Think Build Implement Repeat
AI & Machine Learning

Are Open Models Worth It for Your Business?

Last updated:

Three genuine reasons to self-host

  1. Data cannot leave your infrastructure. A contractual or regulatory constraint, not a preference.
  2. Sustained high volume, where GPU capacity running continuously is cheaper than per-token pricing.
  3. Offline or air-gapped operation, where there is no external connectivity at all.

If none of those applies, hosted APIs are almost always the better economic and operational choice.

The cost that is not the model

Open weights are free. Running them is not. GPU instances are billed by the hour whether busy or idle, which means the economics depend on utilisation rather than on request count.

The crossover is about sustained load. A model serving a few thousand requests a day is usually cheaper on a hosted API; one serving continuous high volume can be cheaper self-hosted, if you have the engineering to keep it running.

The engineering you are taking on

  • Deployment, scaling and failover for GPU workloads
  • Model updates and the evaluation that follows each one
  • Monitoring for both availability and output quality
  • Capacity planning, since demand spikes cannot be absorbed elastically as cheaply
  • Security of the infrastructure holding your data

That is a real ongoing commitment, and it is the reason many self-hosting projects quietly move back to hosted APIs after a year.

Capability, honestly

Open models have improved markedly and, for many business tasks — classification, extraction, summarisation, straightforward drafting — they are entirely adequate.

At the frontier of reasoning and complex instruction-following, the leading proprietary models generally remain ahead. Whether that gap matters depends entirely on your task, which is why an evaluation on your own data settles it rather than a benchmark table.

A pragmatic middle path

Use hosted APIs with regional processing and enterprise terms for most work, and self-host only the specific workload where the constraint genuinely applies.

Design the system so the provider is abstracted, and you can move a workload either way without rebuilding around it.

Frequently asked questions

Are open models private by default?

Self-hosted, yes, in the sense that data stays on your infrastructure. Open models accessed through a third-party hosting provider have the same considerations as any other API.

What hardware do we need?

It depends heavily on model size and throughput. Smaller models run on modest GPUs; larger ones need serious capacity. Price it against your actual load before committing.

Can we fine-tune an open model on our data?

Yes, and it is one of the genuine advantages. Whether it is worth it depends on having enough consistent examples and a behaviour problem rather than a knowledge problem.

Which licences should we check?

They vary considerably, including some with commercial restrictions. Read the licence for any model you intend to use commercially rather than assuming open weights means unrestricted.

Keep reading

Told you should self-host for privacy?

Sometimes that is right and often regional processing under an enterprise agreement achieves the same thing for less. Tell us the constraint and we will advise.

Book a free 30-minute call Get a project estimate WhatsApp us

Related services

What we build for problems like this one

AI AgentsMachine LearningAI Integration