Choosing between self-hosting an open-weight model and calling a cloud AI API is a genuine cost-and-control trade-off — worth running the actual numbers for your specific situation rather than assuming one is universally cheaper.
When Cloud APIs Genuinely Win on Cost
For low-to-moderate, genuinely variable request volume, cloud APIs’ pay-per-token pricing avoids the fixed cost of provisioning and continuously running dedicated GPU hardware, whether it’s actually being used at any given moment or not — genuinely the more economical choice below a certain, calculable request volume threshold.
When Self-Hosting Genuinely Wins on Cost
Self-hosted GPU: high fixed cost (hardware or cloud GPU instance rental),
but the MARGINAL cost per additional request is genuinely near-zero
Cloud API: no fixed cost, but a real, ongoing PER-TOKEN cost that scales
linearly and directly with usage volume
At sufficiently high, sustained volume, the fixed cost of self-hosting
gets amortized down enough that it genuinely becomes cheaper per-request
than paying per-token indefinitely
Beyond Pure Cost: Genuine Control Factors
- Data privacy — self-hosting keeps data entirely in-house, genuinely important for regulated industries or sensitive data
- Latency — a self-hosted model on local infrastructure avoids the network round-trip a cloud API always requires
- Model capability — the very best-performing frontier models are frequently only available via cloud API, not as open weights you can self-host at all
A genuinely honest cost comparison must include real GPU depreciation/rental cost, ongoing electricity, and crucially, the real engineering time required to operate self-hosted infrastructure reliably — self-hosting’s true total cost is routinely underestimated when only comparing raw hardware cost against per-token API pricing.