Local AI vs. Cloud AI: What Changes for You?

August 27, 2026 · By Michel Valenzuela, Founder of Daryl

You open an AI application, ask a question, and receive an answer.

The experience can look almost identical whether the response is generated on your computer or on a server thousands of kilometres away. Yet underneath that familiar interface are different arrangements for your information, your hardware, and your access to the service.

Understanding those differences helps you choose an AI that fits the way you want to work.

With local AI, the model processes your request on your device. With cloud AI, it processes your request on remote infrastructure.

Neither arrangement tells you everything about the product. But it gives you a useful starting point.

Where your thoughts go

Imagine you want help preparing for a difficult conversation with a business partner.

You might explain the relationship, describe a disagreement, and explore things you would never include in the final message. The conversation becomes useful precisely because you can share the circumstances behind it.

With cloud AI, the information you submit travels to the service for processing. How that information is subsequently stored or used depends on the provider, the product, your settings, and the applicable terms.

With local AI, the request can be processed without sending its content to a remote AI service.

That difference matters when deciding what you feel comfortable sharing.

Local processing does not automatically make an entire application private. Online integrations, diagnostics, backups, and synchronisation can introduce other routes through which information leaves your device. You still need to understand the application’s behaviour.

The practical question is: what information leaves my computer, for what purpose, and under whose control?

What the AI can handle

Cloud infrastructure allows providers to run models that demand more resources than a typical personal computer can supply. It also gives them a way to maintain and update those models centrally.

That can make cloud services useful for demanding tasks, broad capabilities, and access across multiple devices.

Local AI works within your computer’s resources. The model needs to fit the available memory and run at a usable speed on your hardware.

Its usefulness depends on the model and the task. A smaller local model may serve a focused purpose well while struggling with work that requires broader knowledge or more complex reasoning.

You should assess the actual application against what you need it to do. “Local” describes where processing happens. It does not establish the quality of every answer.

The same applies to “cloud.” Remote processing alone tells you little about whether a particular service is suitable for your work.

What happens when you lose your connection

A local model can continue generating responses offline once the required software and model files are installed.

That can be useful while travelling, working somewhere with unreliable connectivity, or simply choosing to disconnect.

Cloud inference requires a connection to the remote service. If that connection is unavailable, the application cannot obtain a new response from that service.

However, offline use has boundaries.

A local assistant still needs internet access to retrieve a current webpage, search online, or connect to another service. Installation and updates may also require a connection.

It helps to separate two activities:

• Working with information already available on your device.
• Retrieving information or using services elsewhere.

Local processing can support the first without connectivity. The second involves a network connection, regardless of where the AI model runs.

Who supplies the computing power

With cloud AI, the provider supplies the infrastructure that generates your responses. Your device mainly provides the interface and exchanges information with the service.

With local AI, your computer performs that work.

That means hardware matters. Memory, processing performance, available storage, and the application’s design all influence the experience. Local workloads can also affect battery use and leave fewer resources available for other applications.

Response speed is not automatically better in either arrangement. A local model avoids a network round trip, but it may generate responses slowly on limited hardware. A cloud service has powerful infrastructure, but connectivity and service demand can affect the experience.

The useful benchmark is your actual workflow on the hardware you intend to use.

What you pay for

Processing location and pricing are separate decisions.

A local application can charge a subscription. A cloud service can offer free access. Buying local software does not necessarily include every future update, and paying for cloud access does not necessarily mean every feature is unlimited.

Compare the terms rather than assuming the architecture determines the business model.

Ask what the payment includes, whether there are usage limits, what happens if you stop paying, and whether you can continue accessing your information.

For Daryl, I have chosen a purchase model with no mandatory monthly subscription. You keep using your eligible licensed version, with future feature updates available through an optional renewal after the included update period.

That is a product decision shaped by how I believe personal software should work.

Convenience depends on the application

Cloud services can make it straightforward to begin using AI without downloading models or managing local hardware requirements.

Local applications need to handle installation, model availability, compatibility, and updates. How much of that work reaches the user depends on the product.

A well-designed local application can make those details manageable. A more technical tool may give you extensive control while asking you to make more decisions yourself.

This is one of the reasons I’m building Daryl as an application. Making a model run is part of the job. Making it comfortable to use every day is another.

A practical comparison

You may use both.

A cloud service might suit one project, while a local assistant suits a private conversation or a task you want to complete offline. The choice can change with the information involved and the work you need to accomplish.

Why I’m choosing local for Daryl

Daryl begins with a particular purpose: personal conversation and help thinking through decisions.

Those conversations can involve unfinished ideas, competing priorities, and circumstances you have not shared elsewhere. I want people to have a useful place to explore them on their own computer.

That is why Daryl is being developed for Mac with local processing for its core conversations.

Optional online capabilities are part of the longer-term direction, with clear boundaries and user authorization. A local assistant can connect to information beyond your device while keeping you informed about what that connection involves.

My aim is to make the experience understandable. You should know what your assistant can do, where the processing happens, and what you control.

Choosing AI becomes easier when those arrangements are clear.

Daryl is coming soon to Mac.