How I Scaled an OpenAI Chatbot to 1,200 Concurrent Users on Kubernetes

Deploying a GPT-4 chatbot is easy. Making it survive real traffic without burning your API budget is where most teams fail.
These are the 4 things that made the difference when we hit 1,200 concurrent users:
• Redis session management — limits context to last 10 messages, cuts token costs 35%
• Circuit breaker with exponential backoff — stops cascading 429 errors when you hit OpenAI rate limits
• Response caching — 40% of questions in support chatbots are identical, Redis answers them in <10ms instead of 4s
• Kubernetes HPA — autoscales from 3 to 10 pods automatically when CPU hits 70%
Real numbers: 50k messages/month with this stack costs ~$270. Without caching, $300 in OpenAI alone.
Full architecture with all the code:
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