Choosing where to run your AI workload is as important as the model itself. The two main options — cloud and self-hosted — each have trade-offs in cost, control, and complexity.
The cloud advantage
Cloud providers offer on-demand GPUs, managed inference endpoints, and the ability to scale instantly. For spiky or experimental workloads, you pay only for what you use and avoid large upfront hardware costs.
The case for self-hosting
- Cost predictability — a fixed monthly server can be cheaper for steady, high-volume use.
- Data privacy — sensitive data never leaves your infrastructure.
- No vendor lock-in — you own the stack end to end.
A pragmatic middle ground
Many teams run smaller open models on their own servers for routine tasks, and call cloud APIs only for the heaviest jobs. This hybrid approach balances cost and capability nicely.
Whatever you choose, start small, measure real usage, and let the data — not the marketing — guide your decision.
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