wide area network → wide area intelligence
Your hardware is
the datacenter.
The cloud is the failover.
Wide Area Intelligence is an OpenAI-compatible AI gateway that routes every request to your own hardware first — office workstations, on-prem servers, lab machines — and silently fails over to the cloud when it has to. It's an LLM gateway for the models you self-host: your apps never know the difference, and requests served by your own hardware carry no per-token fees.
| dst | via | metric | state |
|---|---|---|---|
| inference/* | node-01.tunnel | 0 | UP |
| inference/* | openrouter.ai | 100 | STANDBY |
| cache/hit | edge.kv | — | 38.2% |
/// the explainer
The idea, in a couple of minutes.
What Wide Area Intelligence is, and why running inference on your own hardware first — cache, then your GPUs, then the cloud as plan B — costs less than sending every token to someone else's API.
/// powering inference across the portfolio
/// what brings you here
One gateway. Three reasons to run it.
Your AI spend is too high
Run the bulk of your traffic on hardware you own — no per-token fees — and burst to the cloud only for the hard part. OpEx becomes CapEx.
Explore →keep it privateYour data can't leave
Inference runs on machines you control and is never fed into Big AI training loops. Disable cloud failover and nothing leaves your network.
Explore →own the stackYou want to run your own AI
Any open-weight model, on the hardware you already have, pooled behind one endpoint — no vendor deciding what you can run.
Explore →/// how it works
The AI gateway, in three tiers. Zero code changes.
EDGE CACHE
Identical request seen before? Served instantly from Cloudflare's edge — the prompt never touches a GPU. Toggle it per-account, set your own TTL.
YOUR HARDWARE
A one-line Docker command turns any machine with a GPU into a node. It opens a secure Cloudflare Tunnel — no port forwarding, no static IP — and serves llama.cpp behind your gateway.
CLOUD FAILOVER
Node offline? Timed out? The same request silently re-routes to the cloud — GPT, Gemini, Claude — through our managed gateway, billed only against your prepaid credits. If one provider has an outage, the gateway fails over to the next. Your app sees one response, every time.
/// node setup
One command.
Any machine. Any OS.
The installer finds Docker, Podman, or Colima — or sets one up for you — then starts an agent bundling llama.cpp and a Cloudflare Tunnel. Run it on a workstation, a Mac Studio, or a rack server in the office: it registers itself and starts serving traffic in about a minute.
$ curl -fsSL https://wideareaai.com/install.sh \ | sh -s -- --key wai_node_… [wai] Platform: linux [wai] Using container runtime: docker [wai] NVIDIA GPU passthrough enabled ✓ tunnel established ✓ node registered · node-01 ONLINE
/// app integration
Keep your SDK.
Change one URL.
Anything that speaks the OpenAI API speaks Wide Area Intelligence — Python, TypeScript, LangChain, agents, curl. Point it at the gateway and routing, caching, and failover happen automatically.
from openai import OpenAI client = OpenAI( base_url="https://wideareaai.com/api/v1", api_key="wai_sk_…", ) resp = client.chat.completions.create( model="llama-3.1-8b-instruct", messages=[…], )
/// pricing
Own your baseline. Burst to the cloud.
Your GPUs serve the baseline with no per-token fees — your costs are the nodes and the power bill. The first two nodes are free; each node after that is $100/mo ($1000/yr). Team seats are unlimited. Cloud failover is prepaid credits, billed per request only on the overflow.
STARTER
- 2 compute nodes — free
- Unlimited team seats
- 2M requests / mo per node
- Exact-match edge cache
- Cloud failover (prepaid credits)
PER NODE
- Everything in Starter
- Each node beyond the first 2
- Or $1000 / yr per node (2 months free)
- Auto-billed as you add & remove nodes
- Cloud usage still metered on credits