Every few weeks someone posts a GPU cost comparison showing that running a 70B model locally is dramatically cheaper than the API. The math is usually correct. The conclusion is almost always incomplete.
Hardware costs are the most visible part of local AI deployment, and they are genuinely significant. But hardware is also the part that gets the most attention. The actual expensive parts tend to be the ones that are harder to put in a spreadsheet.
The Hardware Part Is Real But Not The Point
Yes, a server-grade H100 setup costs $200K+ upfront. Yes, ongoing electricity costs add up. Yes, you can run smaller models on commodity hardware. These are all true and all worth understanding. But teams that have done this at scale consistently report that hardware is not where the budget surprise happens.
Electricity costs for a moderately loaded inference cluster can run $3K-$8K per month depending on your model size, utilization, and local energy costs. That is real money. Most teams budget for it. It is the things they do not budget for that cause problems.
Infrastructure and DevOps Talent
Serving AI models in production is a specialized engineering problem. You need people who understand GPU scheduling, model batching, KV cache management, quantization, and inference optimization. These skills do not overlap cleanly with standard backend or DevOps engineering.
A senior ML infrastructure engineer costs roughly the same whether they are working on local deployment or cloud APIs. But on the local side, they are also your only option for debugging why your model throughput collapsed after a firmware update, or why quantization is producing degraded outputs on your specific dataset. On the cloud side, that problem is someone else's problem.
For smaller teams, the opportunity cost of diverting engineering time to AI infrastructure is often larger than the direct cost of the API calls they are replacing.
The Maintenance Overhead
Open-weight models update frequently. A model you deploy today will have a better version available in three to six months. If you are running in production, updating means re-evaluating performance on your specific use cases, re-running fine-tuning if applicable, and coordinating a migration with zero downtime.
Cloud APIs abstract this entirely. You get model updates automatically, usually with a grace period to evaluate the new version. Local deployment means this cycle is your responsibility.
The Falling Behind Tax
This is the least discussed cost and the most significant for many organizations. Frontier models advance quickly. A locally deployed model starts aging the moment it is deployed. In fast-moving product categories, running a model that is twelve months behind the frontier can mean the difference between a competitive product and a noticeably inferior one.
The cloud advantage here is not just access to newer models. It is the optionality to switch models without changing infrastructure, to A/B test performance across versions, and to scale instantly when usage patterns change.
When Local Makes Sense
Local deployment is genuinely the right call for regulated industries where data cannot leave the premises, for applications with predictable, high-volume, latency-sensitive inference that justifies the infrastructure investment, and for organizations with strong ML engineering teams that prefer operational control for philosophical or strategic reasons.
For everyone else, the total cost of local is usually higher than it looks. The math works best when you have already solved the hard parts.