AI Latency Budgeting & Reactive Scaling Framework

A Production Performance Reference by Vipin Kumar

Welcome to the official documentation for the AI Latency Budgeting framework.

🔗 GitHub Repository: View on GitHub

🚀 Overview

This project presents a production-grade latency budgeting model and reactive scaling architecture for AI systems using p50, p95, and p99 latency signals.

It helps you:

Break down latency across pipeline stages
Define end-to-end SLOs
Handle tail latency (p99)
Trigger scaling and apply backpressure
Safely rollback under degradation
Optimize Infrastructure Costs: Prevent over-provisioning by using high-fidelity p99 signals to scale only when mathematically necessary.

📊 Architecture & Concepts

👉 View full architecture and latency model in the GitHub repository

📄 Technical Reference

The complete production reference, including latency modeling, SLO design, and tail-latency strategies, is available below:

👉 Download Full Production Reference