Don't Confuse Open-Weight AI with Open-Source — It Costs More

With headlines about 'open-weight AI', it's easy to confuse it with open-source and believe it will be cheap and risk-free. Unfortunately, the difference can be costly: from computing power to GDPR. Below, I explain in simple terms how to avoid the most expensive pitfalls for SMEs.

Open-weight ≠ open-source: What’s the Difference?

Open-weight refers to a model where the 'weights' can be downloaded under a license. Weights are numbers that the model learned during training — think of them like spices in a ready-made sauce. You can run such a model on your own or in the cloud, but only under the terms of the license.

Open-source means the code is open and you have broad usage rights (according to the Open Source Initiative — an organization that defines open-source licensing rules). This allows you to check how it works 'under the hood', modify it, and integrate it into your product, usually without needing additional permissions.

Examples of model families with open weights include Llama (by Meta) and Mistral AI. You can find them in catalogs like Hugging Face. Always check the license of the specific version. Conclusion: open-weight does not equal 'free and risk-free'.

Where Costs Add Up: Power, Cloud, and People

Inference (running the model to calculate an answer) costs money. You pay for GPU (Graphics Processing Unit) power, like NVIDIA cards, data transfer, and runtime. If you host it yourself, you also pay for the hours when machines are idle, as well as for updates, monitoring, and backups.

For example, consider support: a FAQ bot works great in a demo, but on Monday, the queue grows, the model slows down, and customers wait. You need to scale servers in AWS, Azure, or Google Cloud, monitor delays, and synchronize the knowledge base. This requires hours of work and real costs. With an API (a service on demand via the internet), you mainly pay for usage and have built-in scaling.

In marketing and sales, content quality matters. A weaker model requires more detailed prompts (instructions for the model) and more editing. Every minute of editing costs money. Sometimes, a more expensive API turns out to be cheaper overall. Conclusion: before buying hardware, run a 2-week pilot on an API and calculate the real load.

• Computing power: GPU/cloud, including idle hours.
• Human time: configuration, fixes, monitoring, and support.
• Data: logs, backups, encryption, access.
• Quality testing: acceptance thresholds for the team and customers.

Licenses, Updates, and Support: What Often Gets Overlooked

Open-weight licenses vary. They often allow you to run the model, but with conditions: commercial scope, attribution requirements, restrictions on resale, or additional permissions if you integrate it into a product for customers. This is not 'fine print'; it’s real project risk.

Updates. A new version of the model means new behaviors. You need to refresh prompts, tests, and integrations. If you host it yourself, that’s your cost and your risk of downtime. With an API, you usually have versioning and migration support.

Support. Who will respond at 2 AM if the server goes down? With self-hosting, it’s your team or service partner. With an API provider, you have an SLA (Service Level Agreement) that guarantees availability and a support channel. Conclusion: first, check the license and update plan, then consider servers.

• Does the license allow for commercial use in your business model?
• Can you host it in your chosen cloud or on-premise (your own server room)?
• Is attribution required in your product or materials?
• What about modifications and sharing further (for customer products)?

GDPR and the Illusion of No Vendor Lock-In

GDPR (General Data Protection Regulation). Personal data isn’t just a PESEL number — it also includes email addresses, usernames, and the content of inquiries. Open-weight on your own makes it easier to control where processing occurs, but you still need a legal basis, Data Processing Agreements (DPA) with the cloud, activity logs, retention policies, and data deletion procedures. When using an API, choose an EU region and check how long logs are kept.

Vendor lock-in (dependency on a provider) doesn’t disappear with 'open weights'. You remain tied to your own integrations, prompts, test sets, and team habits, which means additional rework and retesting when changing providers. Switching models requires effort. Openness helps, but not for free. Conclusion: plan for an exit strategy from day one.

• Keep your company knowledge in your data, not in the 'trained' model.
• Document prompts, tests, and decisions in a shared catalog with versions.
• Have a simple 'model switch' in your solution's configuration.
• Archive data in neutral formats (CSV, JSON).

Open-weight is a useful option, but not the default choice 'because it's cheaper'. First, calculate the costs of computation and human resources, check the license and GDPR, and run a short pilot on an API. Want to calculate this based on your numbers and processes? Contact us — we’ll arrange a calm, 30-minute consultation without sales pressure.