One thing I’d add is that most teams underestimate how hard observability becomes once they move past the “single GPU demo” phase. Logging token counts, caching hits, and model-version drift saves way more money and headaches than people expect. Scaling LLMs isn’t just about bigger GPUs, it’s about tighter feedback loops.
One thing I’d add is that most teams underestimate how hard observability becomes once they move past the “single GPU demo” phase. Logging token counts, caching hits, and model-version drift saves way more money and headaches than people expect. Scaling LLMs isn’t just about bigger GPUs, it’s about tighter feedback loops.