What Is Local-First AI? Your Data, Your Keys, Your Machine

Most AI apps you use today are thin clients to someone else's cloud. You type a prompt, it goes to their servers; your files get uploaded so the model can read them; your conversation history lives in their database; and the API key — if there even is one — is theirs, billed back to you with a margin. That arrangement is convenient, and for a lot of uses it's fine. But it quietly makes one assumption: that your data, your work, and your credentials are theirs to hold.

Local-first AI flips that default. This article explains what "local-first" actually means, what changes when you apply it to AI specifically, and how to tell whether a tool that claims to be local-first really is. Orkas is built this way, so we'll use it as the concrete example — but the ideas apply to any tool in this category.

Scope note. The direct-key sections below describe Orkas's bring-your-own-provider path. Orkas also offers optional managed official models; those requests use Orkas's managed model service. Local-first describes workspace defaults and control, not a requirement that every model request bypass Orkas.

Quick answers

What is local-first AI?

Local-first describes where workspace data and the control plane live by default: on your device. It does not require every model call to bypass the app vendor. Orkas offers optional managed official models and also a direct bring-your-own-provider path.

Is local-first AI the same as running a local LLM?

No. Local-first describes where your data, keys, and control plane live — on your machine. The model itself can still be a cloud API such as OpenAI or Anthropic. Running a fully local model (for example with Ollama) is one option within a local-first design, not the definition of it.

What does bring-your-own-key (BYO-key) mean?

Bring-your-own-key means you connect a provider using your own API key or OAuth account. With this path, the key stays on your device and model calls go directly to that provider. It is an alternative to Orkas's optional managed official models.

What "local-first" actually means

The term comes from software design generally, not from AI. Stripped down, a local-first app holds to three properties:

1. Your data lives on your device by default. Documents, history, settings — the source of truth is a file on your own disk, not a row in a remote database.
2. The app works from your machine. The core features run locally; they don't require a round-trip to a server to function.
3. The network is opt-in, not the foundation. Sync, sharing, and collaboration are features you can turn on — not the thing the app is fundamentally made of. Turn them off and the app still works.

Contrast that with the cloud-first SaaS default, where the server is the source of truth and your device is just a window onto it. Close the laptop lid on a cloud-first app and your data was never really yours to begin with; it was always sitting on their infrastructure, governed by their terms, visible to their staff and subpoenas. Local-first inverts the burden of proof: the data is yours unless you explicitly decide to send a copy somewhere.

What changes when the app is an AI agent

For a bring-your-own-provider path, those properties create two additional privacy benefits.

Bring your own key (BYO-key). You supply a provider credential — an API key or OAuth sign-in — and control that provider's models, limits, and bill. In Orkas this is an alternative to optional managed official models.

BYO model calls go direct. When an agent uses your own provider, the request goes straight from your machine to that provider and does not pass through Orkas. Managed official models use Orkas's managed model service instead.

It's worth being precise here, because "the AI runs locally" is a common misreading. Local-first does not mean the model itself runs on your laptop (though it can — more on that below). The model can still be a giant cloud API. What's local is everything around the model: your data, your keys, your agent configuration, and the control plane that decides what to send and when. Local-first is about who holds your stuff, not about where the GPU is.

Where your stuff actually lives

Concretely, in a local-first agent like Orkas, here's the split between what stays put and what can move.

By default, on your own machine:

• Your API keys and OAuth tokens — encrypted at rest and excluded from any sync.
• Your chat history, plans, and generated files.
• Your agent configurations, skills, and knowledge bases.
• Your personal memory — the preferences and facts the agent has learned about you.

The credential handling deserves a closer look, because keys are the most sensitive thing an AI tool touches. The lifecycle of a key in a well-built local-first app is short and contained:

1. You add it        →  paste an API key (or sign in with OAuth), on-device
2. Encrypted at rest →  sealed with authenticated AES-256-GCM before it touches disk
3. Stored locally    →  written to a machine-private config file, excluded from sync
4. Used directly     →  decrypted in memory only to call your provider, straight from your machine

The key never crosses the wire to the app vendor — not at rest, not in transit, not in telemetry. (Authenticated encryption like AES-256-GCM also makes tampering detectable; it guards against casual disclosure — a stray log, a backup that runs OCR, another app reading the folder — which is the realistic threat for an on-disk secret.)

What still leaves — and only on your command

"Local-first" does not mean "nothing ever leaves your machine." Orkas documents each network path: provider and connector calls you initiate, managed features you select, optional sync, and limited product analytics.

• The model call to your own provider. Your prompts and the replies travel directly between your computer and the provider you picked. They leave your machine — but they go to your provider, not to Orkas.
• Connector calls you explicitly authorize. When you connect GitHub, Notion, Google, and so on, those calls go from your machine to that service. The OAuth tokens are stored on your device; for a few providers that rotate their tokens on every refresh, the refresh step is coordinated through Orkas so your devices don't race each other — a deliberately narrow exception, documented in the open.
• Cloud sync, if you turn it on. Multi-device sync is opt-in. Enable it and the data you choose to sync is stored on Orkas servers so it's available across your devices. Leave it off and nothing syncs.
• Limited, privacy-respecting analytics. Aggregate usage events (which features get used) — never your prompts, file contents, message text, or credentials.

For the BYO path, your provider key stays local and its model traffic does not touch Orkas servers. Managed official models, sync, connectors, and other selected cloud features use Orkas services as described on the Security page.

Why local-first matters

This isn't privacy theater. The default of "your stuff stays yours" buys several concrete things.

Data sovereignty. Your conversations, the files an agent reads and writes, the knowledge base you built — they sit on your disk, under your control. You can back them up, inspect them, or delete them, without asking anyone. There's no vendor database holding the canonical copy.

Auditability. With your own provider, you can read the open-source client, watch the network, and confirm the direct prompt path. Managed features have a different documented path through Orkas services.

Model choice. Choose optional managed official models or connect your own provider and switch its models when needed.

Cost transparency. Managed capabilities consume Orkas credits; usage through your own key is billed directly by that provider.

Resilience. Because the core runs on your machine and your data is local, the tool isn't one outage or one discontinued-product email away from taking your work with it.

Local-first vs. a fully local model — a clarification

One distinction trips people up often enough to be worth its own section. "Local-first AI," "on-device AI," and "running a local LLM" are related but not the same.

• Running a local LLM (with something like Ollama) means the model weights run on your hardware. Nothing — prompt or reply — leaves your machine at all. It's maximally private, but bounded by your hardware, so the models are smaller than the frontier cloud ones.
• Local-first AI is about where your workspace data, keys, and control plane live by default. Model traffic follows the path you choose: Orkas-managed official models use Orkas services, while own-provider calls go directly to that provider.

So they compose rather than compete: a local-first tool that also supports a local model gives you the strongest privacy posture available, while the same tool pointed at a cloud API gives you frontier capability with your data still under your control. Local-first is the architecture; a local model is one of the engines you can drop into it.

How to tell if an AI tool is really local-first

The label gets used loosely, so here's a short checklist you can apply to any tool that claims it. Ask:

• Where does workspace data live by default? Local-first means the device is the default source of truth.
• Which network paths exist? A trustworthy product distinguishes direct BYO calls from managed-model, sync, connector, and analytics traffic.
• Is sync opt-in? Local-first data remains local until you deliberately enable synchronization.
• Can I verify the claims? Open-source clients let you inspect code and network behavior.
• Which model path do I want? BYO gives direct provider billing and traffic; managed models trade that path for convenience and Orkas credits.

Wrapping up

Local-first is a stance about defaults: workspace data and control begin on your machine, while each optional network feature has a disclosed path. In Orkas, managed official models use Orkas services and your own-provider calls go directly to that provider.

If you want to see exactly how this is implemented — the key lifecycle, the encryption, the precise list of what stays and what leaves — read the Security & Trust page; because the client is open source, none of it has to be taken on faith. And if you want the engineering underneath the agent itself, see how a single agent is built to run reliably on your machine and how a lead agent coordinates a team of sub-agents.