Most enterprise AI infrastructure planning focuses heavily on compute power, specifically GPUs, for model training. However, this approach often overlooks the escalating data storage costs associated with AI inference. While training is a finite process, inference runs continuously, accumulating substantial amounts of back-end data such as model logs, cached context, and safety records.
The scale of this retained data is significant. For instance, an 8-second AI-generated video clip returning a 3.97MB file to a user can leave more than 500MB of associated data on the back end, representing a 130-fold increase. According to a WD-sponsored IDC white paper surveying 763 decision makers, nearly 95% reported data volume growth due to generative AI over the past 12 months, with approximately 61% experiencing growth of 25% or more. Furthermore, McKinsey projects AI inference demand will grow at a 35% compound annual growth rate through 2030.
As this data compounds, it shifts from being a static archive to an active asset for future workloads, such as retrieval-augmented generation (RAG). The IDC survey noted that 96% of respondents anticipate needing faster archive retrieval. Consequently, failing to budget for scalable storage can lead to expensive bottlenecks, where costly GPUs sit idle waiting for data. Organizations must adjust their retention policies and invest in economically scalable capacity that balances performance with total cost of ownership.