#llm-inference

The 2026 MLOps Stack: Serving, Observability, and Cost Control When Models Move to Production

How serving, observability, and cost control work together when your models move to production in 2026. A practical guide for MLOps and platform teams.

The 2026 AI Inference Chip Race: How New Silicon Is Rewriting the Economics of Agentic AI

For the better part of a decade, the story of AI silicon was a training story. Bigger clusters. Longer pre-training runs. Denser FLOPs on a benchmarking leaderboard. If you wanted to know which compan

The New ML Stack in 2026: Comparing PyTorch, JAX, and MLX for Production Machine Learning

A practical comparison of PyTorch, JAX, and MLX for production ML workloads in 2026 — covering performance, ecosystem, deployment, and cost.

"We Hit 10x Query Volume With Zero Latency Spikes": An SRE's AI Infrastructure Story

An SRE's real-world story of scaling AI inference infrastructure from baseline to 10x query volume — with zero latency spikes. Covers observability, KEDA autoscaling, semantic caching, and warm pool strategies.

vLLM vs TensorRT-LLM: A Practical Benchmark for Enterprise Deployment in 2026