Inference Autopilot
Inference Autopilot is an AI agent that continuously optimizes inference infrastructure to reduce latency, increase throughput, and lower total cost of ownership.
Core trick
Feeds deployment configs and telemetry to an LLM that proposes model placement and batching changes, then gates them behind approval and blue-green deploys.
Description
Inference Autopilot is an AI agent that continuously optimizes inference infrastructure to reduce latency, increase throughput, and lower total cost of ownership. It helps teams run multiple models across heterogeneous hardware and cloud environments as traffic patterns and serving requirements change.
Powered by GPT-6 Astra, it turns deployment configurations, workload history, and operational telemetry into actionable optimization plans. It identifies bottlenecks, evaluates deployment alternatives, and explains the benefits and tradeoffs of changes to model placement, capacity, batching, and caching—all within the operator’s performance, reliability, and budget constraints.
Our goal is to close the loop from observation to verified improvement: monitor the fleet, recommend changes, obtain approval, deploy safely, and measure the results. The hackathon prototype demonstrates this workflow through real Astra analysis, interactive fleet exploration, and simulated blue-green deployments.
README excerpt
# Inference Autopilot **An AI agent for continuously optimizing production inference fleets.** Inference Autopilot is being built to run alongside your inference infrastructure and turn deployment configuration, traffic patterns, and operational telemetry into actionable optimization plans. Its goal is to help teams **reduce latency, increase throughput, and lower cost** while protecting model quality and service-level objectives. The product vision spans heterogeneous fleets: multiple models and use cases, different accelerator architectures, and deployments across hyperscalers, neoclouds, and on-premises environments. OpenAI Astra serves as the analyst, reasoning about workload behavior and explaining the tradeoffs behind each recommendation. ## Why Inference Autopilot Inference workloads change throughout the day. A deployment that works well for short interactive requests can struggle with long-context prompts, sustained code generation, or bursts of concurrent agent traffic. Capacity can sit idle in one part of the fleet while another model queues requests or misses its latency target. Inference Autopilot is designed to connect these signals and answer practical questions: - Where should each model run, and how many complete serving replicas does it need? - Would a different accelerator, placement, batching policy, or cache strategy improve service? - Should the fleet consolidate during quiet periods or warm capacity before an expected surge? - When would a differ