Comparison · AI Models
Open source or proprietary LLM: which to pick
Open-weight models like Llama versus closed models like GPT or Claude: control and cost against performance and speed.
In brief
For most SMEs it makes sense to start with a proprietary model (OpenAI GPT, Claude): better performance right away, no infrastructure to run, a useful first version in weeks. An open source LLM like Llama fits when strict privacy, data that must not leave, high volumes or per-token cost become the dominant constraint. In practice many companies use both: proprietary for hard tasks, open source where control and predictable cost matter. The recommended starting point for most SMEs is proprietary, with open source introduced only when privacy or cost make it necessary.
Option A
Open source LLM (e.g. Llama)
Open-weight models you can host on your own infrastructure or European cloud, with full control over where the data runs.
Pros
- +Data under your control: keep it on-premise or in European cloud, useful for GDPR and the AI Act
- +No per-token cost: at high volume self-hosting can cost less than pay-per-use
- +No vendor lock-in: switch provider or version whenever you want
- +Customizable with fine-tuning on your own data and cases
Cons
- −Requires infrastructure and MLOps skills for hosting, scaling and updates
- −Performance usually below the best proprietary models on complex tasks
- −Real upfront cost: GPUs, setup, monitoring, which at low volume never pays back
- −Maintenance (patches, new versions, security) stays on you
Best for
- Companies with strict privacy needs or data that must not leave their infrastructure
- Very high volumes where per-token cost matters
- Teams that already have MLOps skills
Option B
Proprietary LLM (e.g. GPT, Claude)
Closed models accessed via API: state-of-the-art performance with no infrastructure to run, paid per use.
Pros
- +Best performance on complex tasks (reasoning, code, multilingual)
- +Zero infrastructure: start via API, useful first version in weeks
- +Updates and security handled by the vendor
- +Mature ecosystem of tools, SDKs and documentation
Cons
- −Per-token cost that can grow fast at high volume
- −Vendor dependency: pricing, policy and model deprecation are out of your control
- −Data transits through an external provider: check the DPA and data residency for GDPR
- −Less room for deep customization than fine-tuning an open model
Best for
- SMEs that want to validate a use case early without building infrastructure
- Tasks that need the best performance available
- Low-to-medium volumes where pay-per-use stays affordable
| Criterion | Open source LLM (e.g. Llama) | Proprietary LLM (e.g. GPT, Claude) |
|---|---|---|
| Performance on complex tasks | Good, usually below top proprietary | State of the art |
| Data control | Full (on-premise / your cloud) | Depends on provider and DPA |
| Cost model | Fixed infrastructure + management | Pay-per-use per token |
| Time-to-value | Longer (setup and MLOps) | Weeks (API ready) |
| Skills required | High (MLOps, hosting) | Low (API integration) |
| Lock-in | Low | Medium-high |
The verdict
This is not an ideological or permanent choice. For most SMEs it makes sense to start proprietary: better performance right away, no infrastructure, and in a few weeks you see whether the use case delivers value. Open source becomes the right call when privacy, data residency or high volumes make per-token cost and external transit weigh heavily. In practice the most common answer is hybrid: proprietary where quality matters, open source where control and predictable cost matter. The right question is not 'open or closed', but 'which constraint dominates my use case'.
FAQ
What people usually ask us.
Is an open source LLM really cheaper?
Is open source safer for privacy?
Do I have to choose once and for all?
How do I start without an MLOps team?
Not sure which one fits your case?
20 minutes with the CEO to work out the right choice for your processes. No pitch, no obligation.
