4 posts tagged with "inference"

Managed Inference already gives you an open-weight LLM running on a dedicated EU GPU in Helsinki, served by vLLM behind an OpenAI-compatible endpoint, reached as foundrydb_managed/<model> with an fdb-inf key and wrapped in rate limits, token ceilings, EU residency, and GPU-hour metering. That is a great base model. It is not your model.

Today it can be. You can now serve your own LoRA fine-tuned adapters on that same managed GPU, live, with no restart. The base model stays loaded, your fine-tunes load alongside it, and each one answers on the same endpoint under the same governed controls. Your weights and your fine-tunes never leave EU infrastructure.

Retrieval-augmented chat is the demo everyone wants and almost nobody ships cleanly. The interface is easy. The plumbing is not. You need a vector store, somewhere to keep the documents, an inference endpoint that does not leak your data, and an app that knows how to reach all three. That is a database, a bucket, an API key, a handful of environment variables, and a firewall rule or two, all wired by hand before you see a single answer.

The rag-chatbot stack collapses that into one launch. Pick it, accept the cost preview, and a few minutes later you are chatting over your own data on infrastructure you own, resident in Europe.

Every managed platform you have ever used hands you a bag of parts. A database here. A bucket there. An API key, a network rule, a connection string, an environment variable. Each one is a primitive, and each one is yours to wire together. The pitch is "look how much you can build." The reality is an afternoon of plumbing before you see a single useful screen, and a config file that only you understand by Friday.

Today we flip that around. FoundryDB Stacks is live. A stack is the finished thing. One button stands up a complete, production-ready application, composed of those same primitives but already wired together, already metered, in minutes, and resident in Europe. You do not assemble the app. You launch it.

Your data already lives in Europe. Your databases run in European zones, your backups stay in European object storage, and your compliance story is clean right up until your application calls a model. Then a prompt full of customer data crosses the Atlantic on a key someone pasted into an environment variable, with no ceiling, no metering, and no answer to the question "where did that text actually go?"

Today we close that gap, and we do something bigger than close it. Three features ship together: vector search as a service over pgvector, embedding pipelines that run as real jobs with schedules and run history, and a managed inference proxy that puts one governed, OpenAI-compatible endpoint in front of OpenAI, Anthropic, Mistral, and Azure OpenAI. Together they make FoundryDB the European AI Data Platform: the one place where your data, your embeddings, and your model calls live under a single set of controls.