cost

Tokenization looks like plumbing until it explains your bill, your latency, and why the model fumbles numbers and rare languages. The signal is knowing how subword tokenizers work and the consequences that fall out of them.

The 2025-2026 landscape isn't 'bigger is better' anymore. The signal is knowing why small models and reasoning models exist, the test-time-compute tradeoff, and routing the right model to the right task. Here is the practitioner's view.

This is an architecture and business decision, not a religious one. The signal is weighing capability, cost, privacy, control, and operational burden, then making a call instead of declaring 'open is always better.'

Token count is latency and dollars, not just style. 'Be concise' barely works, and max_tokens just truncates mid-sentence. Here is how to actually shape length without amputating answers.

The per-token sticker price hides the real decision. Self-hosting only wins past a volume break-even most teams misjudge. Here is the back-of-envelope a staff engineer does on the whiteboard.

When a drug name or a Hindi word becomes nine subword tokens, you pay in cost, context, and accuracy. Adding tokens is easy and adding them well is not. Here is the tradeoff.

Everyone budgets image tokens. Almost nobody budgets image latency. Images are a prefill problem, prompt caching stops paying for itself, and the cheapest answer is often not to call a VLM at all.

A 2025-2026 question that catches people clinging to dogma in either direction. The signal is a cost, latency, accuracy, and scale tradeoff, plus knowing the 'lost in the middle' failure of long context. Here is the framework for choosing.

More chunks means higher recall but more noise, more cost, and more lost-in-the-middle. The right k is an empirical tradeoff, not a default of 5. Here is how to find it.

The most role-defining judgment call in Applied AI: a shiny benchmark number against the operational price of a system nobody can debug at 2am. Most candidates ship on the number and lose the loop there. Here is the answer that passes.

Exact-match caching barely helps when no two prompts are identical. Semantic caching reuses answers for similar queries, and its whole risk is returning a near-match that's subtly wrong. Here is how to build it safely.

Batch scoring a billion items is a throughput-and-cost problem, the opposite of low-latency serving. Every choice that hurts latency helps you here. Here is how to design for dollars-per-million-documents.

Give every team an LLM API key and your bill becomes a mystery with a six-figure surprise at month end. Attributing, limiting, and optimizing spend is a platform feature. Here is how to build it.

Autoscaling GPUs is not autoscaling web servers: GPUs are scarce and expensive, model loading is slow, and the right signal is not CPU. The signal is scaling on queue and GPU metrics, taming cold starts, and the scale-to-zero economics.

Spot GPUs are often 60-90% cheaper, and they vanish with two minutes' warning. The savings are only real if a preemption costs you minutes, not the run. Here is how to make that true.

Dedicating a GPU per model strands most of a fleet on idle silicon. Sharing safely is a real systems problem with four distinct mechanisms, each tuned to a different traffic shape. Here is how to pick.

An attacker doesn't need to breach your LLM to hurt you, just make it do expensive work. A handful of crafted prompts can pin your GPUs and run up the bill. The defense is not a bigger rate limit.