azure-openai error: The server had an error processing your request - GPT-5.2-chat

The behavior described matches transient service-side or capacity issues rather than an application bug, especially given:

• The errors started suddenly without changes on the application side.
• They are intermittent and correlate with peak hours.
• Requests fail with 408/500 and the message “The server had an error processing your request. Please retry your request.”

From the available guidance:

1. Handling 408/500 and intermittent failures

• 408 (timeout) and 5xx errors are treated as transient internal errors and are expected to be retried with exponential backoff.
• The Azure OpenAI client libraries automatically retry connection errors, 408, 429, and >=500 internal errors twice by default with exponential backoff. This applies to both Python and JavaScript SDKs.
• If requests fail unexpectedly, recommended actions include:
  • Implementing or tuning retry logic (increasing max_retries / maxRetries).
  • Retrying with smaller prompts to rule out payload-size issues.

Relevant configuration examples:

• Python:

from openai import OpenAI
client = OpenAI(max_retries=0)  # adjust higher instead of 0 for resilience

client.with_options(max_retries=5).chat.completions.create(
    messages=[{"role": "user", "content": "When was Microsoft founded?"}],
    model="gpt-4o",
)

• JavaScript/TypeScript:

const client = new OpenAI({
  maxRetries: 0, // default is 2; increase for more resilience
});

await client.chat.completions.create(
  { messages: [{ role: 'user', content: '...' }], model: '' },
  { maxRetries: 5 },
);

2. 500 internal errors and sampling parameters For some 500 errors related to model output (for example, invalid Unicode or unexpected special tokens), recommended mitigations include:

• Reduce temperature to < 1.
• Ensure presence/frequency penalties and logit biases are at default values.
• Optionally set top_p to a lower, non-default value to reduce sampling of low-probability tokens.
• Always use a client with retry logic and reattempt failed calls.

3. 408/400 and charging behavior

• 408 timeouts and some 400 errors (for example, content filter or input limit) can still incur charges because the service performed processing even though the status code is not 200.
• 401 and 429 errors do not incur charges because the service does not perform processing in those cases.

4. Global Standard, region, and capacity The provided material does not list specific, current capacity incidents or region-specific issues for GPT-5.x or East US 2, and does not explicitly state that this pattern is “expected” for Global Standard deployments under peak load.

However, based on similar guidance for other Azure AI services:

• When intermittent timeouts and 5xx errors appear clustered in time and region, it is consistent with transient regional or capacity issues.
• Recommended actions in such cases are:
  • Check Azure Status and Service Health in the Azure portal for incidents in the target region.
  • If no incident is reported, test the same workload in a different region to see if the behavior is region-specific.

5. Whether switching region or deployment type helps From the available guidance for other Azure AI services and regional issues:

• If the issue is regional (for example, only in West US 2 or a specific region), creating or using a resource in another region can restore normal behavior.
• This pattern is used as a diagnostic step: if another region works normally under the same load, the problem is likely regional capacity or service-side behavior.

Given that, for this scenario:

• It is reasonable to:
  1. Ensure robust retry logic (including for 408/429/5xx) with exponential backoff.
  2. Reduce sampling randomness (temperature, top_p) and reset penalties/logit bias to defaults.
  3. Test the same deployment configuration in another supported region to see if reliability improves.
  4. Monitor Azure Status and Service Health for East US 2 for any incidents affecting Azure OpenAI.

If the same pattern persists across regions with proper retry logic and conservative sampling parameters, opening a support case with request IDs (like the one provided in the error message) is the next step so the service team can investigate backend issues.

References:

• Azure OpenAI frequently asked questions
• Azure OpenAI supported programming languages (programming-language-python)
• Azure OpenAI supported programming languages (programming-language-javascript)
• Azure OpenAI On Your Data
• Use the GPT Realtime API for speech and audio (programming-language-javascript)
• Document Intelligence classification and extraction requests timing out - Microsoft Q&A
• Azure Deployment Takes 70 Minutes to Fail with Obscure Error - Microsoft Q&A
• Unable to deploy resources, can't view Quotas or request increases - Microsoft Q&A