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European infrastructure for AI workloads

Run models on infrastructure you control.

One workspace for the assistant, the agents, the endpoints and the cost record — on managed compute, on your own cluster, or on capacity bought from other operators.

Install the CLI
Models listed
55
Verified entries
14
Serving engines
4

Build with AI. Run it under your control.

Chat across models, review the agent's work, and ship from one workspace. Bring your own tools and keys; keep the run history close to the work.

  1. 01ChooseOpen a model from the real Edelux registry.
  2. 02WorkChat, edit in the IDE, and inspect each agent run.
  3. 03ShipPublish the project, or call it through a scoped API key.
Edelux workspaceIllustrative preview

From idea to inference

One connected workspace.

OpenAI-compatible

Chat, agents, endpoints and the cost record share one page, so a run can be read from the prompt that started it to the invoice it produced.

POST
POST /resale/v1/chat/completions · Review an incident report
Open the workspace
  • Scoped API keys
  • Request history
  • Your deployment target

Deployment

Run it where the workload belongs.

Start on managed compute to try something out, then move it to your own hardware. The deployment artifact is the same either way.

An accelerator board drawn part way out of its chassis

Managed compute

Choose CPU or GPU resources for a private endpoint. Review the price before you start, and set an idle timeout so an experiment does not bill all night.

Hosted endpoint · scale to zero when idleExplore endpoints

Your own infrastructure

Download a portable bundle, read its configuration, and apply it to a Kubernetes cluster, a Compose host or a bare Linux server. Nothing is hidden in a vendor console.

Bundle · Kubernetes · Docker · LinuxExplore bundles

Supported targets

  • Kubernetes
  • Docker
  • Google Cloud
  • Microsoft Azure
  • AWS
  • OVHcloud
  • Scaleway
  • Bare metal Linux

Getting started

Three steps, and the tools you already use.

The setup script finds the coding tools installed on your machine and writes the gateway endpoint, token and model profiles into each one.

  1. 01

    Read how a run is put together

    Every agent run keeps its trace, its token usage and its outcome, and you can read the whole thing before it reaches anyone else.

    See a run
  2. 02

    Point your editor at the gateway

    One command writes the endpoint, token and model profiles into the coding tools already on your machine. Scoped keys decide what each tool may reach.

    Issue a key
  3. 03

    Decide where the model runs

    Bring a model you already run, or let Edelux start one on managed compute. Either way the endpoint is the same shape.

    Start an endpoint

8 tools configured

  • Oh My Pi~/.omp/agent/models.yml
  • Claude Code~/.claude.json
  • Codex CLI~/.codex/config.json
  • OpenCode~/.config/opencode/opencode.json
  • Edecode~/.config/edecode/edecode.json
  • Hermes Agent~/.hermes/config.yaml
  • Continue.dev~/.continue/config.json
  • Aider~/.aider.conf.yml

Pricing

Pay for the month, spend the credit on any model.

A personal plan buys monthly inference credit for Build and Chat. Unused credit does not roll over, and renewal is manual.

Starter

Monthly

€10/ month

Inference credit

€20

For lighter Build and Chat use.

  • €20 of inference credit each paid month
  • Build and Chat across the model catalogue
  • Manual crypto renewal, no automatic charges
Choose Starter

Plus

Monthly

€100/ month

Inference credit

€200

For more frequent Build and Chat use.

  • €200 of inference credit each paid month
  • Build and Chat across the model catalogue
  • Manual crypto renewal, no automatic charges
Choose Plus
API reference rate
0.25× the official published price
Marketplace fee
8% of a settled sale
Billing interval
Per second, while a workload runs

Business plans, reserved capacity and wholesale commitments are quoted per contract. Talk to us.

Governance

Give teams access, keep the record.

Control who can run workloads, what they may spend, where they may connect, and which steps need a human decision.

  • Scope every surface

    Roles and scoped API keys decide who reaches which workspace, checked on each request rather than in the interface.

    Manage team & roles
  • Cap the spend

    Budgets, quotas and per-team chargebacks, with alerts that fire before a limit is passed.

    Set budgets
  • Keep a human in the loop

    Require an approval inside a workflow, assign the reviewer, and store the decision with the run.

    Add an approval
  • Detect tampering

    Consequential actions append to a per-organisation SHA-256 hash chain that a verification endpoint can replay.

    Open the audit trail
  • Classify under the AI Act

    Register a system, get its risk tier and obligations, and track the reporting windows the regulation sets.

    Register a system
  • Fence the network

    An egress policy restricts the domains a workload may reach, in audit mode first, then enforced.

    Configure egress

Market

Spare capacity is an asset, not a sunk cost.

Most clusters are either saturated or half empty, rarely correctly sized. Foundry puts both sides in the same book and settles the result in your own ledger.

models listed
55
serving engines
4
surfaces, one ledger
29
  • Buy what you lack

    Discover MCP servers, blueprints and compute packs published by other organisations, with the contract terms attached to each listing.

    Browse the marketplace
  • Sell what sits idle

    List spare capacity on the order book. Matching, commission and settlement are recorded against your own wallet, so the position is inspectable.

    Open the exchange
  • Serve your own clients

    Resell inference behind per-client keys, or sign take-or-pay wholesale commitments whose endpoints are probed against the contract.

    See resale

Start here

Open a workspace, or connect your editor first.

Signing in gives you the assistant, the agents, the endpoints and the cost record. Connecting an editor first is the smaller step, and it needs nothing but a key.