AI Economics
What adoption and operations cost: request pricing, inference, project budgets and ways to avoid overpaying. Numbers you can lean on when planning a budget.
What is latency and how to keep it within seconds
Latency is the time between a request and the model's answer: it is broken into its parts, each part is measured, and the user's wait is brought down to seconds.
Context bloat: what it means in plain terms
Context bloat is the growth in the volume of text sent to the model with every request, without any growth in the value that text delivers.
What is context compaction and why long sessions need it
Context compaction replaces a bloated conversation history with a short summary: the work continues, but the input volume of each turn stops growing.
AI agent in six weeks: where to start and when it pays off
Breaking a process down step by step delivers a first working version in six weeks. Four conditions for an agent to pay off, and the checks that confirm them in two days.
The Real Cost of AI Adoption: Nine Budget Lines and Three Hidden Ones
The range from RUB 500,000 to several million comes down to nine cost lines. Here the budget is taken apart piece by piece, including the three lines that rarely appear in a vendor proposal.
What Is a Token and Why You Pay for It
A token is a chunk of text roughly three quarters of a word long: the model reads a request in tokens, and the provider bills for how many there are.
Run the numbers
An estimate of monthly spend on AI automation for a single process and the payback period — from five figures already known about the company.
Calculating the monthly charge for model calls based on dialogue volume, conversation length, and model class — with the price of a single dialogue and the share of context in the bill.
An estimate of the annual cost of manual work at a single operation and the share of hours automation realistically removes — based on request volume, minutes per item and headcount.