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Your own LLM instead of the OpenAI API: when self-hosting pays off

TCO, performance and control: three levers that decide whether a self-hosted model is the better choice.

Updated 2026-06-18 · Beyonetix Engineering · 8 min read

Three decision levers

Whether a self-hosted model pays off comes down to three factors: cost (TCO), performance and control.

1. Total cost of ownership

Cloud APIs bill per token, ideal for sporadic use and prototypes. At high, steady volume the maths flips: an owned or rented GPU has predictable fixed costs that, above a certain utilisation, sit well below token prices. We model this for your concrete load.

2. Performance: are open models enough?

For most enterprise workloads: yes. With vLLM, open models from 7B to 70B achieve high throughput thanks to PagedAttention and continuous batching; tensor parallelism scales across GPUs. Not every task needs a frontier model.

3. Control and sovereignty

The often-underrated lever: a self-operated model runs in your sovereign environment, no subscription lock-in to US providers, no overnight price or model change, no US jurisdiction, no data egress. For regulated industries this isn't a nice-to-have but a prerequisite.

FAQ

Frequently asked

From what point does self-hosting pay off?

Rule of thumb: at high, steady volume. We compare per-token cloud against GPU fixed costs for your concrete load.

Aren't small models too weak?

For many tasks 7B-70B models are more than enough, often faster and cheaper than frontier-model overkill.

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