Frugal AI: reducing the cost and footprint of your AI projects

Bigger, more powerful, more expensive: for a long time, the race for AI models followed this logic. Yet most business use cases do not need the largest model available. Frugal AI offers another approach: achieving the expected result with just the right amount of data, computing and energy.

What is frugal AI?

Frugal AI means designing, training and using artificial intelligence systems that consume as few resources as possible to reach a given goal. It starts with a simple, often forgotten question: is AI really the right answer to the need? Sometimes a business rule, conventional automation or better-organised data is enough.

In France, AFNOR (the French standardisation body) published a dedicated framework in 2024, the AFNOR Spec on frugal AI, which sets out a method and good practices to measure and reduce the environmental impact of AI services.

Why it matters for businesses

• Costs. Every request to a large model has a price. At company scale, an oversized model quickly weighs on the cloud budget.
• Environmental footprint. Training and running models consumes energy, water and hardware. Reducing computing means reducing that impact.
• Performance. A lighter model often responds faster and can run closer to the data, even on your own infrastructure.
• Control and compliance. Simpler models are easier to explain, audit and bring into line with the EU AI Act.

Five practical levers

1. Start from the need, not the technology

Define the problem, the expected gain and the level of quality that is good enough. A well-scoped use case avoids deploying an oversized tool.

2. Choose the right model

A specialised, smaller or open-source model often gives results as good as a very large general-purpose model on a targeted task, for a fraction of the computing.

3. Look after your data

Clean, relevant, well-structured data is worth more than a large volume of raw data. It reduces the need for training and makes results more reliable.

4. Optimise day-to-day use

Caching frequent answers, limiting unnecessary requests, choosing cloud regions with low-carbon electricity, shutting down unused resources: these simple settings make a real difference over time.

5. Measure, then improve

You can only reduce what you measure. Tracking both the value the solution creates and the resources it consumes lets you make informed trade-offs and improve engagement after engagement.

Frugality and performance go hand in hand

Frugal AI is not cut-price AI. It is AI sized for its use: faster, cheaper to run, easier to maintain and more responsible. In the vast majority of projects, these goals reinforce each other rather than conflict.

The Acrion approach

At Acrion, we are AI-native: AI is part of our methods on every IT engagement, and we apply it with the same commitment to sobriety that we recommend to our clients. Scoping the need, choosing the model, industrialisation and impact measurement: we support your teams at every step.

Frequently asked questions

Is frugal AI less effective?

No. On a targeted task, a well-chosen, well-trained model often reaches the same quality as a much larger one, with a shorter response time.

Where should we start?

With an inventory of your current or planned AI uses: the need covered, the model used, request volumes and cost. This review is often enough to identify the first gains.