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Open-source or closed models? What to actually choose for your company
Open source gives you control; closed models give you convenience and quality. The decision depends on your data, budget and who will maintain the infrastructure.
"Open source or closed?" is one of those questions with no universal answer. An open model gives you control, but it requires infrastructure and a team. A closed model simplifies the start, but makes you dependent on the vendor.
In short:
- Open source is not just a licence — it is also responsibility for hosting and updates.
- Closed models win on convenience and often on quality in difficult tasks.
- Most often the best answer is routing: locally where it is enough, in the cloud where it is needed.
What open source means in practice
"Open source" is sometimes used as shorthand for "a model whose files you can download". It is worth checking three things: the licence, the availability of the weights and the terms of commercial use.
Even when the weights are open, it does not mean the whole stack is open. The tokenizer, training data and tools may have different rules. On top of that comes the cost of GPUs, cooling and maintenance.
Closed models: convenience and quality
A model behind an API does not require your own infrastructure. The vendor updates it, scales it and is responsible for availability. That is a major advantage if you have no ML team and want to test an idea quickly.
The price is vendor dependency: pricing changes, limits, data policy and the risk of a model being retired. It is worth having a migration plan, even if you do not need one today.
| Criterion | Open source | Closed |
|---|---|---|
| Control over data | full, if you host it yourself | dependent on the contract and region |
| Start | slower | fast |
| Fixed cost | higher | low |
| Cost per query | low at high volume | higher |
| Quality in difficult tasks | depends on the model | often higher |
| Maintenance | on your side | on the vendor's side |
Open source is not free. It is just paid for differently — with infrastructure and team time.Neurasensei AI
When open source makes sense
- Your data cannot leave the organisation or the region.
- You have a large, predictable query volume.
- You need your own modifications, quantisation or fine-tuning.
- You have a team that will maintain the GPUs, updates and monitoring.
- You want to avoid vendor pricing changes and model retirement.
When closed
- You want to validate an idea quickly without investing in infrastructure.
- The task requires the highest quality of context understanding.
- Traffic is variable and you do not want to keep GPUs running permanently.
- You have no ML team and do not want to build one.
- You need ready-made features: tools, multimodality, security.
Hybrid in practice
- Leave classification and simple tasks to a local model.
- Route difficult questions to a frontier model through a router.
- Decide which data may leave the company and which may not.
- Measure cost and quality separately for each path.
- Keep an abstraction over vendors so that switching remains possible.
Decision checklist
- Can the data go to an external vendor?
- What is the realistic cost of infrastructure and maintenance?
- Do you have a team to handle GPUs and updates?
- How quickly can you switch models?
- Does the licence allow commercial use?
Not sure which model to choose? Get in touch via the contact form — we will compare the options against your data and constraints.