Private Beta

Turn employee devices
into your private inference fleet

Cloudless AI turns the laptops and desktops your team already manages into a live inference pool. It schedules work onto idle NPU and GPU capacity, then falls back to the cloud only when necessary. Same SDK. Different base URL.

Join waitlist → Read the docs
We are onboarding design partners in phases while we validate device-aware scheduling, IT controls, and load shedding on real employee hardware for non-mission-critical agents.
app.py
# Keep your app code. Swap the router.
client = OpenAI(
  base_url="http://cloudless-router:8080/v1",
  api_key="your-org-token",
)

Why Cloudless

Built for endpoint fleets, not generic AI infrastructure

Cloudless is for organizations that want to use the spare compute already sitting on employee devices.

Built for devices people already use

Cloudless is designed around employee laptops and desktops, not datacenters, racks, or permanent server pools.

IT and policy first

The router understands device load, model availability, and enterprise policy so ops teams can steer inference with intent.

One control plane for fallback

Use the endpoint fleet when it is available and fall back to cloud models when it is not — without changing application code.

Everything you need

Private inference on employee endpoints,
without building a generic AI platform

Deploy in minutes. Use idle laptops and desktops. Keep sensitive data on the device fleet.

🔌

Drop-in compatible

Point your existing OpenAI or Anthropic SDK at the Cloudless router. Same app code, same request shape, same org tokens.

📡

IT-managed device fleet

Employee laptops and desktops register automatically, report hardware capability, and stay visible to the router across subnets.

⚖️

VRAM-aware scheduling

The router picks the least-loaded, most-capable endpoint for each request and can shed work when local load rises.

☁️

Seamless cloud fallback

When the endpoint fleet is saturated, the router falls back to OpenAI, Anthropic, or any OpenAI-compatible endpoint automatically.

🔒

Policy-driven routing

Define which models may use cloud fallback. Sensitive requests stay inside the employee device pool with a full audit trail.

📊

Operational visibility

The dashboard shows live device status, routing decisions, model availability, and request telemetry by team.

Getting started

How the endpoint fleet works

1

Register devices

Install the lightweight agent on employee laptops and desktops with spare GPU or NPU capacity. They report in automatically.

2

Set policy

Tell the router which models may use endpoint hardware, when to fall back, and how aggressively to shed load.

3

Point your SDK

Change one line: set base_url to your router. Your existing OpenAI or Anthropic code works unchanged.

4

Watch the fleet

Track device health, model readiness, and routing behavior in the dashboard.

Turn employee endpoints into a private inference fleet

We're onboarding a limited number of early design partners in private beta to validate endpoint fleet routing, load shedding, and policy controls.

Request access →