If you’re building AI models, then you will understand that the data flow never stops, with massive training sets, checkpoints, logs, and raw files. Only a small percentage of this data is ever hot and active; the vast majority still needs to be stored for future AI and ML training or compliance.

Data is vast, growing and demanding more and more power.

As Data Architects, Engineers and DevOps will understand, the problem with building data workflows on a hyperscaler platform, the platform either keeps data hot and accessible even when not needed which wastes power, power that metropolis based data centres are struggling to draw from the utilities providers as the grid restricts power supplies.

To save on power and reduce initial costs for customers data is directed to data lakes like Amazon Glacier, Azure Blob or GCP Archive. Archive tiers of cloud data storage may appear inexpensive compared to the top tier hot cloud storage offerings, but they all have retrieval and egress fees which may come as an unpleasant added surprising additional cost.

Retrieval costs quickly eat into budgets right at the moment your AI project requires an historical dataset restored or rehydrated to learn, analyse or retrain models.

Sound familiar? You’re not alone.

Specialist cloud storage solutions like Cloud Cold Storage have no restore or egress fees, we store pettabytes of data for the long haul in a sustainable, environmentally friendly fashion using little to no power on industry-standard cold storage equipment, ready to stream back when you need it.

Saving power for your GPU’s and budget to run more AI workflows ML and analysis.

Say no to egress and retrieval fees.