AI API Status & Outage Guide for Developers

AI API Status — Current Monitored Providers

Understanding the Difference: API Outage vs. Web App Outage

One of the most common sources of confusion for developers using AI APIs is the distinction between an API outage and a web application outage. These two failure modes look very different and require different responses.

This separation is important: if ChatGPT’s web interface is down, the OpenAI API may still be responding normally. Test your API integration directly rather than assuming that a web app outage also affects the API, and vice versa.

Common AI API Error Codes and What They Mean

Rate Limits vs. True Downtime — How to Tell the Difference

Rate limiting is the most commonly mistaken-for-downtime scenario. Here is how to reliably distinguish between a rate limit and a true API outage:

1. 1Check the HTTP status code — 429 is a rate limit; 503 is service unavailable. These require different responses.
2. 2Read the response body — rate limit responses include details about your current usage and limits; outage responses are typically generic or empty.
3. 3Check the Retry-After header — rate limit responses often include this header specifying when you can retry. Downtime responses typically do not include it.
4. 4Try with a different API key — if the error follows your key but not another key, it’s account-specific (rate limit or billing). If it affects all keys, it’s likely a genuine service issue.
5. 5Check your provider’s usage dashboard — if your usage shows you near your RPM (requests per minute) or TPM (tokens per minute) limit, that’s your rate limit — not an outage.

Best Practices for Handling AI API Downtime in Production

Production applications that depend on AI APIs need resilience strategies to handle both rate limits and true downtime gracefully. Building for failure is not pessimism — it is engineering reality when working with external AI services.

1. 1Implement exponential backoff with jitter — on 429 or 503 responses, wait and retry with increasing delays. Add random jitter to avoid synchronized retry storms from multiple clients.
2. 2Use a circuit breaker pattern — if multiple consecutive requests fail, stop sending requests temporarily and serve a degraded response. Resume after a cool-down period.
3. 3Implement a fallback model — if your primary AI provider is down, have a secondary provider configured. For example, fall back from GPT-4o to Claude if OpenAI is degraded, or vice versa.
4. 4Queue time-insensitive requests — for non-real-time tasks like batch content generation, queue requests and process them when the API recovers rather than failing immediately.
5. 5Set appropriate request timeouts — AI API response times vary widely. Set generous timeouts (30-120 seconds for complex requests) but do not set them to infinity — hanging requests tie up resources.
6. 6Monitor your API error rates — alert on unusual 5xx error rates from your AI provider. A sudden spike in 503s is an early indicator of an emerging outage.

Authentication Errors vs. Service Issues

Authentication failures (HTTP 401 Unauthorized) are frequently confused with service outages, particularly when an API key suddenly stops working after previously working correctly. Common causes include:

What to Do During an AI API Outage

When you have confirmed a genuine AI API outage (not rate limits, not auth issues), the recommended approach is:

1. Stop sending requests. Continuing to hammer a degraded API endpoint consumes your rate limit quota and contributes to the load on a recovering system. Implement a circuit breaker to stop requests immediately when an outage is detected.

2. Switch to a fallback provider if critical. If your application cannot tolerate downtime, switch to a secondary AI provider. Design your AI integration layer to be provider-agnostic where possible, with model selection as a configuration parameter.

3. Queue non-real-time work. Store requests that don’t need immediate processing in a queue (SQS, Redis, etc.) and process them once the outage resolves.

4. Monitor and alert your team. If your product depends on an AI API, integrate the monitoring of that API into your alerting infrastructure. An AI API going down should trigger the same kind of alerts as your own service going down.

❓ AI API Status — Frequently Asked Questions

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