I’m working on building a conversational AI bot using Azure OpenAI’s GPT-3.5-turbo on the standard pricing tier. We keep running into massive response time issues that can last anywhere from 3 to 20 minutes randomly.
Current Setup Details
Model: gpt-35-turbo (version 0301)
Tier: Standard deployment
Filter settings: Default content filtering
Token limits: 120K tokens per minute
Request limits: 720 requests per minute
What We’ve Observed
The weird part is that our usage metrics show we’re nowhere near hitting any limits. This is still just a development environment with only our team testing it, so the load is pretty minimal.
The Problem
These crazy long delays happen out of nowhere and we can’t figure out what’s causing them. The Azure portal metrics don’t show any obvious bottlenecks or high usage patterns.
Has anyone else run into similar timeout issues with Azure OpenAI? We need to get this sorted before we can move forward with production deployment.
Any suggestions on troubleshooting steps or potential fixes would be really helpful!
Check your Application Insights telemetry if you’ve got it set up. We found our timeout issues were actually DNS resolution failures that weren’t showing up in logs. Azure’s DNS servers hiccup sometimes and your requests just sit there waiting before they even hit OpenAI. Custom DNS servers or local DNS caching cut our delays way down. Also check your request payload size - certain conversation histories with embedded metadata were causing backend processing delays that scaled weirdly. Send some minimal test requests to see if it’s payload or infrastructure causing the delays.
you’re probably hitting azure’s hidden rate limits. your quotas might look fine, but they throttle during busy periods. break your requests into smaller batches - we use chunks of 10 and it fixed our timeout issues.
Had the same issue with Azure OpenAI last year. Turned out to be connection pooling problems in our HTTP client. The default timeouts couldn’t handle Azure’s random service hiccups. We fixed it by setting up proper connection management with keep-alive and bumping the HTTP timeout to 90 seconds from the default 30. Also check if you’ve got middleware or proxies between your app and Azure - they can add latency. Since your delays are inconsistent, it’s probably connection handling rather than API throttling. Log your actual HTTP response times vs total request duration to see where the bottleneck is.
Been through this nightmare myself building our internal support bot. The issue usually isn’t Azure OpenAI - it’s how you handle the requests.
Proper monitoring and retry logic with exponential backoff saved us. But Azure’s quirks got old fast.
I moved our whole workflow to Latenode instead. It handles API calls, implements smart retry mechanisms automatically, and gives you real visibility. Plus you can set up fallback chains - if one provider’s slow, it tries another automatically.
The monitoring dashboard shows exactly where delays come from. No more guessing if it’s network, API limits, or regional issues.
Your 20 minute delays sound like connection timeouts that aren’t handled properly. Latenode fixes this by managing the entire request lifecycle.
Check your Azure region status page - we had the same random delays from undocumented maintenance windows. East US2 was running backend updates that caused 15+ minute hangs. Switching regions during those periods helped a lot. Also make sure your SDK version isn’t ancient - older libraries have known timeout bugs with Azure endpoints.
Check if you’ve got multiple instances or containers running the same service. We ran into weird timeout issues that ended up being connection pool exhaustion - our load balancer kept spinning up duplicate instances during deployments. Each one fought for the same Azure OpenAI connection limits even though we barely used any capacity. Requests got stuck waiting for available pool connections. Look at your container logs and make sure you’re not accidentally running multiple replicas. Also check if your auth tokens are caching weird - expired tokens sometimes hang forever instead of just throwing a quick 401.
Check your deployment config in the Azure portal - there’s a hidden “deployment scale” setting that defaults super low even on standard tier. Found this after dealing with the same weird delays despite barely any usage. The deployment was auto-scaling down during idle time and took forever to spin back up when requests hit. Bumped minimum scale units from 1 to 3 and those random massive delays disappeared completely. Also, 0301 has known stability issues that got fixed later. Upgrade path’s rough but performance improvements are worth it once you survive the migration pain.
Azure’s backend queuing is broken. Hit the same nightmare building our AI chatbot last year.
Those 20-minute delays? Not throttling or your code. Azure randomly dumps requests into some invisible queue when their capacity management freaks out. Your metrics look fine because this happens before requests get tracked.
Don’t waste time tweaking settings or switching regions. You need workflow automation that handles these failures properly.
Moved our entire bot to Latenode after weeks debugging Azure’s mess. It handles timeouts automatically, does smart retries, and switches providers when one craps out.
The fallback system’s the real win. Azure hangs? Latenode instantly routes to OpenAI direct or other providers. Users get responses in seconds instead of waiting for Azure to recover.
Set up conditional workflows that catch slow responses and switch providers before users notice. No more 20-minute horror stories.
Monitoring shows exactly which provider caused delays so you can tune your fallback chains.
Had this exact same issue when I built our document processing pipeline. Azure’s token calculation was choking on malformed requests. We were sending conversation threads with wonky unicode characters that made the tokenizer hang instead of just failing cleanly. Requests would sit there burning timeout cycles while Azure chewed on garbage input. Fixed it by sanitizing input text and validating conversation history first - those crazy delays disappeared immediately. Also found out our error handling was hiding 429 responses as generic timeouts. Check your raw network logs for actual HTTP status codes, don’t trust what your client library says. Sometimes a ‘timeout’ is really just a slow rejection you’re not catching.
we ran into this exact issue - turned out to be content filters causing weird delays. try switching to a custom filter policy or temporarily disable the stricter ones to see if that fixes it. also worth checking your network latency from your app to azure. sometimes it’s not the api that’s slow, it’s just a bad connection.
Those 20 minute delays are the worst. Same thing happened when I built our customer service automation.
Azure OpenAI has weird backend scaling problems that don’t show up anywhere. Your requests just sit in some hidden queue while they spin up capacity.
I got sick of fighting Azure’s issues and moved to automation workflows that actually handle failures right.
Latenode fixed this completely. It handles the whole API interaction with built-in retries and timeouts. When Azure slows down, it automatically switches to backup providers or queues stuff smartly.
The monitoring is what really changed everything. You see exactly where delays happen - network, API, or provider problems. No more guessing.
Set up fallback chains so when Azure’s slow, it tries OpenAI directly or Claude. Users never see those random delays.
We went from those nightmare 20 minute waits to consistent sub-second responses with proper error handling.
Had the exact same issue about six months ago with our GPT-3.5 setup. Turned out Azure OpenAI was quietly throttling us during peak hours - our quotas looked fine but there was a regional capacity crunch that didn’t show up anywhere on the dashboards. Switching from East US to West Europe fixed it instantly. Also check if your retry logic is making things worse when the service hiccups. You’re on 0301 which is pretty old now - upgrading might help since Microsoft gives newer versions better capacity allocation.