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Configuring LLM Providers

PaddleBoard is bring-your-own-key: you connect your own model accounts. There is no hosted PaddleBoard plan, and nothing is gated behind a PaddleBoard sign-in.

Add a provider

During onboarding (easiest). First launch includes an AI Providers section: expand a provider, paste a key, and Save — or take the zero-key path with Local Models (below).

Any time after that:

  1. Open the agent panel.
  2. Open agent settings → Configure Providers (or the command palette → provider config).
  3. Pick a provider, paste your API key, and save. Keys are stored in your OS keychain (never in settings or plaintext).

Most providers also read a standard environment variable (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY) as a fallback.

Switching providers

The LLM provider panel is a fast switcher — dock it wherever is convenient. It lists your providers with an authenticated check, marks the current default, and offers Use as Default to promote the selected one. It is deliberately just a switcher: deep configuration lives on the settings LLM page, and browsing or installing lives in the AI Dock.

Local Models — no API key needed

PaddleBoard can run a model on your machine with nothing to install: it ships a signed llama.cpp server and manages the process for you. Open Local Models in the AI provider settings (or during onboarding), turn on Run locally, managed by PaddleBoard, and pick a model — Gemma 3 4B is a good default. It downloads once, binds to 127.0.0.1 only, and is Metal-accelerated on Apple silicon. Afterwards it appears in the model picker like any other provider. Power users can still point it at their own llama.cpp server.

Supported providers

Includes Anthropic, OpenAI (and ChatGPT subscription auth), Google AI (Gemini), Google Vertex AI, Amazon Bedrock, DeepSeek, Mistral, OpenRouter, Vercel AI Gateway, PaddleBoard’s managed Local Models, and other local runners (Ollama, LM Studio).

Google Vertex AI (Gemini Enterprise)

Vertex is a PaddleBoard addition for teams whose Gemini access runs through Google Cloud. It supports three auth modes, in precedence order:

  1. Service-account key — set credentials_path to a service-account JSON file.
  2. Express API key — paste an AI Studio / Express key.
  3. gcloud (recommended, no stored secret) — run gcloud auth login once; PaddleBoard borrows short-lived Application Default Credentials. Just set project_id (and optionally location, default global) and it works.

Configure it from the provider settings (project id, location, optional credentials path). Newer models (e.g. Gemini 3) live on the global location, which is the default.

Privacy

Telemetry is hard-disabled in PaddleBoard — your prompts and usage never leave your machine except for the calls you make directly to your chosen provider.