Your first InferCrane request

In this guide you will start the complete control and request path, call a stable model endpoint, and inspect the durable state behind it. No GPU or cloud account is required.

The local stack uses GPU-free fake vLLM workers. It proves application and lifecycle behavior, not model quality, GPU performance, or real-provider compatibility.

See the complete safety loop first

From a repository checkout, one command runs the local product proof and cleans up its isolated Docker project:

make demo

It connects an existing endpoint, sends and inspects a request, creates an intentionally unready candidate without provisioning it, records a deterministic Release Guard rejection, verifies the active revision did not change, rejects the candidate, and removes all temporary state. The output is explicitly marked as fixture evidence; it does not claim GPU or provider qualification.

Prerequisites

Git
Docker with Compose v2
Port 18000 available

Clone InferCrane

git clone https://github.com/infercrane/infercrane.git
cd infercrane

Start the local stack

docker compose up --build -d
curl -fsS http://127.0.0.1:18000/readyz

The stack includes PostgreSQL, InferCrane, two fake workers, and a development router. The development API key is infercrane; production mode rejects that default.

Send a request

docker compose exec infercrane \
  infercrane request qwen-prod --message "Explain durable operations in one sentence."

curl -fsS http://127.0.0.1:18000/v1/chat/completions \
  -H 'Authorization: Bearer infercrane' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "qwen-prod",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:18000/v1",
    api_key="infercrane",
)

response = client.chat.completions.create(
    model="qwen-prod",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://127.0.0.1:18000/v1",
  apiKey: "infercrane",
});

const response = await client.chat.completions.create({
  model: "qwen-prod",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.choices[0].message.content);

Add --stream to the CLI request to print response text as chunks arrive.

Inspect what happened

docker compose exec infercrane infercrane status qwen-prod
docker compose exec infercrane infercrane events qwen-prod
docker compose exec infercrane infercrane explain qwen-prod

These commands use the authenticated control-plane API. Public CLI workflows never connect directly to PostgreSQL.

Open the terminal operations view

docker compose exec infercrane infercrane ui

The terminal workspace reads the same durable control API and can be closed without cancelling operations. The separately released browser console is currently in private preview; see Operations console for the local self-hosted boundary.

Stop the stack

docker compose down

This preserves PostgreSQL data. Add --volumes only when you intend to delete the local state.

Choose your next path

Deploy a real model

Plan provider and runtime choices before creating billable capacity.

Connect an endpoint

Observe an existing vLLM, LiteLLM, or OpenAI-compatible endpoint first.

Understand the model

Learn endpoints, environments, serving plans, deployments, and revisions.

Before creating real infrastructure

The Homebrew formula and release archives are prepared but not yet publicly released. Until the release package is published, build the CLI from source:

go build -o ./bin/infercrane ./cmd/infercrane
export INFERCRANE_API_KEY='ISSUED_CONTROL_PLANE_CREDENTIAL'
./bin/infercrane init --url https://infercrane.example
./bin/infercrane auth status
./bin/infercrane doctor --cloud
./bin/infercrane plan Qwen/Qwen3-8B --cloud runpod --gpu L40S

plan is side-effect free. A real deploy can create billable resources, so check provider setup and the capability matrix first. Adapter registration does not prove that an exact model, runtime, GPU, and provider combination has passed real-infrastructure qualification.

GET STARTED

BUILD

OPERATE

INTELLIGENCE

INTEGRATE

REFERENCE

PROJECT

Quickstart

Your first InferCrane request

See the complete safety loop first

Prerequisites

Choose your next path

Deploy a real model

Connect an endpoint

Understand the model

Before creating real infrastructure

​Your first InferCrane request

​See the complete safety loop first

​Prerequisites

​Choose your next path

Deploy a real model

Connect an endpoint

Understand the model

​Before creating real infrastructure

Your first InferCrane request

See the complete safety loop first

Prerequisites

Choose your next path

Before creating real infrastructure