Why OpenClaw Runs Locally

Benefits of local execution

Running locally means you decide where data is stored, what files can be accessed, and how logs are retained. For privacy-focused teams, this is a major advantage over cloud-hosted assistants.

The tradeoffs

Local control also requires local maintenance. You must manage updates, secure the gateway, and handle system permissions. If you expose services to the public internet, the risk increases quickly.

How cloud models fit in

Even with a local runtime, you may connect to external model APIs. That means model inference can still happen in the cloud, so you should review your model provider's data policy.

When local is the right choice

If you want an assistant that can touch local files, run automations, and remain within your security boundary, local makes sense. If you only need lightweight chat and no execution, a cloud service may be simpler.

Hybrid setups are common

Many teams run the agent locally but call cloud models for reasoning. This hybrid approach balances control with capability. The key is to be explicit about what data can leave the local environment and to review vendor data policies.

Operational maturity matters

Local systems require regular updates, backups, and monitoring. If you already manage servers or developer tooling, OpenClaw fits naturally. If you do not want that responsibility, a hosted assistant may be the better fit.

Compliance and data policies

Local execution can simplify compliance by keeping data within your environment. However, if you still call cloud models, you must consider that data leaves your network during inference. For regulated industries, document exactly what flows out and why.

Official reference

Architecture details are documented here: Architecture.