Local AI vs. Cloud AI: Which Makes Sense for Everyday Users?

AI tools now show up everywhere, from your phone’s keyboard to your web browser. Behind each one is a basic question that affects your privacy, your budget and what the tool can do: is the AI running on your own device, or on a company’s computers somewhere else?

What “local” AI means

Local AI, also called on-device AI, means the AI model runs on hardware you own, such as your phone, tablet or computer. The model is the software “brain” that reads your request and writes a reply. Your words and files don’t have to travel over the internet to get an answer. There are two common ways to get local AI.

AI built into your phone or computer

The major phone and PC platforms now include on-device AI features, each working a little differently.

• Apple. Apple describes on-device processing as the “cornerstone” of its approach to privacy. It also says that more sophisticated Apple Intelligence requests need help from larger models in the cloud. For those requests Apple built Private Cloud Compute, which it says is designed so that personal data isn’t kept after the response is returned and isn’t available to Apple staff, and so that outside researchers can inspect the software running on its servers [1]. In other words, Apple Intelligence is a mix of local and cloud processing.
• Microsoft. Copilot+ PCs are Windows 11 computers built around a neural processing unit (NPU), a chip designed specifically for the math that AI models use. Microsoft requires that NPU to handle more than 40 trillion operations per second, a figure usually written as “40+ TOPS” [2]. Copilot+ PCs also need at least 16 GB of memory (RAM) and 256 GB of storage [3].
• Google. On Android, Google’s Gemini Nano model runs inside a system service called AICore. According to Google’s developer documentation, it lets apps offer AI features “without needing a network connection or sending data to the cloud.” Examples include summarizing articles, proofreading and rewriting short messages, and describing images [4].

Running models yourself

The second route is to download an AI model and run it on your own computer with a free-standing app. Many of these are “open-weight” models, meaning the files that make up the model are published so anyone can download and run them. Two well-known tools are:

• Ollama, a tool for running models on Mac, Windows or Linux. Its FAQ says, “Ollama runs locally. We don’t see your prompts or data when you run locally.” Ollama also offers optional cloud-hosted models, and it explains how to switch cloud features off if you want to stay fully local [5].
• LM Studio, an app for finding, downloading and chatting with models. Its documentation says it “can operate entirely offline” once you’ve downloaded model files, and adds: “Nothing you enter into LM Studio when chatting with LLMs leaves your device” [6]. (An LLM, or large language model, is the type of AI behind chat assistants.)

What “cloud” AI means

Cloud AI runs on the provider’s servers. You reach it through a website, a phone app or, for businesses and developers, an API (application programming interface), which lets other software send requests to the model automatically. The Federal Trade Commission (FTC) calls companies that offer models this way “model-as-a-service” companies [7].

With cloud AI, what you type or upload is sent to the provider and handled under that provider’s terms. Those terms vary from company to company, and so do the settings you can change. One example is Google’s Gemini Apps Privacy Hub, which explains that a subset of chats is reviewed by human reviewers, that activity is auto-deleted after 18 months by default (you can change this), and that chats reviewed by people can be kept for up to three years. It also advises users not to enter “confidential information that you wouldn’t want a reviewer to see” [8]. Other providers publish their own privacy pages, so read the one for each service you use.

The trade-offs in plain language

Privacy and data handling

Local AI’s biggest advantage is that your data can stay on your device. Google says on-device processing keeps sensitive data on the phone, and that AICore doesn’t keep a record of inputs or outputs after processing them [4]. Ollama and LM Studio make similar statements about local use [5][6].

Cloud AI isn’t automatically unsafe. Some providers describe strong protections, such as Apple’s statements about Private Cloud Compute [1]. The key difference is that you’re relying on the provider’s promises and settings. The FTC notes that customers “may reveal sensitive or confidential information” when using these services, and it says companies must keep the privacy commitments they make, whether those commitments appear in marketing materials or in terms of service [7].

Cost

Built-in features come with the device. Google notes that on-device AI reduces inference costs, meaning the cost of running the model for each request [4]. The catch is that you may need a more capable, and more expensive, computer. Cloud assistants and APIs have paid tiers, and pricing changes often, so check the provider’s current pricing page before committing, especially for business use.

Hardware requirements

Local AI asks more of your hardware. Copilot+ features require the 40+ TOPS NPU, 16 GB of RAM and 256 GB of storage mentioned above [2][3]. For running models yourself, LM Studio recommends 16 GB or more of RAM. It says Macs with 8 GB may work if you stick to smaller models, that Intel-based Macs aren’t supported, and that Windows PCs should have at least 4 GB of dedicated graphics memory (VRAM) [9]. Ollama’s FAQ explains that memory needs rise when the model is given more text to work with at once [5]. With cloud AI, the heavy lifting happens on the provider’s servers, so the main thing your device needs is an internet connection.

Capability

Both Apple and Google describe sending harder requests to bigger cloud models. Apple says it turns to “larger, more complex models in the cloud” for more sophisticated requests [1]. Google’s documentation says Gemini Nano has limits on how much text it can handle, depends on the device’s hardware, and has “model capability boundaries,” so apps can hand harder requests off to cloud models [4]. For everyday tasks like summarizing or rewriting, a local model may be all you need. For long documents or complex reasoning, cloud models usually have the edge.

Offline use and speed

Local AI can keep working without internet access. That’s useful in rural areas, while traveling, or during an outage. LM Studio lists chatting with downloaded models and with your own documents among the tasks that work offline [6]. On speed, Google points out that on-device AI skips the round trip over the network, but adds that “inference speed depends on device hardware” [4]. Microsoft says AI features designed for the NPU run faster and use less battery [2]. Put simply, a newer device feels quicker, and an older one may struggle.

Updates and upkeep

Built-in features are maintained for you. On Android, for example, AICore handles distributing and updating Gemini Nano [4]. When you run models yourself, you need an internet connection to find and download new models and to check for app updates [6], and deciding when to update is up to you.

Energy

Microsoft’s developer guide says the NPU in Qualcomm’s Snapdragon X Elite chip uses energy on AI tasks more efficiently than a computer’s main processor (CPU) or graphics chip (GPU), which means longer battery life [2]. On the cloud side, the International Energy Agency estimates that data centers used about 415 terawatt-hours of electricity in 2024, roughly 1.5% of the world’s electricity. It projects that figure will more than double to around 945 terawatt-hours by 2030, with AI as the most important driver of the growth [10]. These are global totals, so they can’t tell you the energy cost of any single question you ask.

A simple decision guide

For households

• Mostly quick tasks like drafting a text, summarizing an article or proofreading a message: try the AI features already on your phone or computer first.
• Big questions, research or long documents: a cloud assistant will usually do better. Keep personal details such as account numbers, health records and passwords out of the chat unless you’ve read the provider’s data terms.
• Privacy or offline use is the top priority, and you have a computer with 16 GB or more of RAM: consider a local tool like Ollama or LM Studio, and expect some setup and trial and error.
• Shopping for a new device: if on-device AI matters to you, check the platform’s published hardware requirements before you buy [3].

For small businesses

• Sort your data first. Public marketing copy can go almost anywhere. Customer records, financial information and anything covered by a confidentiality agreement deserve more caution.
• For sensitive data, either keep it local or choose a cloud service whose written terms clearly cover how your data is stored, used and reviewed. The FTC notes that a provider working through APIs may be able to infer business information from how you use it, which is another reason to read the terms [7].
• Choose approved tools for staff and put the rules in writing. A one-page policy is better than none.
• Plan for internet outages if AI becomes part of daily work. A local option can serve as a backup.

Practical checklist

• List the AI features already on your phones and computers before buying anything new.
• For each cloud AI service you use, find its privacy page and check whether chats are kept, reviewed by people or used for training, and how to change that.
• Turn on any settings that limit data retention or training, if you prefer that.
• Write a “never paste” list: passwords, account numbers, health details, client files.
• Before trying local models, check your computer’s RAM and graphics memory against the tool’s published requirements.
• If you use a local tool, confirm whether it has optional cloud features and turn them off if you want everything to stay on your device.
• Keep devices updated and locked with a passcode, since local AI means your data lives on that device.
• Review your choices every six months or so. AI features and privacy terms change often.