Comparison
Build your own LLM vs buy a sovereign model
What it really takes to build from scratch, and why "buy and own" gets you there faster without giving up ownership.
In short
Building your own LLM from scratch costs millions, takes a specialist team many months, and carries real execution risk. Buying a sovereign model means a vendor post-trains a strong base on your data and hands you the weights, so you own the asset in weeks, not years, without the cost and risk of training from zero.
The true cost of building from scratch
Training a competitive model from scratch requires large-scale compute, a rare research team, enormous curated datasets, and a long timeline, with no guarantee the result matches an existing strong base model. For all but a handful of organisations, the economics don't justify it.
What "buy and own" gives you
The middle path captures the best of both: a vendor like Locai post-trains a proven open base on your proprietary data (using Forget-Me-Not™ to add your domain without losing general capability) and gives you the weights and IP. You skip the cost and risk of pre-training, yet you still own the model outright, and it keeps improving via continual learning.
A simple decision framework
- Build from scratch: only if model-building is your core business and you have the team and compute.
- Buy and own: if you want an owned, domain-expert model fast, without becoming an AI lab.
- Rent an API: only for general, non-sensitive tasks where ownership doesn't matter.
Build vs buy-and-own vs rent
| Buy & own (Locai) | Build from scratch | Rent an API | |
|---|---|---|---|
| Time to value | Weeks | Many months+ | Instant |
| Upfront cost | Moderate | Very high | Low |
| You own the model | Yes | Yes | No |
| Domain-trained | Yes | Yes | No |
| Execution risk | Low | High | Low |
| Ongoing cost | Fixed | Fixed (+ team) | Per-token |
What this looks like with Locai
Where the comparison points toward owning your model, this is what it looks like delivered in practice.
Locai Labs believes organisations should own their intelligence. Renting access to a general-purpose model that lives on someone else's servers is fine for low-stakes work; for the AI that touches your data, your customers and your decisions, the model itself should be yours. That is the bet behind everything we build.
It is also a bet that an expert model beats a generalist on the work that actually matters to your business. A smaller model trained on your data, your language, your workflows and your edge cases routinely outperforms much larger generalists on the tasks you care about, and it does so on infrastructure you control. The goal is not the biggest model; the goal is the right model for your business.
And it is deployed sovereignly: an owned model that runs inside your perimeter, on-prem via Locai One, in your private cloud tenant, in a UK sovereign cloud, or fully air-gapped, depending on your residency and security requirements. Your prompts, your documents and your outputs stay inside your environment, under UK jurisdiction, with a data path designed to fit GDPR and the procurement standards regulated organisations are held to.
Frequently asked questions
Should I build or buy an LLM?
For most organisations, buy and own: a vendor post-trains a strong base on your data and gives you the weights, so you get an owned, domain-expert model without the cost and risk of building from scratch.
How much does it cost to train your own LLM?
Training a competitive model from scratch typically runs into the millions in compute and team, with a long timeline, which is why post-training a strong base is usually the better economics.
Do I still own a bought model?
With Locai, yes, you receive the weights and IP. "Buy" here means buying an owned asset, not renting access.
How long does it take?
Buying and post-training a sovereign model takes weeks; building from scratch takes many months or more.
Book a sovereign AI briefing
A 30-minute session on owning your model: deployment options, the data path, and a clear cost range for your use case.