I’m running into a problem when calling Azure OpenAI GPT-4 multiple times from my Python script. Each request uses around 5000 tokens total (prompt plus response). The first API call works perfectly fine, but the second one always fails with this error message:
"The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes,"
I thought maybe it was hitting rate limits since GPT-4 has token per minute restrictions, so I added a 60 second delay between calls. But I’m still getting the exact same error on every second request. Has anyone run into this issue before? What could be causing this deployment error only on subsequent calls?
Had this exact problem with Azure OpenAI. The error’s misleading - your deployment exists, but something’s corrupting your connection config between calls. Check if you’re properly reinitializing your client for each request or just reusing the same instance. I was tweaking config parameters after the first call, which broke subsequent requests. Try creating a fresh client for each API call to isolate what’s happening. Could also be your auth token getting invalidated between requests. Azure’s picky about token refresh cycles, especially with longer processing times. Don’t cache credentials in a way that might kill authentication on the second attempt.
This looks like a token management issue, not a deployment problem. Azure OpenAI’s per-minute quotas reset weirdly, and their error messages are garbage.
I dealt with the same Azure OpenAI rate limiting hell until I automated everything. Azure throws “deployment errors” when you’re actually hitting quota limits or the service needs time to process your last request.
You need smart retry logic with exponential backoff, token counting before requests, and queue management for multiple calls. Building this yourself sucks and takes forever.
I automate all my Azure OpenAI stuff now. Proper request queuing, automatic retries with smart delays, and token tracking that works with Azure’s broken quota system. Takes 5 minutes to set up and these fake deployment errors disappear.
Bonus: you can add fallback logic to different models or deployments when one craps out. Way better than trying to decode Azure’s cryptic errors.
Azure’s error messages are garbage and this “deployment not found” thing happens when their request routing gets confused.
I spent weeks debugging similar Azure OpenAI issues before giving up on manual management. The problem’s Azure’s load balancing and session management between requests. Your deployment exists but Azure somehow loses track of it.
Instead of fighting Azure’s quirks, I automated everything. Proper request orchestration handles all the weirdness automatically - smart routing, session management, and automatic failover when Azure gets confused about deployments.
You get retry logic that actually works with Azure’s broken error handling, plus automatic fallback to different deployment instances when one gets lost. No more cryptic deployment errors or manual debugging.
Saves me hours every week not babysitting Azure’s inconsistent API behavior. Set it up once and forget about these deployment routing issues.
weird issue - i’ve hit this when azure randomly reassigns your deployment endpoint mid-session. usually happens under heavy load or when their load balancer freaks out. hard-code your exact endpoint url instead of letting the sdk build it automatically. also double-check you’re not bouncing between availability zones - azure might route you to instances where your deployment hasn’t replicated yet.
Check your deployment name config - I’ve seen this when the deployment name gets corrupted in memory between requests. Azure OpenAI is super picky about exact naming, and sometimes variables get modified during script execution without you realizing it. Print your deployment name, endpoint, and API version right before each call to make sure they’re identical. Also check if you’re accidentally switching between different resource instances or regions. I had the same issue where my second request hit a different Azure region than the first due to variable scope problems. The deployment existed in one region but not the other, which threw that misleading error.