Local-first AI explained

Local-first AI means your own device holds the primary copy of your data, and the AI tool works from that copy, sending out only what a given task needs. It does not have to mean the AI model itself runs on your machine, although it can.

The term "local-first" comes from software design. A local-first application treats the copy on your device as the real one. The network is used for sync, sharing and backup, not as the only place your work lives. The application keeps working when you are offline, and your files remain readable if the vendor disappears.

Applied to AI, the same idea raises a useful question: for each part of the tool, where does it run and where does its data rest?

Three things that can be local

It helps to separate three layers, because a product can be local in one and not the others.

1. Your data. Notes, documents, code, mail, transcripts. Local-first means these sit on your disk in formats you can open without the tool, such as plain text or Markdown.
2. Your credentials. API keys and sign-ins. Local means they sit in the operating system's secret store, not on a vendor's server. See where your AI keys and logins should live.
3. The model. The AI that reads the prompt and writes the answer. This may run on your device, or in a provider's data centre.

Many practical setups are local for the first two and remote for the third. That is still a meaningful improvement over a setup where all three live on someone else's servers.

Local models and remote models

Running a model on your own hardware is possible. Open-weight models can be downloaded and run on a laptop or workstation, and smaller ones run acceptably on ordinary machines.

The trade-off is capability against hardware. Larger models generally give better answers on hard tasks, and they need a lot of memory and a capable graphics processor or similar accelerator. The strongest models are typically offered only as hosted services. Smaller local models do well on narrower jobs: transcription, classification, summarising short text, searching your own notes.

So a sensible local-first design is often a mix:

Task	Often runs locally	Often runs remotely
Speech-to-text	Yes	Also common
Search over your own notes	Yes	Sometimes
Short summaries and tagging	Possible	Common
Long reasoning, large coding tasks	Rarely	Usually

The guide on on-device meeting transcription looks at one of these in detail.

What "only what the task needs" means

When a remote model is involved, something has to leave your device. Local-first design is about keeping that to the minimum and making it visible.

In a cloud-first tool, your whole workspace may be uploaded and indexed on the vendor's servers so that the AI can search it. In a local-first tool, the index sits on your machine. When you ask a question, the tool finds the relevant passages locally and sends only those, with your question, to the model.

The difference is between a standing copy of everything and a small, task-sized extract. The guide on what gets sent to an AI provider explains what that extract usually contains.

The benefits

• Fewer custodians. If the tool vendor never stores your content, there is one less party to assess, and one less place for a breach to happen.
• Works offline, at least in part. Your notes and files are there without a connection. Anything that needs a remote model will wait.
• Speed for local operations. Opening, searching and editing do not wait on a server round trip.
• Durability. Files in open formats outlive the tool. You can move to another editor or another vendor without an export project.
• Clearer answers to compliance questions. "Where is the data?" has a short answer.

The costs

Local-first is not free, and it is worth being honest about that.

• You are the custodian. Backups, disk encryption and lost-laptop plans become your job. A cloud vendor's operations team is no longer covering for you.
• Sync is hard. Keeping several devices and several people in step without a central server is a real engineering problem. Techniques such as conflict-free replicated data types exist for this, but edge cases remain, and many local-first tools still use a server for sync while keeping the device copy primary.
• Hardware limits. Local models are bounded by the machine. Battery, heat and memory all matter.
• Shared work needs thought. Team features such as permissions, audit and search across everyone's material are simpler with a central server. Local-first tools have to design these deliberately.
• Updates and support. Software running on many different machines is harder to keep uniform than one hosted service.

How to tell whether a tool is really local-first

Marketing uses the term loosely. A few checks cut through it:

1. Disconnect from the network. What still works?
2. Find your data on disk. Can you open it with another program?
3. Ask where your sign-ins and keys are stored.
4. Ask what is uploaded in the background, not only when you press send. This includes indexing, telemetry and crash reports.
5. Ask what happens to your data if you stop paying.

If the answers are vague, the list of questions to ask an AI vendor gives a fuller set.

When local-first is not the right choice

Some work is better served centrally. Large shared datasets, heavy model training, and systems that many people must query at once belong on servers. A team with no capacity to manage devices may be safer with a well-run hosted service than with sensitive files scattered across unmanaged laptops. Local-first is a design preference, not a rule. The aim is to know where your data is, and to have chosen that on purpose.

Where Prism fits

Prism Desktop follows this pattern. Its vault is Markdown notes you own, keys stay in the computer's keychain, speech-to-text for meetings runs on your own device, and only the prompt goes to the AI provider you chose. It is Linux first, with Windows and macOS in progress, and early access is by waitlist. See Prism Desktop or join the waitlist.