How to Monitor AI Model Uptime and API Availability

Monitoring AI model uptime requires a different approach than traditional web services. Because LLM inference is probabilistic and resource-intensive, a simple 200 OK status code is insufficient; you must also track latency, error rates, and token throughput to detect silent failures or model degradation. In this guide, we will implement a robust monitoring wrapper to track the health of your AI integration in real-time. You will learn how to instrument your inference calls, implement interval-based health checks, and structure your logs for actionable alerting.

Prerequisites

• 1. Python 3.9 or higher installed on your system. 2. A valid AI provider API key with sufficient permissions. 3. Access to a logging service or observability platform (e.g., Datadog, Prometheus, or a structured log file). 4. Basic familiarity with the OpenAI Python SDK and error handling patterns.

Steps

1. 1

   Define a Lightweight Health-Check Prompt

   Create a minimal, low-latency prompt designed specifically for uptime verification. This query should be small enough to execute quickly, allowing you to isolate infrastructure latency from model complexity.

2. 2

   Instrument Your Inference Wrapper

   Wrap your standard SDK client call in a function that captures performance metadata. Ensure you record the start time, completion time, and any exceptions thrown by the API client to differentiate between network issues and model errors.

3. 3

   Implement Interval-Based Polling

   Use a simple background task or cron job to trigger your health-check function at fixed intervals, such as every 60 seconds. This creates a consistent heartbeat signal that helps you distinguish between intermittent hiccups and sustained downtime.

4. 4

   Capture and Log Key Metrics

   Output your metrics in a structured format like JSON to your logging pipeline. Include the model name, response time (latency), error code if applicable, and a boolean flag indicating whether the inference was successful.

5. 5

   Configure Failure Thresholds and Alerting

   Set alert conditions in your observability tool that trigger when the success rate drops below 95% over a five-minute window or when latency exceeds a specific percentile threshold. This prevents noise while ensuring you are notified immediately of outages.

Code

import time
import logging
from openai import OpenAI

client = OpenAI(base_url="https://api.select.ax/v1", api_key="your_api_key")

def monitor_uptime():
    start_time = time.time()
    try:
        response = client.chat.completions.create(
            model="smart-select",
            messages=[{"role": "user", "content": "ping"}]
        )
        latency = time.time() - start_time
        logging.info(f"Health check passed. Latency: {latency:.2f}s")
    except Exception as e:
        logging.error(f"Health check failed: {str(e)}")

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    while True:
        monitor_uptime()
        time.sleep(60)

Pro tips

Visual guide

Route your models intelligently

Use one API key for routing, fallback, and cost control across model providers.