AI model building on macOS is entering a new era. Microsoft has launched Foundry, a local AI model development tool.
For the first time, developers on Apple Silicon Macs can build and train models without relying on external servers.
As a result, Microsoft is expanding beyond the Windows ecosystem. This signals a major shift in their developer strategy.
Although Foundry is part of Windows AI Studio, it works natively on macOS. Therefore, it meets developers where they already work.
Moreover, it supports small prebuilt models like Phi and Mistral. From there, developers can fine-tune these models with private data.
In contrast to cloud-based workflows, Foundry enables offline development. This reduces latency and enhances data privacy.
Furthermore, teams can iterate faster without waiting on cloud services. This feature can significantly speed up model testing.
Importantly, companies that value privacy can now build AI tools without moving sensitive data off their devices.
Consequently, Foundry offers full control over AI development. Users can customize models securely and locally.
Additionally, the tool appeals to startups and enterprises alike. Both groups benefit from reduced infrastructure costs and faster deployment.
At the same time, Microsoft’s move challenges Apple’s push into on-device AI, expected to grow at WWDC in June.
On another front, Microsoft also announced new chatbot tools during its Build 2025 conference. These are aimed at website integration.
As competition in AI intensifies, Microsoft is clearly adapting. Its cross-platform tools could attract a broader developer base.
Even so, the timing is strategic. Apple is preparing its next wave of AI announcements, which may raise the stakes further.
Therefore, Foundry’s macOS support strengthens Microsoft’s position in the AI landscape. It shows they are listening to developer needs.
Ultimately, AI model building on macOS is now more accessible than ever. Developers can work locally, quickly, and securely.
With Foundry, Microsoft empowers innovation without compromising privacy or performance.
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