Shared infrastructure is a fine default for most web apps. AI workloads are not most web apps. Training and inference are bursty, memory-hungry and latency-sensitive in ways that punish the “noisy neighbour” model of shared hosting.
The noisy-neighbour tax
On shared hardware your throughput depends on what everyone else is doing. A model that benchmarks beautifully at 2am can stall at peak hours because another tenant is saturating the same GPU, memory bus or network card.
Dedicated hardware removes that variance. You get predictable performance, which is the difference between a 50ms response and an SLA you can actually promise to customers.
Data isolation is a feature, not a luxury
For regulated workloads, physical and logical isolation simplifies your compliance story enormously. Dedicated tiers mean your data and your models never share an execution context with anyone else’s.
When it pays for itself
Once your inference traffic is steady, dedicated capacity is usually cheaper per request than on-demand cloud — and far more predictable to budget. We help clients model the crossover point before they commit.