For heavy AI users, the economics of the current boom are starting to bite. Over the past year, major labs have nudged prices upward while tightening the screws on usage — whether through stricter rate limits, smaller context windows on lower tiers, or the gradual reshuffling of features behind more expensive plans. Even where per-token costs have fallen in headline terms, the reality for users is more complicated: higher volumes, more complex workflows, and new tooling expectations mean monthly bills are creeping up, not down.
At the same time, open-weight models have improved rapidly, consumer hardware has become more capable, and tools like LM Studio, Ollama, and llama.cpp have made local deployment far more accessible than it was even a year ago. The result is a renaissance in running models on your own machines.