AI Runway

Deploy and manage large language models on Kubernetes — no YAML required.

Note

AI Runway is still under heavy development and the APIs are not currently considered stable. Feedback is welcome! ❤️

AI Runway gives you a web UI and a unified Kubernetes CRD (ModelDeployment) to deploy models across multiple inference providers. Browse HuggingFace, pick a model, click deploy.

Demo

Highlights

• 🚀 One-Click Deploy — Browse models, check GPU fit, and deploy from the UI
• 🎯 Unified CRD — Single ModelDeployment API across all providers
• 🔧 Multiple Engines — vLLM, SGLang, TensorRT-LLM, llama.cpp
• 📈 Live Monitoring — Real-time status, logs, and Prometheus metrics
• 💰 Cost Estimation — GPU pricing and capacity guidance
• 🌐 Gateway API Integration — Unified inference endpoint via Gateway API Inference Extension with auto-detected setup
• 🔌 Headlamp Plugin — Full-featured Headlamp dashboard plugin

Supported Providers

Provider	Description	Provider Shim
NVIDIA Dynamo	GPU-accelerated inference with aggregated or disaggregated serving	dynamo.yaml
KubeRay	Ray-based distributed inference	kuberay.yaml
KAITO	vLLM (GPU) and llama.cpp (CPU/GPU) support	kaito.yaml
LLM-D	vLLM (GPU) with aggregated or disaggregated serving	llmd.yaml
Direct vLLM	Direct OpenAI-compatible vLLM Deployments for newest model support	vllm.yaml

Quick Start

Prerequisites

• Kubernetes cluster with kubectl configured
• helm CLI installed
• GPU nodes with NVIDIA drivers (KAITO also supports CPU-only)

Option A: Run Locally

Download the latest release and run:

./airunway

Open http://localhost:3001

macOS: Remove quarantine if needed: xattr -dr com.apple.quarantine airunway

Option B: Deploy to Kubernetes

set -euo pipefail

# Block AgentDeployment CREATE/UPDATE before webhook versions can mix (phase 1)
kubectl apply -f https://raw.githubusercontent.com/ai-runway/airunway/main/deploy/agentdeployment-webhook-guard.yaml

# Install CRDs and roll out without AgentDeployment admission routes (phase 2)
kubectl apply -f https://raw.githubusercontent.com/ai-runway/airunway/main/deploy/controller.yaml

# Force a new ReplicaSet even when the image tag string is unchanged, then wait
# until every webhook Service endpoint runs the replacement pods.
kubectl rollout restart deployment/airunway-controller-manager -n airunway-system
kubectl rollout status deployment/airunway-controller-manager -n airunway-system --timeout=5m

# Activate the complete validator/mutator set, then unblock writes (phase 3).
# Wait until cert-controller injects the serving CA before deleting the guard.
kubectl apply -f https://raw.githubusercontent.com/ai-runway/airunway/main/deploy/agentdeployment-webhook.yaml
kubectl wait secret/airunway-webhook-server-cert -n airunway-system --for=jsonpath='{.data.ca\.crt}' --timeout=5m
AIRUNWAY_WEBHOOK_CA_BUNDLE="$(kubectl get secret/airunway-webhook-server-cert -n airunway-system -o jsonpath='{.data.ca\.crt}')"
test -n "${AIRUNWAY_WEBHOOK_CA_BUNDLE}"
kubectl wait mutatingwebhookconfiguration/airunway-mutating-webhook-configuration --for="jsonpath={.webhooks[?(@.name==\"magentdeployment-v1alpha1.kb.io\")].clientConfig.caBundle}=${AIRUNWAY_WEBHOOK_CA_BUNDLE}" --timeout=5m
kubectl wait validatingwebhookconfiguration/airunway-validating-webhook-configuration --for="jsonpath={.webhooks[?(@.name==\"vagentdeployment-v1alpha1.kb.io\")].clientConfig.caBundle}=${AIRUNWAY_WEBHOOK_CA_BUNDLE}" --timeout=5m
kubectl delete --ignore-not-found=true -f https://raw.githubusercontent.com/ai-runway/airunway/main/deploy/agentdeployment-webhook-guard.yaml

# Install dashboard UI (optional)
kubectl apply -f https://raw.githubusercontent.com/ai-runway/airunway/main/deploy/dashboard.yaml
kubectl port-forward -n airunway-system svc/airunway 3001:80

For production upgrades, use a versioned release and an immutable digest or a new image tag. The explicit restart prevents an unchanged Deployment template from making the rollout check succeed against old pods.

Open http://localhost:3001 — see deployment docs for more options.

Getting Started

1. Install a provider shim — Apply one or more provider shims to register providers with AI Runway. See Supported Providers for available options.
2. Install the provider — Go to the Installation page and install the upstream provider via Helm
3. Connect HuggingFace — Sign in via Settings → HuggingFace (optional for non-gated models)
4. Deploy a model — Browse the catalog, pick a model, configure, and deploy
5. Monitor — Track status, stream logs, and view metrics on the Deployments page

Access Your Model

Deployed models expose an OpenAI-compatible API:

kubectl port-forward svc/<deployment-name> 8000:8000 -n <namespace>
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "<model-name>", "messages": [{"role": "user", "content": "Hello!"}]}'

ModelDeployment CRD

apiVersion: airunway.ai/v1alpha1
kind: ModelDeployment
metadata:
  name: my-model
spec:
  model:
    id: "Qwen/Qwen3-0.6B"

The controller automatically selects the best engine and provider, creates provider-specific resources, and reports unified status. See CRD Reference for details.

Documentation

📖 Browse the docs at ai-runway.github.io/airunway

The same content also lives in docs/ for in-repo browsing.

Topic	Link
Architecture	docs/architecture.md
CRD Reference	docs/crd-reference.md
Providers	docs/providers.md
Observability	docs/observability.md
Development	docs/development.md
Kubernetes Deployment	deploy/README.md
Gateway Integration	docs/gateway.md
Headlamp Plugin	docs/headlamp-plugin.md

Community

• Slack: Join the CNCF Slack workspace and find us in #airunway
• Issues: Report bugs or request features in GitHub Issues
• Roadmap: Follow planned work on the AI Runway project board

Contributing

See CONTRIBUTING.md for development setup. We also accept AI-assisted prompt requests.