Colocation vs cloud: when owning your hardware wins
Cloud is convenient, but for steady, hardware-heavy AI workloads the economics and control often favour colocation. Here is how to think about the trade-off.
The cost curve
Cloud GPU instances are excellent for bursty, short-lived work. But when a server runs 24/7 for months, metered pricing adds up fast. Owning the hardware and colocating it turns a large recurring bill into a fixed, predictable cost plus metered power.
Control and privacy
With colocation the machine is yours: full root, your own disk encryption, your own software stack, and no shared tenancy. For sensitive or regulated data that is often the deciding factor.
Performance consistency
Dedicated hardware means no noisy neighbours and no surprise throttling. Latency and throughput stay constant, which matters for production inference.
Where cloud still wins
- Short experiments and spiky demand.
- Workloads that need to scale to zero.
- Teams with no hardware to colocate yet.
A common pattern: prototype in the cloud, then move steady production workloads onto colocated or dedicated hardware to cut cost and gain control.
In short: For steady, 24/7 AI workloads, colocation usually beats cloud on cost, control and consistency. See colocation pricing