On-device AI runs the model entirely on your hardware — phone, laptop, or tablet — with no data sent to any server. Cloud AI sends your input to remote servers, processes it there, and returns the result. Both work. The difference is in what happens to your data, how much it costs over time, and what happens when your internet goes out.
The short version: on-device AI gives you privacy as a technical guarantee and zero per-query cost; cloud AI gives you more raw capability and access to the largest models. For a full technical explanation of how on-device inference works, see The Complete Guide to On-Device AI.
Privacy: Technical Guarantee vs Policy Promise
This is the sharpest distinction between the two approaches.
When you use a cloud AI service, your message travels from your device to the provider’s servers, gets processed, and a response is returned. That communication happens over TLS, so it’s encrypted in transit. But the provider’s servers do receive and process your plaintext input — that’s how the service works. What happens to that data afterward depends on the provider’s privacy policy, their data retention settings, and their internal practices.
OpenAI’s terms of service state that conversations may be used to improve models unless you opt out via an account setting — and that setting isn’t on by default for all users. Google’s Gemini Advanced has similar provisions. These policies change, and they vary by product tier, region, and whether you have an account. The common thread is that some form of data processing on remote servers is inherent to how cloud AI works.
On-device AI has a different structure entirely. The model weights live on your device. Your input never leaves your device. There are no servers to breach, no privacy policies to read carefully, and no settings to manage. The privacy protection is architectural, not contractual.
For sensitive topics — health concerns, legal questions, financial details, personal relationships — this distinction is meaningful. According to a 2025 Pew Research survey, 72% of adults said they were uncomfortable with AI companies using their conversations to train future models. On-device AI removes that concern entirely because the data never reaches the company in the first place.
Speed and Latency: Local Wins for Short Responses
Cloud AI has one latency component on-device AI doesn’t: the network round trip. A request to GPT-4o or Gemini 1.5 Pro has to travel to a data center, queue behind other requests, generate a response, and send it back. Under ideal conditions (fast connection, uncongested servers), this round trip adds 200–500ms before the first token appears. Under load, it’s longer.
On-device AI has no network round trip. The model starts generating the moment you submit your prompt. First-token latency on a modern iPhone with a 3–4B model is typically under 100ms.
For longer responses, the picture is more nuanced. Cloud models generate tokens fast — GPT-4o sustains roughly 80–100 tokens per second under load. A well-optimized 4B on-device model on an iPhone 15 Pro generates 35–45 tokens per second. For a 400-word response (~500 tokens), that’s about 11–14 seconds on-device versus 5–6 seconds in the cloud. The gap is noticeable but not dramatic for most use cases.
The more significant speed advantage for on-device is reliability. Cloud AI speed degrades during peak hours and depends on your connection quality. On-device speed is consistent regardless of network conditions.
Cost: Per-Token Pricing vs One-Time Download
Cloud AI pricing has come down substantially. As of early 2026:
- OpenAI GPT-4o costs approximately $0.0025 per 1K input tokens and $0.010 per 1K output tokens
- Google Gemini 1.5 Pro costs approximately $0.00125 per 1K input tokens and $0.005 per 1K output tokens
- Subscription plans (ChatGPT Plus, Gemini Advanced) run $20/month and typically include rate-limited access to flagship models
For occasional users, these costs are trivial. For regular users generating 50,000–200,000 tokens per month across conversations, the math shifts. A user generating 100K output tokens per month with GPT-4o spends roughly $12–15 per month on API costs, or pays $20/month for a capped subscription.
On-device AI has zero per-query cost. You download the model once (a one-time storage cost), and every conversation after that is free. Cloaked itself has no subscription. Heavy users — researchers, writers, developers who use AI throughout the day — save meaningfully over time.
The hidden cost comparison is time. Downloading a 2.9GB model takes a few minutes on Wi-Fi. Cloud AI is immediately available. For most users this is a one-time inconvenience, not an ongoing cost.
Capability: Where Cloud AI Still Leads
It would be misleading to ignore the areas where cloud AI retains real advantages.
Context window length. The largest cloud models handle 128K–2M tokens in a single context window. On-device models on current hardware are typically limited to 8K–32K tokens. For tasks like summarizing a long document, analyzing a full codebase, or maintaining very long conversations, cloud models have a structural advantage.
Model size and raw capability. GPT-4o and Gemini Ultra are estimated to have hundreds of billions of parameters — orders of magnitude larger than what fits on a phone. For highly complex reasoning, nuanced creative tasks, or cutting-edge scientific questions, the largest cloud models still outperform any on-device option available today.
Always-current training. Cloud models are updated by their providers. On-device models are static after download — they don’t know about events after their training cutoff, and you need to manually update to a new model version to access improvements.
For the majority of practical tasks — drafting emails, explaining code, answering factual questions, summarizing text, brainstorming — the performance gap between a well-chosen on-device model and a cloud model is small enough that most users won’t notice it in daily use. The gap is real and matters for edge cases; it’s not a reason to dismiss on-device AI for everyday work.
When to Use Each
Use on-device AI when:
- The content of your conversations is sensitive (health, legal, financial, personal)
- You need to work offline or in low-connectivity environments
- You want zero ongoing cost for regular use
- You prefer not to create accounts or share data with any company
Use cloud AI when:
- You need to process very long documents that exceed on-device context limits
- You need the absolute state-of-the-art capability for complex research or creative work
- Speed for long responses is more important than privacy
The practical answer for most people is on-device AI for daily use — the models are good enough, the privacy benefit is real, and the cost is zero — with a cloud option available for the occasional task that genuinely requires it.
For a complete technical breakdown of how on-device inference works, including the MLX framework that powers Cloaked, see The Complete Guide to On-Device AI and The AI Privacy Guide.
Download Cloaked on the App Store to run AI models entirely on your iPhone — no account, no cloud, no per-message cost.
Frequently Asked Questions
Is on-device AI as good as cloud AI?
For most everyday tasks — writing, summarization, Q&A, coding help, brainstorming — modern on-device models like Qwen 3.5 4B or Phi-4 Mini perform comparably to cloud models from a year or two ago. For cutting-edge research-level tasks or very long contexts, cloud models still lead. The gap is narrowing fast.
Does on-device AI work without internet?
Yes. Once a model is downloaded, on-device AI works completely offline — on a plane, in a basement, in a country with restricted internet. Your conversations never touch a network.
Is cloud AI safe to use?
Cloud AI providers have privacy policies, but your conversations do travel to and are processed on remote servers. Most major providers have confirmed they use conversation data in some form for model improvement unless you opt out — and opt-out mechanisms vary. If the content of your conversations is sensitive, on-device is the only architecture that makes a technical guarantee.
How much does cloud AI cost vs on-device AI?
Cloud AI costs roughly $0.002–$0.015 per 1,000 tokens depending on the model, which adds up quickly with regular use. On-device AI has zero per-query cost — you pay once for the device and download the model once. Heavy users who generate 100,000+ tokens per month save meaningfully over time.