Attention is focused on artificial intelligence, and yet the storage of data is very discreet in these debates. A paradox, since it is the keystone of AI projects.
At a time when artificial intelligence seems to be able to do everything, generate images, write texts, pilot industrial processes, a fundamental element remains surprisingly little discussed: data storage. This is not a subject that arouses great philosophical debates or great societal promises. However, it conditions, in the shadows, the technical, economic and operational success of any AI project.
In the collective imagination, data is an abundant and intangible resource, as if they were freely floating in the air. But in reality, they take up space. They must be stored, sorted, called, moved, archived and on an unprecedented scale. An AI project does not use a few gigabytes to cause a model, but teraoctets, even petacts, continuously. Storage is therefore not a peripheral subject. It is the invisible framework of any project.
Managing data is organizing intelligence even before it exists
It is often thought that AI begins with algorithm. In reality, it begins with data: raw, disorderly, imperfect. It must be ingested, clean it, structure it. At this stage, storage plays a leading role. It must be able to absorb huge volumes, while making fluid access. If this base is not solid, everything else vacillates.
AI can learn about bad material, of course. It will then produce blurred, oriented results, sometimes dangerous. Or, it will not produce anything at all, because the data is not available in time. Too often, companies forget that the promise of AI is not based first on the magic of models, but on the robustness of the infrastructure that feeds them.
A chain of needs that nothing standardizes
The data life cycle in an AI project is far from linear. Ingestion, preparation, training, deployment, archiving: each phase has its own requirements. At ingestion, you must be able to take a massive influx of heterogeneous data. In training, minimal latency is required, effective synchronization. At archiving, we are looking above all for sustainability, at the best cost.
The choice of storage adapted to each use is not technical luxury. It is a factor of performance as much as profitability. Without this agility, AI projects become fragile, expensive, rigid.
Object storage: an under-exploited lever
In this landscape to contrasting needs, object storage in the cloud is increasingly imposed as a relevant response. It is not intended to replace the high performance solutions essential for the training of models. On the other hand, it is cut to absorb massive volumes at a lower cost, with flexibility that facilitates back and forth on the data, even several months after their first use.
This is precisely where he makes the difference. Too often, datasets are archived and then forgotten, because their recovery costs time or money. On the contrary, object storage allows a form of continuity in the processing of information. He transforms the archive into a living resource, available at any time to re -include a model or launch a new exploration.
This ability to adapt to the non-linear rhythms of the AI is essential. It allows teams to work with flexibility, to multiply the tests, to return to a dataset passed without starting from zero. In a word: to innovate without friction.
Watch out for threshold effects
That said, the choice of a cloud solution alone does not guarantee serenity. Behind the attractive call prices of certain suppliers often hide additional costs for each operation: reading, writing, data extraction. In an IA context, where these operations are incessant, the trap closes quickly. And the company finds itself limiting its uses … not by strategy, but for fear of the bill.
This phenomenon generates a paradox: we invest in AI to go faster, explore more, automate better. And we end up rationing access to data. By arbitrating each action. By slowing down development cycles. This goes against what artificial intelligence promises.
Choosing an infrastructure is also making a cultural choice
Opt for a storage solution with transparent costs, such as those that provide neither access nor outings is not just a technical decision. It is a signal. This means that the company wishes to work freely with its data. That it values experimentation, collaboration between teams, the ability to go back to better advance
It is this type of posture that guarantees the sustainability and agility of long -term AI projects. Rather than being locked in a rigid architecture, or subject to a deterrent pricing grid, these projects must be able to evolve at the rate of uses, business needs, and new data that arrives constantly. Because, in AI as elsewhere, what really matters is not what shines, but what lasts.
