Skip to content
RunAnywhere

Backed by Y Combinator

We make the best open models faster and cheaper to run, for everyone.

Access to intelligence should not depend on who can pay for it. We serve frontier open models through Wally, one API key and one CLI. Under it, our AI agents build the kernels and the full serving stack, so every model can run faster and cost less.

Copy this into your agent

What we build

Run open models in the cloud with Wally, or on your own devices with our open-source SDKs.

Our product

Wally

Frontier open models behind one OpenAI-compatible API, and a CLI that connects your coding agent to them in one command.

  • Ten models in the catalog, with fast versions for agent loops
  • Works with OpenCode, Claude Code, Codex and more
  • Zero data retention by default
Visit runwally.com

How it fits together

A request, from your agent to the model and back.

  1. 01

    Your app or agent sends a request

    Use the OpenAI SDK you already have, pointed at inference.runanywhere.ai/v1, or run wally opencode --cloud to put your coding agent on a hosted model.

  2. 02

    Wally takes it

    One API key and one endpoint for every model in the catalog. Usage is paid from prepaid credits you buy in the console.

  3. 03

    An engine runs the model

    Wally sends the request to an engine serving that model. Fast versions of a model run on capacity tuned for speed, for agent loops and anything a user waits on.

  4. 04

    Tokens stream back

    The reply streams back as the model writes it. Wally does not store your prompt or the reply.

Our mission

Access to intelligence is a fundamental human right. We exist to make it universal.
Sanchit Monga, Co-founder and CEO

In practice that means faster, cheaper inference. The best open models should be fast, affordable and one line of code away for anyone who builds with them, wherever they are.

Every time a model gets faster and a token gets cheaper, more people can build with intelligence. So we host those models well, and our AI agents build the kernels and the serving stack under them, because that is where speed and cost are decided.

Read why we build inference for everyone

From the lab

What we're writing.

All posts