This guide covers how to deploy Kerdos AI services or the Kerdos API Platform in your environment, ensuring scalability, security, and high availability.

• Docker installed on your server or local machine
• Kubernetes cluster (optional for large-scale deployment)
• Access to cloud services (AWS, Azure, GCP) if deploying in the cloud
• API credentials from Kerdos platform

1. Clone the repository:

git clone https://github.com/kerdosdotio/kerdosai.git
cd kerdosai

2. Build the Docker image:

docker build -t kerdosai:latest .

3. Run the container:

docker run -d -p 8000:8000 kerdosai:latest

4. Access the API at http://localhost:8000

1. Prepare your Kubernetes cluster.

2. Apply deployment manifests:

kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

3. Verify pods are running:

kubectl get pods

4. Access the service through the configured LoadBalancer or Ingress.

• Use Terraform scripts (if available) or cloud provider CLI to provision infrastructure.
• Deploy Docker containers or Kubernetes clusters as per your cloud setup.
• Configure API Gateway and DNS routing.
• Enable monitoring and logging via Prometheus, Grafana, or cloud-native tools.

• Set environment variables for API keys, database URLs, and other secrets securely.
• Configure scaling parameters based on expected load.
• Enable HTTPS and security best practices (firewalls, IAM roles).

• Use Prometheus & Grafana dashboards for real-time monitoring.
• Set up alerts for uptime, latency, and error rates.
• Regularly update images and dependencies for security patches.

• Check container logs:

  docker logs <container_id>

• For Kubernetes pods:

  kubectl logs <pod_name>

• Verify network connectivity and API endpoint accessibility.

• [Kerdos API Documentation](API-Reference)
• [GitHub Repository](https://github.com/kerdosdotio/kerdosai)
• Contact support at [[email protected]](mailto:[email protected])

© 2025 Kerdos Infrasoft Private Limited