A decision that looks like a cost question
At some scale, someone runs the math: "We are paying X per million tokens to an API. A GPU server costs less than that. Let us self-host." The arithmetic is real, but it is measuring the wrong thing. Where your model runs is an operations decision wearing a cost disguise.
What the API price actually buys
A hosted API token price is not just compute. It bundles autoscaling, uptime, security patching, model upgrades, and a team that gets paged at 3am so you do not. When you self-host, all of that moves onto your plate. The GPU is the cheap part.
Where self-hosting genuinely wins
There are real reasons to run your own:
- Data residency — sensitive data that legally cannot leave your environment.
- Steady, high volume — predictable round-the-clock load where a reserved GPU beats per-token pricing.
- Latency and control — no network hop, no provider rate limits, no surprise deprecations.
- Customization — fine-tuned weights you own and can deploy freely.
Where the API keeps winning
For most teams, most of the time, the API wins on the things that actually hurt: spiky traffic, the need for frontier quality, and small teams who cannot afford to run inference infrastructure as a second product.
// The honest cost model is not price-per-token.
const selfHostCost =
gpuHours + engineerHours + onCallRisk + upgradeLag;
const apiCost = tokensUsed * pricePerToken;
// Compare these, not just the first term of each.
A pragmatic path
Start on an API. It is the fastest way to learn what your real workload looks like — the volume, the latency needs, the quality bar. Once that load is large, steady, and well understood, then revisit self-hosting with actual numbers. Optimizing infrastructure for a workload you have not measured yet is the most expensive guess in the stack.
The takeaway
Treat "self-host vs API" as a question about your team and your traffic, not a line item. The cheapest token is the one you never had to operate.