HOW WE SAVED 10S OF THOUSANDS OF DOLLARS DEPLOYING LOW COST OPEN SOURCE AI TECHNOLOGIES AT SCALE WITH KUBERNETES

Summary from the TLDR Web Dev newsletter

This author successfully replaced OpenAI's API with an open-source alternative to reduce the cost of running large-scale AI applications. They tried using Ollama on a local machine to generate text summaries, but limitations with concurrent processing led to them using vLLM (a fast inference runner). To handle large volumes of requests, the author used a Kubernetes cluster to deploy and load balance vLLM.