What’s the difference between local AI and cloud AI?

The first difference is where inference runs

An AI model receives input and uses learned weights to produce a classification, detection, generation, or another result. That computation is inference. With local AI, inference runs on the user's computer or phone. With cloud AI, the input is sent to a server, where a server-side model performs inference and sends the result back.

That one architectural choice changes the data path, connectivity requirements, latency, device load, and practical model size. AI does not inherently require the cloud, and local inference is not limited to trivial tasks. In some stacks, the same machine-learning model can be deployed either in the browser or on a server.

Local AI brings the model and computation to the device

To run local AI in a browser, the model weights and runtime must be made available on the client. Some workloads run in JavaScript, some use WebAssembly for CPU execution, and supported environments can use APIs such as WebGPU for GPU computation. Libraries such as ONNX Runtime Web and TensorFlow.js provide ways to execute machine-learning models directly in the browser.

However, local AI does not automatically mean that the application never needs a network connection. A web app may initially download its page, runtime, or model files from a server. If those assets are bundled or cached, later inference may work offline. The key question is not where the model was originally downloaded from, but whether the user's inference input is sent elsewhere when the task runs.

Cloud AI sends the input to where the compute is

With cloud AI, a browser or app sends input to a server through an API or similar interface, and the server performs inference. The user does not need to download a large model onto the device, and powerful GPUs and large memory pools can be shared across users. This is especially useful for workloads such as large generative models that may be impractical to host on a typical client device.

The tradeoff is that network communication sits before and after inference. Upload time can become significant for large images, audio, or video. The service can also become unavailable when the network is unreliable or the remote system has an outage.

For privacy, look at the data path rather than the AI label

A clear advantage of local inference is that photos, text, or other material being analyzed can be processed without uploading that material to a server. That matters when the goal is to avoid transmitting personal photos, internal documents, or unpublished data in the first place.

But the label “local AI” does not prove that the entire application makes no external requests. Inference may be local while analytics, crash reporting, external APIs, or cloud storage still communicate over the network. Conversely, cloud AI services differ in retention, training use, and access controls. The inference location and the service's data-handling policy are related questions, but they are not the same question.

Local is not always faster, and cloud is not always faster

For a small, optimized model, local inference avoids an upload-and-return network round trip and can work well for interactive camera or UI experiences. ONNX Runtime Web similarly lists reduced inference latency as a benefit when models are optimized for client hardware.

For a large model, heavy input, or computation-intensive task, the user's CPU, GPU, and memory can become the bottleneck. On phones, heat and battery consumption matter too. A sufficiently powerful cloud GPU can therefore be faster even after network time is included. Real latency is the combination of communication time and computation time.

Cloud makes large models easier to use, but larger does not always mean more accurate for your task

Local AI needs to distribute a model to the device. Larger models increase initial load time and memory use, and browser implementations have practical constraints around model and memory size. ONNX Runtime's web guidance therefore notes that tiny or small model variants are commonly used for web scenarios.

Cloud deployment does not require distributing the model to every client, making it easier to host much larger models on stronger hardware. But model size is not the same thing as accuracy on a specific task. For a narrowly defined task such as face detection or classification into a fixed set of categories, a smaller specialized model may be entirely sufficient. The useful model is the one that meets the required quality for the task, not automatically the largest one.

Using a model for inference is different from training a model

For both local and cloud AI, everyday product use often consists of inference with a pretrained model. Classifying a photo does not by itself mean that the model has learned or memorized that photo. Inference and training are separate processes.

At the same time, technologies such as TensorFlow.js can train models in the browser. So “local means no training” and “cloud means the provider trains on everything” are both incorrect shortcuts. Whether user data is used to update a model is a separate implementation and policy decision that must be checked independently.

Hybrid designs sit between local and cloud

Real applications do not have to choose an all-local or all-cloud design. An app might resize or redact an image on the device before sending only the required information to a server, or run simple classification locally while forwarding difficult cases to cloud inference.

So instead of asking only whether a service is “local AI,” ask which stages run on the device, which run on a server, and whether the original source data itself leaves the device. That reveals more about the actual architecture.

How Browser Kitty uses local AI

Browser Kitty tools such as Smart Image Sorter and the face-redaction tools run classification or detection models in the browser instead of uploading images to a remote AI service for inference. The source photo does not need to be uploaded to a Browser Kitty server for that inference step.

That does not mean local AI is inherently more advanced than cloud AI. These tasks are a good fit because they are narrowly defined and suitable models can run on the client. A large generative model or another workload may lead to a different architectural choice.

Tips and limitations

• Treat “local AI” primarily as a statement about where inference runs. Verify separately whether the application makes other network requests.
• A local-AI web app may still need the network for its first page load or model download. Local inference and fully offline operation are not synonyms.
• Sending data to a cloud AI service and using that data for model training are separate questions. Check the service's retention, logging, and training policies.
• A small specialized model can be less general than a large model yet still be entirely adequate for a fixed classification or detection task.
• WebGPU can accelerate some browser AI workloads, but available APIs and performance vary across browsers, operating systems, and GPUs.