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.
Let's work through an example (prices are indicative—verify against current pricing, since API and hardware rates change fast). Assume a typical task: ~1k input tokens + ~0.5k output tokens. With a mid-class model (order of magnitude ~0.30 USD per 1M input tokens and ~1.20 USD per 1M output), a single call costs ~0.0009 USD. On the self-hosting side, take a GPU box amortized over 36 months plus electricity and maintenance—realistically that lands in a range of ~600–1200 USD per month, depending on the card class and whether the hardware is shared with other workloads.
| Volume / month | Cloud cost (indicative) | Self-host cost (range) |
|---|---|---|
| 0.5M calls | ~450 USD | ~600–1200 USD |
| 2M calls | ~1800 USD | ~600–1200 USD |
| 5M calls | ~4500 USD | ~600–1200 USD |
In this scenario the lines cross somewhere between 0.5M and 2M calls per month—above that threshold the fixed hardware cost starts to pay off. This is just one illustrative profile: longer prompts, RAG, and pricier models push the threshold down (cloud gets expensive faster), while cheaper tasks push it up. Compute your own threshold in the inference cost calculator—and once your volume crosses it, the next step is migrating from an API to your own model, which quantifies the same intersection point in practice.
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), maintenance cost (the full TCO: monitoring, updates, on-call) and compliance cost (personal data leaving for the cloud adds obligations—see self-hosted LLM and GDPR). 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. Close behind is token cost optimization—shorter prompts, caching, and controlling context length.