A major shift is happening in the world of artificial intelligence.
More and more, enterprises are choosing to run their production AI workloads not in the vast public clouds, but within the controlled walls of their own private clouds. A recent survey from Broadcom highlights this trend clearly: 56% of IT leaders now run or plan to run AI inference on private infrastructure, while the share for public clouds has dropped to 41%. This isn't just a small change; it marks a significant reversal, making private cloud the new primary home for enterprise AI.
So, what's driving this migration? First, the primary reason is cost and control. Public clouds can be like a taxi with a meter that's always running, leading to unpredictable bills. Costs for moving data out ('egress fees') and fluctuating demand for powerful GPUs can quickly spiral. Reports show that 'cloud waste' has risen to nearly 29%, a figure that gets executives' attention. For a steady, predictable workload like AI inference, the predictable budget of a private cloud is very appealing.
Second, another key driver is security and regulation. As AI becomes more powerful, governments are stepping in with new rules. The EU AI Act, for example, places strict requirements on how AI systems are run, especially those considered high-risk. For companies in finance or healthcare, handling sensitive data means they need a secure, auditable environment. A private cloud provides that level of governance, ensuring data stays where it's supposed to and that compliance is easier to manage.
Finally, there's the issue of resource competition. The largest AI companies are building massive data centers, consuming gigawatts of power and booking up the supply of advanced chips for years to come. This creates fierce competition for resources in the public cloud, driving up costs and making it harder for other businesses to secure the capacity they need. By building their own AI infrastructure, enterprises can guarantee they have the resources for their critical inference tasks without competing with tech giants.
In short, while the public cloud remains an excellent place for experimentation and development, the trend for running mature, production-level AI is pointing firmly toward the private cloud, driven by a need for predictable costs, tighter security, and guaranteed resources.
- AI Inference: The process of using a trained AI model to make predictions on new data. It's the 'live' or 'production' phase of AI.
- Private Cloud: A cloud computing environment operated exclusively for a single organization, offering greater control and security.
- Egress Fees: Charges levied by public cloud providers when customers move data out of the provider's network.