Agent Infrastructure Is Creating New Security Challenges
AI agents aren’t chatbots. They execute code, hold credentials, and call APIs on their own. Here’s why that breaks traditional security models — and what teams are doing about it.
Posts
AI Gateways: The Missing Layer Between Your Apps and Every LLM Provider
AI gateways sit between applications and foundation model providers, unifying auth, routing, caching, and observability. This post breaks down the patterns, the production wins, and the open source options like LiteLLM and Portkey.
GPU Utilization Is Becoming a Business Metric
GPUs are no longer a hidden infrastructure cost. They are a tracked business metric, tied to revenue, margins, and team accountability. This post unpacks why utilization moved from engineering dashboards to board decks, and what it means for the way you build, schedule, and bill ML workloads.
Inference Is Becoming a Distributed-Systems Problem
Once a model fits on one accelerator, inference is mostly GEMMs and kv cache lookups. Once it doesn’t, you inherit every problem distributed systems has spent thirty years failing to solve.
Kubernetes for AI Inference: Serving Models at Production Scale
Kubernetes has quietly become the de facto control plane for production AI inference. This post walks through the architecture, the GPU plumbing, and the patterns teams use to serve models that actually meet SLOs.