self-hosting
5 free lessons tagged self-hosting across Programming, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Operating It: Security, Upgrades, and Knowing When to Stop
Self-hosting is a permanent operational responsibility rather than a project. This lesson covers the security surface a local model creates, model upgrades and why they are harder than they look, capacity and cost control, what to monitor, and the honest signals that the deployment should be retired.
The Serving Stack: Throughput, Memory, and Hardware Sizing
A model that runs is not a model that serves. This lesson covers what an inference server does that a naive loop cannot, continuous batching and why it dominates throughput, the memory arithmetic that decides which hardware you need, quantization for serving, and how to size a deployment from a traffic estimate.
The Case for Self-Hosting, Examined Honestly
Self-hosting is usually justified on privacy grounds, and the argument is often weaker than assumed. This lesson examines what actually stays private, which regulatory requirements genuinely demand it, the reasons that hold up better than privacy, and the honest total cost against an API.
LLM Pricing and Latency: What Actually Drives Cost
How frontier LLM costs and latency actually work in 2026 — input vs output token asymmetry, prompt caching, batch API discounts, TTFT and tokens-per-second, reasoning-model amplification, and when self-hosting breaks even.
Open Weights vs Closed APIs: The Real Tradeoffs
An honest look at the open-weights vs closed-API choice for LLMs in 2026 — covering data privacy, cost at scale, fine-tuning, latency, regulatory concerns, and the gap in raw capability per dollar.