Frugal AI & Green IT.
A model's value is not measured by accuracy alone: it is also measured by its cost and its footprint. We design AI systems and data architectures whose consumption is measured, reduced and traced — without sacrificing performance.
What is frugal AI and how do you reduce the footprint of an AI system?
Frugal AI is a design approach that aims to deliver the expected business value from an AI system with the minimum of compute, energy and data. It rests on measurable choices: sizing the model to the real need rather than reaching for the largest available, reusing and distilling existing models, optimising inference (quantization, batching, caching), scheduling workloads at lower-carbon-intensity hours and regions, and estimating and tracking the footprint (kWh, tCO₂). Frugal AI is part of digital sobriety: you only automate and only train when it is measurably useful.
Frugal AI — An approach to designing artificial-intelligence systems that maximises business value per unit of resource consumed (compute, energy, data). It favours right-sizing, model reuse, inference optimisation and systematic measurement of environmental footprint.
- kWh / tCO₂
- Footprint estimated, not guessed
- FinOps
- Cost analysis and right-sizing
- Frugality
- Right-sizing by default
- Sovereignty
- Hosting suited to your data
Why sobriety is a performance question
Performant code is frugal code. Reducing a system's footprint almost always reduces its latency, its cloud bill and its technical debt. The two goals do not conflict — they converge. We treat sobriety as an engineering discipline, not a marketing claim: every optimisation is measured and every gain is documented.
Where an AI system's footprint hides
- Training — often oversized. A smaller, well-distilled model is enough for most business cases.
- Inference — the recurring cost. Quantization, caching and batching cut consumption without degrading service.
- Data — collect, store and move less. Data sobriety reduces footprint before the first computation.
- Schedule and region — the same training emits far less when run at low-carbon-intensity hours and locations.
Estimate before promising
Sobriety starts with measurement. We estimate footprint from real consumption and cloud tooling, then prioritise the optimisations that actually matter. No reduction is announced without a baseline and an explainable method — no comfort figures.
What you get
- Footprint estimation (kWh / tCO₂) per service or model
- Eco-design review and good practices
- Frugality plan: right-sizing, reuse, inference optimisation
- Lower-carbon region and scheduling choices
- Cloud cost analysis (FinOps) and sobriety levers
- Prioritised reduction levers
Frequently asked
Answers to the most common questions — timelines, sectors, compliance, hosting, methodology.
Is frugal AI just less capable AI?
No. In the vast majority of business cases, a well-sized, optimised model reaches the same service quality as an oversized one, for a fraction of the cost and footprint. Frugality removes waste, not performance.
How exactly do you measure the footprint?
We estimate footprint from real consumption (kWh via cloud tooling) and per-region emission factors. We stay transparent about the method and its limits: these are useful estimates to decide with, not certified measurements.
Is sobriety compatible with my ESG / CSRD goals?
Yes — it feeds them directly. The kWh and tCO₂ indicators we produce are designed to slot into your CSRD reporting and your digital decarbonisation trajectory.
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