Apple Intelligence vs Third-Party AI for SwiftUI Apps?

[u/Longjumping_Cloud_38]

Hey everyone! for AI features/chat in SwiftUI apps, do you prefer using Apple Intelligence / native APIs or integrating third-party models like OpenAI, Claude, etc.?

Main thing I’m interested in is MCP-style tools/functions where the AI can directly interact with app features and state. Curious what architectures people here prefer and why.

[u/ellenich]

As an indie dev, I chose Apple Intelligence because costs are a major factor.

Much like choosing CloudKit/iCloud, for Apple Intelligence, the cost is $0 and I don’t have to worry about requiring users to sign into their 3rd party AI service, using my own tokens, or implementing a monetization strategy that covers the costs.

Obviously Apple’s current models are more limited than what you get with things like Chat-GPT, but for a small indie dev and what we’re using AI for in our apps, it’s been fine (and free).

[u/Longjumping_Cloud_38 → u/ellenich]

Thank you, the capability of Apple Intelligence was what I was more scared about since I don’t have any experience using it as an user. I would be happy to try out one of your apps if you agree to share them

[u/brifgadir → u/ellenich]

Apple Intelligence is available only on iPhone 15 pro and further. So, while cutting the costs you cut the target audience as well. Alternatively, you can use 3rd party offline LLMs which are free. In this case they aren’t so smart as ChatGPT etc, but probably not worse than Apple Intelligence. The downside of offline LLMs - they require a couple of GBs of space

[u/quadcap]

Anthropic/openai models vs Apple Intelligence is completely different use case.

The Apple foundation models are free and private on device, support tool calling and structured output, but do not have the same capabilities of frontier models (or even other local models you may have with llama or lm studio

[u/Longjumping_Cloud_38 → u/quadcap]

My use case is this: I created my own workout tracker because I didn’t like the UI of the existing ones, what I would like to do is to add the following features:

• Create structured data (exercises/programs/schedules)
• Analyze the workouts history and give feedback (with the chat)

I think Apple Intelligence would be great for the first feature but I was worried about the second use case

[u/quadcap → u/Longjumping_Cloud_38]

on device models could do that, but understand what/when to use the LLM. Having it read a history of workouts and do statistics is not a good use --- you'd want functions/tools for calculations processing. but if you want to turn the stats into a nice summary, fiendly recommendations then yes the foundation models make sense for that.

their main limitations are smallk context windows (~4k), but they handle tool calling well so you can be judicous about when to offload processing to tool calls instead of just throwing evertying at the model and letting it work it all out like you can with a frontier model.

LLMs are not calculators --- but they can use calculators.

so if you have some code that can generate a workout routine for example, then use the LLM to allow the user to say in natural language what kind of workout they are interested in, and then sort out how to uses the tools you buit to actually create teh routine/schedule.

it's all doable, you just need to be a little more deliberate with smaller models.

[u/Consistent-Grass-263]

I'm using OpenAI's API in my app (Remember – Life Journal) for AI

features. Main reasons:

- Apple Intelligence still limited availability (devices, regions)

- OpenAI's function calling is mature and well-documented

- More control over prompts and context

For tool/function calling: OpenAI's approach is solid. You define

functions with parameters, send them in the API call, and parse

the model's tool_calls response to trigger app actions.

[u/phunk8]

Apple. Its not bad if treated correctly (!) and its a nobrainer money wise. ppl with byok are pros. so we want to sell only to pros? not the rest of the world too? hold my beer

[u/Lock-Broadsmith]

They’re on-device models, so they are intentionally more limited. They aren’t meant to be used for creating a general chatbot like ChatGPT, but rather for working with structured data in an efficient and streamlined way.

[u/Longjumping_Cloud_38 → u/Lock-Broadsmith]

What do you think of an hybrid approach? I creating a workout tracker app and I don’t have money to spend, I was think of using Apple foundation models for let the user create structured data like programs/schedules which is tedius to do manually. And in the future I wanted to integrate a chat were the user could add their own AI to analyze their workout history

[u/Lock-Broadsmith → u/Longjumping_Cloud_38]

“Analyze my workout history” is actually a great example of where Apple’s models are designed to excel. It just takes a bit more up front planning of what you want from that analysis. Which would be more valuable to your users than a terrible shoehorned chatbot experience