AI agents are going interstellar and your infrastructure is the launchpad
Without the right infrastructure, the European AI journey might not make it past the stratosphere. What do modern AI agents require, how can organisations adapt to their needs, and how do regulations affect AI training and deployment?
The AI universe needs lightning speed and lots of space
AI agents, while revolutionary, are only as effective as the infrastructure supporting them. They rely on vast amounts of data to learn, adapt, and make decisions in real time and this creates an unprecedented need for high-performance storage systems. For European SMBs, supporting clients deploying AI agents might mean upgrading infrastructure to provide scalable enough storage to keep pace with the ever-expanding AI universe. These workloads require significant capacity but also incredibly fast access to data.
Handling large-scale data operations needs to come with minimal latency, no downtime and without surprise price tags. The demand for reliable and high-performing infrastructure is massive and those who meet these demands, are well-equipped and think long-term are also those who come out successful in the end — and the happiest.
Modern AI agents explain the investments in data centres
The faster AI evolves, the more storage it consumes - that is just simple physics. Uninterrupted workflows lead to faster training times and better outcomes. Therefore, it’s not only the tech giants that rely on high-performing architecture but anyone who wants to remain competitive while training, deploying, and supporting AI agents.
Modern AI agents rely heavily on data-intensive processes. Over time, advances in machine learning, deep learning, and neural networks have allowed AI agents to analyse complex datasets, learn from feedback, and make autonomous decisions. However, without access to high-quality datasets, AI agents cannot learn effectively — It is like sending a spaceship on mission but not providing them with enough fuel.
These datasets also require lots of secure storing space. In fact, Microsoft recently announced their plans to invest approximately $80 billion in fiscal 2025 to develop data centres tailored for AI training and deployment.
Discard datasets or increase storage capacity?
European companies have lots of high-quality data in store — and AI models require vast amounts of it to train effectively. This includes raw input data, intermediate datasets, model checkpoints as well as the final outputs. SMBs with limited storage might reach near or full capacity quicker than expected and face the decision on which datasets to keep and which to throw into outer space. This compromises the quality and range of the data available for AI training and potentially leads to underperforming models.
If the choice is between risking underperforming agents or switching to a provider that offers not only scalable storage and cost-effective pricing but is also designed for the training of these AI models, the options are worth pondering upon.
AI agents that access sensitive data
According to recent reports, ransomware incidents skyrocketed by over 200% in 2023. AI agents that frequently process sensitive data, including personal identifiers, financial records, or proprietary business information, create higher stakes for industries such as healthcare and finance.
Regulatory frameworks like NIS-2 mandate heavier measures for essential service providers in the EU to protect against data breaches and cyberattacks. The Data Act 2025 emphasises transparency for more secure data sharing and is designed to promote fair competition and to prevent vendor lock-in. As agents are in the forefront of AI advancement, compliancy to these regulations during and after training are in key position for organisations that want to advance in that race. By ensuring that sensitive data accessed and processed by AI agents is stored in a regulation-compliant environment, organisations can mitigate vulnerabilities and protect against the annoyance (to put it lightly) that ransomware attacks are.
Reliable storage for European SMBs
As AI models become more intricate, the infrastructure supporting them needs to evolve as well. Training a single LLM, like those powering advanced conversational agents, can require processing hundreds of terabytes and consume energy equivalent to powering hundreds of households for a year. Central to this is a reliable, high-performing storage provider that focuses on regulatory compliance and the needs of organisations that train and deploy these models.
Preparing for launch!
AI is the next great frontier, and storage is the launchpad. Those who invest in reliable, high-performing, and compliant infrastructure will be the ones who upgrade, scale, and prepare for launch (or already took off).
The AI space race is on.
