CommunityPostmortem-style writeup of launching an LLM-backed feature. The model wasn't the problem — rate limiting, streaming backpressure, and prompt injection were. Sharing fixes and the monitoring we wish we'd had on day one. 1. The Setup We buil
Provider outages and rate-limits happen daily. We implemented a dynamic sliding-window health score that automatically reroutes LLM inference calls between Anthropic, OpenAI, DeepSeek, and Google without dropping WebSocket client streams. t
Here is our production GitOps setup for managing high-availability GPU inference nodes across multiple Kubernetes clusters using KEDA and the NVIDIA GPU Operator. Key Components 1. Node Provisioning: Spot GPU instances with Karpenter on AWS