Foundation Models framework

What it does

The Foundation Models framework is a Swift API that lets app developers call language models from their own code. It takes text prompts, and images attached to prompts, and returns generated text or structured output. It gives direct access to Apple's on-device foundation model and to Apple Foundation Models running on Private Cloud Compute, and it can route to server models such as Claude and Gemini through the same interface.

Developers use it to summarize content, call their own tools, and build agent-style features. Guided generation, tool calling and image understanding are built in, with Vision framework tools such as OCR and barcode reading available on-device. Dynamic Profiles let one session swap model, instructions, temperature, reasoning level and tools while keeping a shared transcript. An Evaluations framework tests prompts, and an fm command-line tool and a Python SDK support scripting.

It suits developers building apps for Apple platforms who want private, offline-capable intelligence with the option to use larger models. You work in Swift and Xcode; it is a developer framework, not an end-user app. Private Cloud Compute models carry no cloud API cost for developers in the App Store Small Business Program with fewer than two million first-time downloads. Acceptable use requirements prohibit certain uses of Apple's models.

What it can do

• Prompt on-device and Private Cloud Compute Apple Foundation Models from Swift
• Guided generation and tool calling
• Image understanding with multimodal prompts plus on-device Vision OCR and barcode reading
• Dynamic Profiles that swap model, tools and instructions within one session
• Evaluations framework for testing prompts and app behavior
• Fm command-line tool and Python SDK for scripting

Works with

• Claude
• Gemini
• Vision framework
• Core Spotlight
• MLX Language Models
• Core AI Language Models
• Xcode

Example workflows

Add image-aware features to an app

Illustrative example. Inputs: an image and a text prompt. Steps: attach the image to a prompt, optionally let the model use Vision OCR or barcode tools, and use guided generation for structured output. Output: structured text describing the visual content.

Prototype and test an agent feature

Illustrative example. Inputs: instructions, tools and test prompts. Steps: define Dynamic Profiles with different models and toolsets, switch between them in one session, then run the Evaluations framework. Output: a tested agent behavior to refine before shipping.

Before you choose

• Server models and Private Cloud Compute usage beyond the Small Business offer may carry separate costs; request a quote or check each provider's terms
• Acceptable Use Requirements restrict how Apple Foundation Models may be used
• Some features are new, such as the utilities package and the fm CLI, which requires macOS 27

Listed under

• Coding
• MCP and agent tools
• Apple platforms
• Developers
• Developer tools
• LLM
• LLM API
• On-device AI

Get started

Start from the framework documentation and developer page

1. Install Xcode from the Mac App Store
2. Open the Foundation Models framework documentation and watch the introductory sessions
3. Choose a model (on-device, Private Cloud Compute or a supported server model) and build a first session in Swift

Apple Foundation Models on Private Cloud Compute have no cloud API cost for App Store Small Business Program developers with fewer than two million first-time downloads. Other pricing, including third-party server models, was not confirmed.

We couldn’t find published prices for Foundation Models framework. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 28 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Foundation Models | Apple Developer Documentation ↗developer.apple.com
• Foundation Models framework - Apple Developer ↗developer.apple.com
• Resources - Apple Intelligence - Apple Developer ↗developer.apple.com

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knowledge_base

Last checked 28 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.