Combine multiple requests when appropriate

Warning: Features that require request buffering should be used judiciously. Consider enabling them only for specific use cases or during debugging phases.

Conclusion

UsageGuard delivers powerful features while maintaining high performance. By understanding these performance characteristics and following optimization strategies, you can ensure optimal performance for your LLM applications.

Ready to optimize your LLM integration? Check out our Quickstart Guide to begin implementing these performance strategies.