According to a report that Backblaze released this morning, traffic from content delivery networks and hosting and Internet services providers have stayed largely within historical norms over the past year. But traffic from hyperscalers and neoclouds fluctuated dramatically, with steep climbs in September and October and another uptick in March.
Another network traffic change related to AI is geography. “Traditionally, it didn’t matter where cloud infrastructure was located,” says Nowak. But with AI workloads, if storage is close to compute, enterprises get lower latency and higher throughput.
Today, Virginia and California have a high concentration of AI compute providers. This, in turn, brings in more storage companies. “In July, we chose to double our footprint in US East to increase the proximity to hyperscalers and neoclouds,” says Nowak.
And that, in turn, leads to even more demand for compute, and even greater concentration. “There’s a snowball effect,” Nowak says.
Why neoclouds for AI?
Enterprises might think that they don’t need to worry about network traffic details if they’re using a hyperscaler for their AI workloads because the data and the processing both stay within the cloud. But there are advantages to using a third-party storage provider combined with neoclouds for the GPUs.
According to a report released by Synergy Research Group in early April, neocloud revenues hit $9 billion in the fourth quarter of 2025, a 223% year-over-year increase. Revenues passed $25 billion for the whole year and are expected to hit $400 billion by 2031.
