Technology
Open-Source LLM
The short answer
An open-source LLM is a language model whose weights are freely available and can be operated on your own hardware or cloud of choice — unlike proprietary models, which are only usable via the provider's API. Well-known families come from Meta (Llama), Mistral, and others.
Open versus proprietary — weighing the trade-offs
Leading proprietary models (GPT, Claude, Gemini) typically deliver the highest quality across the board, but run exclusively with their providers. Open-source models now deliver genuinely production-ready quality for many clearly defined tasks — classification, extraction, summarisation — and can run entirely on your own infrastructure.
The choice is rarely ideological; it's practical. How sensitive is your data? What's your volume (own hardware can make sense at scale)? How complex is the task? Often the answer is a mix — open source for standard steps, a top-tier model for the hard cases.
What self-hosting requires
Self-hosting means: suitable hardware (GPU servers), operational responsibility (updates, monitoring, scaling) and realistic expectations about model size — the largest open models demand considerable resources, while smaller ones run on single workstations. For many organisations, hosted open source with a EU cloud provider is the practical middle ground.
Licence isn't one-size-fits-all
'Open source' works differently with LLMs: some models are truly free under genuine open-source licences, others come under community licences with restrictions (usage limits or exclusions for certain purposes), and some publish weights only without training data ('open weights'). Before deploying to production, a quick licence check belongs in your project — especially for commercial use.
Good news for European organisations: providers like Mistral offer powerful open models from the EU, and the gap between open and proprietary models has narrowed significantly for many task types. Choice is growing — and with it, your negotiating position with the big providers.
Responsibility comes with it: maintenance and security in self-hosted setups
Running an open model yourself means more than a one-time setup. Unlike API use, where the provider handles operations, updates and security, self-hosted open source puts full responsibility in-house: server maintenance, security updates for the runtime, monitoring load and availability, rolling out new model versions as better ones appear. These ongoing tasks are the real price of maximum control — and they're permanent, not just at launch.
Then there's the security dimension: a self-hosted model with its knowledge base is an attractive target and needs protecting accordingly — access controls, encrypted storage, isolated environments, scrutiny of model sources. For organisations without their own IT operations team, this is often the deciding argument for the middle path: hosted open source with a EU provider who handles operations while you keep data location under control. You get the data-protection advantage of open models without shouldering the full operational load yourself. The honest calculation includes these running costs — not just the API savings.
Practical example
A company with strict data-protection requirements runs an open model on its own GPU server for preprocessing sensitive documents. Only anonymised, non-critical tasks go to an external top-tier model — data never leaves the house uncontrolled.
Frequently asked questions about Open-Source LLM
Are open-source models worse than GPT or Claude?
On the most demanding tasks, proprietary leaders usually win. For bounded standard tasks, the difference is often practically irrelevant — cost, data protection and control matter more.
Is open source automatically more data-friendly?
Only with self-hosting or EU hosting: then data provably stays in your controlled environment. Running an open model via US cloud negates that advantage.
What does running your own model cost?
Hardware (one-off or rented), power and operational overhead — offset against eliminated API costs. At what volume it makes financial sense belongs in your project budget.
What do you have to handle continuously in self-hosting?
Server maintenance, security updates, monitoring load and availability, rolling out new model versions — plus access controls for the model and knowledge base. Without your own IT operations team, hosted open source with a EU provider often works better.
How relevant is this for your business?
In the free intro call we look at your specific process.