Open-Source LLM

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.

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.

'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.

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.