The "local vs cloud" debate rarely has a single answer because it’s not a technology choice—it’s a cost profile decision. Cloud means variable cost (OPEX) that grows with traffic. Self-hosting means mostly fixed cost (CAPEX + maintenance) nearly independent of traffic. Which structure is cheaper depends on how much you actually use.
Two Cost Profiles
#| Self-hosted (local) | Cloud API | |
|---|---|---|
| Entry cost | High (hardware, deployment) | Near zero (API key) |
| Unit cost | Low, predictable | Variable, grows with traffic |
| Cost scaling | Flat up to hardware limit | Linear with volume |
| Data privacy | Data stays with you | Data leaves to provider |
| Best for | Steady, high volume | Low, irregular traffic |
How to Calculate the Intersection Point
#Calculate the monthly cloud cost (number of tasks × cost per call) and compare it to the monthly amortization of your own infrastructure (hardware spread over time + electricity + maintenance). The volume at which these two numbers equalize is your intersection point. Below it, stay in the cloud; above it, self-hosting starts to save money.
Why a Hybrid Usually Wins
#Rarely is everything "low" or "high" volume. Steady, high-volume tasks (classification, embeddings, semantic search with BGE-M3) are cheaper to handle locally. Rare, heavy inference is more convenient to buy in the cloud. A router directs each task where it’s cheapest and most secure—and it’s the router, matching model to task, that delivers the biggest cost leverage, regardless of local vs cloud choice.
Cost Isn’t Just the Invoice
#The calculation should also include lock-in risk (provider price changes) and compliance cost (personal data leaving for the cloud adds obligations—see self-hosted LLM and RODO). Predictability can be worth more than a few percentage points on the bill.
FAQ
#When is a self-hosted model cheaper than an API?
#When you have steady, high volume. The high entry cost is then spread across many tasks, and the unit cost drops below the cloud price. For low or irregular traffic, the API remains cheaper.
Do I have to choose one or the other?
#No. The optimal solution is usually a hybrid: handle cheap, high-volume tasks locally, and reserve the cloud for rare, heavy inference. A router ties it all into a single workflow.
What reduces LLM costs the most?
#Model-task matching. Routing simple flows to a small, cheap model and reserving a large one only where necessary typically delivers greater savings than the local vs cloud choice alone.