Reduce
Clean inputs, remove duplicates, summarize noise and keep only the data that changes the decision.
Local AI + paid AI, each where it shines
salvatustokens
Learn how to reduce token spend, choose models with intent and build AI workflows that do not burn money on work your own machine can handle.
Local
classify, summarize, clean
Paid
reason, decide, produce
Estimated saving
42%
Method
A simple filter before spending tokens
The site revolves around one question: which part of the job truly needs an expensive model, and which part can be prepared first.
Route
Use local AI for high-volume repeatable work, and reserve paid AI for reasoning, quality and edge cases.
Measure
Calculate cost per workflow, not per isolated prompt. If you do not measure it, saving becomes guesswork.
Calculator
Estimate how much you can save
Enter a common workflow and test how much you could move to local AI, rules or preprocessing before calling a paid model.
Learning paths
From spending by habit to spending with intent
Blog
Latest articles
The offline NotebookLM grows: pick a model and upload documents
the low-power extras, without blowing the RAM
An offline NotebookLM on a Cheap Intel
chat with your documents, no cloud, zero paid tokens
Your own ChatGPT, running locally
private, free and built in one afternoon