Sovereign AI

    AI with reduced operating costs and better team productivity

    Most teams ration their AI against a provider's budget and limits, and send their data outside the organization to use it. A sovereign AI system runs open models inside your own environment, each chosen for the task. Your teams use AI as much as the work needs, and you decide where your data and code go.

    • A different approach

      Smaller open models, each matched to a specific task and run where you choose. For many jobs, a focused model does the work just as well, at a fraction of the cost.

    • First use: engineering productivity

      Helping software teams write, review and test code faster, with the models running under your own governance.

    • We use it ourselves

      Afinico's own engineers used these models to help build a live enterprise AI system.

    • What you get

      Significantly lower cost, less dependence on any single provider, and control over where your data and code go.

    Indicative scenario

    What it means for 300 engineers

    An indicative scenario for a software engineering team of 300, using typical market prices and cautious assumptions. Your figures may differ.

    Assumed: large provider subscriptions

    • About $500 per engineer per month, subscription and usage included
    • $150,000 a month, $1.8 million a year
    • Usage capped at about 25 million tokens per engineer per month

    Your own model

    • About $1 million over three years to buy, set up, host and maintain
    • Handles around 60% of the work; provider subscriptions kept for the rest
    • No usage cap on the work it handles
    to pay back, depending on usage
    6 to 11 months
    net saving over three years
    $2.2 million or more
    on usage for the work it handles
    No cap

    The more your engineers use AI, the sooner it pays back

    • At the assumed budget: about 11 months. Engineers stay at about $500 a month each and around 60% of the work moves to your own model.
    • As usage grows: about 6 months. When a fifth of engineers use four times as much, spend rises to about $240,000 a month. Moving around 70% of the work to your own model saves about $168,000 a month.

    The bigger gain is productivity

    As engineers get more productive with AI, provider costs rise with them. With your own model, usage can grow without the bill growing, so the case gets stronger the more your engineers use it.

    Indicative only. Based on an assumed $500 per engineer per month, rising where some engineers use more, 60% to 70% of work moved to your own model, and $1 million total cost over three years including equipment, setup and maintenance.

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