Open-weight model
An open-weight model publishes its trained weights for download, so anyone can run it on their own infrastructure. That is a narrower claim than fully open source, the training data and code behind it may not be published, but it is enough to run the model yourself instead of calling someone else's API for every request. It sits opposite a closed, API-only model that only ever runs on its provider's servers.
A regulated enterprise that cannot send prompts or documents to a third-party API at all, for residency, confidentiality or air-gap reasons, needs a model it can run entirely inside its own infrastructure, and an open-weight model is what makes that possible.
Running Llama, Mistral, Gemma or Qwen inside a bank's own Kubernetes cluster, so no prompt and no document it processes ever leaves the bank's own network.
In Dynamiq, deploy open-weight models from the catalog on the platform's own or your self-hosted Kubernetes infrastructure using the vLLM engine, behind an OpenAI-compatible endpoint, and the Dynamiq SDK itself ships under Apache-2.0 on GitHub.
See an agent on your own workflow.
Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours.