Browser AI vs Local AI: What Stays on Your Device?

Quick answer

Browser AI runs inference or recognition inside a browser tab. Local AI often runs through installed software on your computer. Either can process input on the device, but neither label proves the entire workflow is offline. Model downloads, cloud fallbacks, analytics, and the surrounding application can make separate requests.

Check four things: where the input goes, where processing runs, which support files download, and what other traffic the page sends. The examples below describe the Access Free Tools implementation reviewed on October 5, 2026. They are not a guarantee for every browser, setting, extension, or future version.

Browser processing does not mean zero network traffic

Source: MDN's Fetch API guide

A web page can download JavaScript, WebAssembly, language data, model weights, and images before it can work. Those downloads connect to the servers supplying the files. Browser code can also send data through fetch requests, forms, beacons, or third-party scripts.

A model download is different from sending a receipt or pasted text to a server for inference. Both are network activity. A useful privacy statement names the content, processing step, destination, and any separate telemetry.

OCR passes the selected image to a browser worker

Sources: Tesseract.jsand its worker API

The Image to Text OCR Tool reads the selected file into image bytes and passes them to a Tesseract.js web worker. Its recognition code does not upload the image to Access Free Tools. The worker, WebAssembly core, and selected language data load from the site's own model directory after Read text is pressed.

Six language files are supplied: English, Spanish, French, German, Italian, and Portuguese. Language data can be cached in the browser, so a later run may make fewer requests. A clear English synthetic samplehas been recognized and copied in a local Chromium test with external requests blocked. That proves that tested English path can work with locally served assets. It does not prove every language, image, browser, or live-site session stays offline.

Image quality affects recognition.OCR can still misread names, dates, totals, and account numbers. Compare the output with the original before using it. The OCR guideshows the sample workflow and file limits.

Model locations and runtime locations are separate settings

Sources: Transformers.jsand its custom model and runtime settings

This site's sentiment and tone classifiers request self-hosted MobileBERT model files withlocal_files_only: true. Here, local means the site's model directory. The browser still downloads those files before running inference. It does not mean the files were already installed on the visitor's device.

That model-loading option does not configure WebAssembly runtime downloads. Transformers.js documents separate model and WASM locations; disabling remote models does not disable every runtime request. This site's shared classifier loader leaves the WASM location unset, so local model loading alone is not evidence of a completely self-hosted or offline runtime.

The summarizer and image classifier can request models from a remote host. The browser speech and transcription workers also request their selected pinned model files from Hugging Face. Their current processing code passes text or decoded media to browser workers or pipelines, rather than uploading that content for server inference. Downloads can recur after a cache is cleared or a model changes. Check the actual request payloads before using sensitive input.

A localhost API can still route a prompt to the cloud

Sources: Ollama APIand Ollama cloud modes

Ollama supports a local API at http://localhost:11434/api and a hosted API athttps://ollama.com/api. A local Ollama server can also use cloud models. Seeing localhost in an application's settings proves the first connection is local, not that inference happens on your computer. Check the selected model and cloud settings.

Ask Access Free Tools first sends every submitted question to the Access Free Tools server. The server tries its supported parser. If no route is found and cloud routing is configured, it can send the question to Ollama's hosted chat API. A parser answer avoids that provider call, but the question still reached the site's server.

A correct answer does not prove a private route

For example, "What is 15% of 80?" has the exact answer 12. The Ask parser can select the percentage calculator and its inputs, then the deterministic runner supplies the answer. Ollama can help route other supported requests, but it does not supply the final arithmetic.

The tool page, REST API, and MCP endpoint use the same calculation logic. That makes the result repeatable. It says nothing by itself about where a question was sent. Verify the processing route and the result as two separate checks.

Analytics and session replay have their own boundaries

Source: Microsoft's Clarity masking documentation

Access Free Tools sends first-party page-view and tool-action events separately from model processing. Those events include page and tool details, browser-generated visitor and session identifiers, language, and screen size. The server hashes identifiers and the network address. Hashing does not justify a promise that all usage data is anonymous.

Microsoft Clarity can also load on public production pages. The tool and Ask interaction areas are explicitly marked for masking, including inputs and generated results. Masking protects content in Clarity recordings; it does not stop every network request or make the entire page invisible to analytics.

The privacy policy provides an Exclude this browser button. After choosing it, reload the page: the site's opt-out prevents first-party analytics and Clarity from loading or sending events on the next page load. The code also honors Do Not Track and excludes private admin pages. Local browser tests check these boundaries without sending telemetry to production services; they are not a live production traffic audit.

A practical checklist for checking a privacy claim

• Read the exact page. Look for input, model, analytics, and fallback details.
• Inspect the Network panel. Clear it, use a non-sensitive sample, run one task, and inspect new requests and payloads.
• Compare first and later runs. A warm cache can hide downloads that a new visitor will receive.
• Check the processing route. A button can call a worker, localhost, the site's server, or a remote provider. Localhost can forward requests.
• Try a disconnected run. Success proves only that the tested path worked with the files already available. A missing asset may still prevent another task.
• Repeat after changing settings. A language, model, cloud toggle, or analytics preference can change traffic.

Browser requests are one part of the picture. Extensions, operating-system services, and local applications can have separate traffic. A single successful test cannot establish how every part of a device handles data.

Use a privacy claim that names the actual task

"This image is processed in the browser and is not uploaded to Access Free Tools for OCR" describes a specific recognition path. "This AI tool is private" leaves the downloads, analytics, and any other routes unexplained. Prefer a claim you can check against the task you are about to run.

Compare the AI tools hub, theprivacy policy, and each tool's limits before choosing where to put sensitive material. The open-source stack articleexplains how the site's components fit together.