Category
mlops
4 articles across 4 sub-topics
Ai Agents(1)
Build vs Buy: Deploying Your Own LLM vs Using ChatGPT, Gemini, and Claude APIs
TLDR: Use the API until you hit $10K/month or a hard data privacy requirement. Then add a semantic cache. Then evaluate hybrid routing. Self-hosting full model serving is only cost-effective at > 50M tokens/day with a dedicated MLOps team. The build ...
•34 min read
Ai(1)

LLM Model Naming Conventions: How to Read Names and Why They Matter
TLDR: LLM names encode practical decisions: model family, size, training stage, context window, format, and quantization level. If you can decode naming conventions, you can avoid costly deployment mistakes and choose the right checkpoint faster. �...
•12 min read
