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Deploy Context7 On-Premise on Kubernetes using raw manifests. This guide assumes you have completed the On-Premise setup and have a valid license key.

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Prerequisites

  • Kubernetes cluster (v1.24+)
  • kubectl configured for your cluster
  • A StorageClass that supports ReadWriteOnce volumes
  • Context7 license key

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Registry Authentication

Context7 Enterprise images are hosted on ghcr.io and require authentication. Create an image pull secret using your license key: 
LICENSE_KEY="<your-license-key>"

# Get a registry token from Context7
TOKEN=$(curl -s -H "Authorization: Bearer $LICENSE_KEY" \
  https://context7.com/api/v1/license/registry-token | jq -r '.token')

# Create the namespace and secrets
kubectl create namespace context7

kubectl create secret docker-registry context7-registry \
  --namespace context7 \
  --docker-server=ghcr.io \
  --docker-username=x-access-token \
  --docker-password="$TOKEN" \
  --dry-run=client -o yaml | kubectl apply -f -

kubectl create secret generic context7-config \
  --namespace context7 \
  --from-literal=LICENSE_KEY="$LICENSE_KEY" \
  --dry-run=client -o yaml | kubectl apply -f -

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Manifests

Context7 Enterprise runs as a single-replica StatefulSet with persistent storage. The manifests below define the core resources: a StatefulSet for the application, a Service for internal routing, and an Ingress for external access.

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StatefulSet

Context7 uses SQLite and LanceDB for local storage, which require a persistent volume. This means it must run as a StatefulSet with a single replica since SQLite does not support concurrent writers.
statefulset.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: context7
  namespace: context7
spec:
  serviceName: context7
  replicas: 1
  selector:
    matchLabels:
      app: context7
  template:
    metadata:
      labels:
        app: context7
    spec:
      imagePullSecrets:
        - name: context7-registry
      terminationGracePeriodSeconds: 60
      containers:
        - name: context7
          image: ghcr.io/context7/enterprise:latest
          imagePullPolicy: Always
          ports:
            - containerPort: 3000
              name: http
          env:
            - name: LICENSE_KEY
              valueFrom:
                secretKeyRef:
                  name: context7-config
                  key: LICENSE_KEY
          volumeMounts:
            - name: data
              mountPath: /data
          resources:
            requests:
              cpu: "1"
              memory: "2Gi"
            limits:
              cpu: "4"
              memory: "8Gi"
          startupProbe:
            httpGet:
              path: /api/health
              port: http
            initialDelaySeconds: 5
            periodSeconds: 5
            failureThreshold: 12
          livenessProbe:
            httpGet:
              path: /api/health
              port: http
            periodSeconds: 30
            timeoutSeconds: 5
            failureThreshold: 3
          readinessProbe:
            httpGet:
              path: /api/health
              port: http
            periodSeconds: 10
            timeoutSeconds: 5
            failureThreshold: 3
  volumeClaimTemplates:
    - metadata:
        name: data
      spec:
        # storageClassName: gp3  # Set this if your cluster has no default StorageClass
        accessModes: ["ReadWriteOnce"]
        resources:
          requests:
            storage: 10Gi
Storage class: If your cluster does not have a default StorageClass, the PVC will stay in Pending and the pod won’t start. Uncomment storageClassName and set it to a StorageClass available in your cluster (e.g. gp3 on AWS EKS, standard on GKE, default on AKS). Run kubectl get sc to see available options.
Resource sizing: The defaults above (1 CPU / 2 GiB request) work for light usage. If you are parsing many large repositories concurrently, increase the limits. Parsing is CPU and memory intensive due to LLM calls and vector indexing.
Do not set replicas higher than 1. Context7 uses SQLite which only supports a single writer. Running multiple replicas will cause database lock errors.

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Service

service.yaml
apiVersion: v1
kind: Service
metadata:
  name: context7
  namespace: context7
spec:
  selector:
    app: context7
  ports:
    - port: 3000
      targetPort: http
      protocol: TCP
      name: http
  type: ClusterIP

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Ingress

ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: context7
  namespace: context7
  annotations:
    nginx.ingress.kubernetes.io/proxy-body-size: "50m"
spec:
  ingressClassName: nginx
  tls:
    - hosts:
        - context7.internal.yourcompany.com
      secretName: context7-tls
  rules:
    - host: context7.internal.yourcompany.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: context7
                port:
                  number: 3000
Replace context7.internal.yourcompany.com with your actual hostname and context7-tls with your TLS secret.

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Apply Everything

After creating the namespace and secrets in the Registry Authentication step, apply the manifests: 
kubectl apply -f statefulset.yaml
kubectl apply -f service.yaml
kubectl apply -f ingress.yaml
Verify the pod is running: 
kubectl get pods -n context7
kubectl logs -n context7 context7-0
Once the pod is ready, open your Ingress hostname in a browser to complete the setup wizard.

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Networking Requirements

Context7 requires outbound connectivity to the following:
DestinationPurpose
ghcr.ioContainer image pulls (imagePullPolicy: Always)
context7.comLicense validation
Your LLM provider (e.g. api.openai.com)AI inference and embeddings
github.com / gitlab.comRepository cloning
If you use NetworkPolicies, ensure egress to these endpoints is allowed:
networkpolicy.yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: context7-egress
  namespace: context7
spec:
  podSelector:
    matchLabels:
      app: context7
  policyTypes:
    - Egress
  egress:
    - {} # Allow all egress (simplest)
For stricter policies, allow egress on port 443 to the specific domains listed above, and ensure egress to kube-dns on port 53 (UDP/TCP) is permitted for DNS resolution.

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Operations

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Updating

Pull the latest image and restart: 
kubectl rollout restart statefulset/context7 -n context7
To pin a specific version: 
kubectl set image statefulset/context7 \
  context7=ghcr.io/context7/enterprise:1.2.0 \
  -n context7
If your registry token has expired, refresh it before restarting: 
LICENSE_KEY="<your-license-key>"

TOKEN=$(curl -s -H "Authorization: Bearer $LICENSE_KEY" \
  https://context7.com/api/v1/license/registry-token | jq -r '.token')

kubectl create secret docker-registry context7-registry \
  --namespace context7 \
  --docker-server=ghcr.io \
  --docker-username=x-access-token \
  --docker-password="$TOKEN" \
  --dry-run=client -o yaml | kubectl apply -f -

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Health Monitoring

The /api/health endpoint returns structured JSON with license status, connectivity, and parsed repo count. Point your monitoring stack at it: 
kubectl exec -n context7 context7-0 -- \
  wget -qO- http://localhost:3000/api/health

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Logs

# Follow logs
kubectl logs -f -n context7 context7-0

# Check license and startup status
kubectl logs -n context7 context7-0 | head -20

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Troubleshooting

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Pod is in CrashLoopBackOff

Context7 validates your license key on startup. If the key is missing, invalid, or expired, the server exits immediately before the health endpoint is available. This means Kubernetes will report CrashLoopBackOff rather than a failed probe. Check the logs first: 
kubectl logs -n context7 context7-0
Look for [license] messages in the first few lines. Common causes:
  • Missing or incorrect LICENSE_KEY in the context7-config secret
  • No outbound connectivity to context7.com for license validation
  • Expired license: contact context7@upstash.com to renew
The startup probe only comes into play after the license is validated. If the pod is crash-looping, the issue is always upstream of the probe. Check logs, not probe events.

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Connecting AI Clients

Once deployed, point your MCP clients to your Ingress URL. See Connecting Your AI Client for client-specific instructions. Replace localhost:3000 with your Kubernetes Ingress hostname.