Private Beta · Private by default. Cheaper to run.

Turn employee devices
into a private, lower-cost inference layer

Cloudless AI routes requests to idle local compute first, so sensitive data stays on-device and cloud spend stays low. Same APIs/SDKs, different URL.

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

Why Cloudless AI

Built for endpoint fleets, not generic AI infrastructure

Cloudless AI is for organizations that want to use the spare compute already sitting on employee devices instead of paying cloud rates for every token.

Built for devices people already use

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

Private by default

Keep prompts and outputs on the device fleet when local capacity is available, with policy controls for what can leave the network.

One control plane for fallback

Use the endpoint fleet first and fall back to cloud models only when needed — without changing application code.

Everything you need

Private inference on employee endpoints,
without paying cloud-only inference prices

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

🔌

Drop-in compatible

Point your existing OpenAI or Anthropic SDK at the Cloudless AI 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.

⚖️

Spend-aware scheduling

The router picks the least-loaded, most-capable endpoint first, so you use local capacity before you pay cloud prices.

☁️

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, request telemetry, and where cloud spend was avoided.

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, routing behavior, and the cloud spend you avoided in the dashboard.

Turn employee endpoints into a cheaper private inference layer

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

Request access →