Self-Hosting LLMs on AWS vs Managed APIs for SaaS

Self-Hosting LLMs on AWS vs Managed APIs for SaaS

Every SaaS team using large language models eventually runs into the same debate. Should we keep calling managed LLM APIs, or should we start self-hosting LLMs on AWS? It sounds like a pricing question. It usually isn’t. The real tension is between flexibility, operational burden, and how predictable your workload

Running Spot GPU Workloads on EKS the Right Way

Running Spot GPU Workloads on EKS the Right Way

GPU workloads are where AWS bills go to get absolutely unhinged. The moment you introduce training jobs, batch inference, or anything remotely ML-related, costs spike fast. That’s why teams inevitably look at Spot GPU instances on Amazon Elastic Kubernetes Service and think, this could save us a large amount of

AI Cost Optimization Starts With Reducing Reprocessing

AI Cost Optimization Starts With Reducing Reprocessing

Most teams think AI cost optimization starts with model choice. In reality, it often starts much earlier, with how often the same work gets done over and over again. Reprocessing is one of the fastest ways AI costs spiral on AWS. It’s also one of the least visible. Teams recompute

The Hidden Cost of “One Model for Everything”

The Hidden Cost of “One Model for Everything”

Most teams don’t overspend on AI because they’re careless. They overspend because they do the obvious thing: choose one powerful model, route every request to it, and ship. It’s fast. It works. And at first, nobody questions it. Then the bill shows up. The real problem isn’t that AI is