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Use this when the provider is not in the catalog. The Cloud dashboard stores the shared credential once and then lets desktop workspaces import it.
Custom LLM provider detail in OpenWork Cloud

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Create the custom provider

  1. Open LLM Providers.
  2. Click Add Provider.
  3. Switch to Custom provider.
  4. Give it a Name (the Provider ID fills in automatically).
  5. Enter the Base URL of the OpenAI-compatible endpoint (for Azure AI Foundry this looks like https://my-resource.openai.azure.com/openai/v1).
  6. List the Model IDs the endpoint serves — on Azure AI Foundry these are your deployment names.
  7. Paste the shared API key / credential.
  8. Choose People access and/or Team access.
  9. Click Create Provider.
That’s it — no JSON required for OpenAI-compatible endpoints.

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Advanced: raw JSON config

If the provider needs more than the form covers (a different AI SDK package, per-model limits or modalities, custom options), click Advanced: edit as JSON and provide a models.dev-style definition. The JSON must include id, name, npm, env, and models; api is optional, but most OpenAI-compatible providers use it. The editor requires valid JSON, at least one environment variable, and at least one model. 
{
  "id": "custom-provider",
  "name": "Custom Provider",
  "npm": "@ai-sdk/openai-compatible",
  "env": [
    "CUSTOM_PROVIDER_API_KEY"
  ],
  "doc": "https://example.com/docs/models",
  "api": "https://api.example.com/v1",
  "models": [
    {
      "id": "custom-provider/example-model",
      "name": "Example Model",
      "attachment": false,
      "reasoning": false,
      "tool_call": true,
      "structured_output": true,
      "temperature": true,
      "release_date": "2026-01-01",
      "last_updated": "2026-01-01",
      "open_weights": false,
      "limit": {
        "context": 128000,
        "input": 128000,
        "output": 8192
      },
      "modalities": {
        "input": [
          "text"
        ],
        "output": [
          "text"
        ]
      }
    }
  ]
}

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Import it into the desktop app

  1. Open Settings -> Cloud.
  2. Choose the correct Active org.
  3. Under Cloud providers, click Import.
  4. Reload the workspace when OpenWork asks.

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Functional example: an LLM gateway (Infron)

An LLM gateway is one OpenAI-compatible endpoint that fans out to many model providers. If you want to run the routing yourself, LiteLLM is a good starting point. Infron is the hosted option: one API key gets you every model in its marketplace with automatic provider fallbacks and a single invoice, so adding a new LLM to OpenWork is just another entry under models. Grab a key from the API Keys dashboard. If you don’t have an account yet, sign up at infron.ai/login and their quickstart walks you through your first request. Paste the key into API key / credential and use this JSON: 
{
  "id": "infron",
  "name": "Infron",
  "npm": "@ai-sdk/openai-compatible",
  "env": [
    "INFRON_API_KEY"
  ],
  "doc": "https://infron.ai/docs/frameworks-and-integrations/openwork",
  "api": "https://llm.onerouter.pro/v1",
  "models": [
    {
      "id": "deepseek/deepseek-v3.2",
      "name": "DeepSeek V3.2",
      "attachment": false,
      "reasoning": true,
      "tool_call": true,
      "structured_output": true,
      "temperature": true,
      "release_date": "2025-09-29",
      "last_updated": "2025-09-29",
      "open_weights": true,
      "limit": {
        "context": 128000,
        "input": 128000,
        "output": 8192
      },
      "modalities": {
        "input": ["text"],
        "output": ["text"]
      }
    }
  ]
}
Once imported, Infron models show up in the Chat model picker alongside everything else:
DeepSeek V3.2 via Infron in the OpenWork Chat model picker
Pick the model and it’s live in the session footer:
Infron · DeepSeek V3.2 selected in an OpenWork session
Add more entries under models to expose other routes the gateway supports (e.g. openai/gpt-5.4, google/gemini-2.5-flash).

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When to use a cloud provider

Use a Cloud provider when the setup is meant to be shared across an org or team. For solo use, configuring it directly in the desktop app is simpler.