On-Premises Deployment

Updated on Jun 19, 2026

The Kendo UI for Angular Agentic UI Generator supports on-premises deployment through a Helm chart that installs the Context API stack into your Kubernetes cluster. The deployment includes the Context API service, a bundled reranking service, and Qdrant for vector storage.

The on-premises package is simplified for customer-hosted environments. This deployment model removes hosted-only concerns such as authentication, rate limiting, and telemetry. If needed, handle those controls at the ingress, API gateway, or cluster level.

Prerequisites

Before you install the chart, make sure the cluster provides:

• Persistent storage for Qdrant
• Access to an OpenAI-compatible inference endpoint that exposes the models required by Context API for request handling and embeddings
• A public or internal DNS name for the Context API service

Deployment Steps

1. Install the Helm chart in the target cluster.
2. Configure the Context API deployment to use the in-cluster Qdrant service.
3. Configure the inference endpoint and the model names used by Context API.
4. Expose the Context API service on a stable URL.
5. Configure the MCP server to use that URL through the CONTEXTAPI_URL environment variable.

Required Context API Configuration

Set the following environment variables for the Context API deployment:

Variable	Description
VECTOR_DB_TYPE	Set to qdrant
QDRANT_HOST	The Qdrant service name inside the cluster
QDRANT_GRPC_PORT	The Qdrant gRPC port, typically 6334
QDRANT_API_KEY	API key for Qdrant, if required
LLM_BASE_URL	The OpenAI-compatible inference endpoint
LLM_API_KEY	API key for the inference endpoint, if required
LLM_DEFAULT_MODEL	The model name used for request handling
LLM_EMBEDDINGS_MODEL	The model name used for embeddings
EMBEDDINGS_DIMENSIONS	The dimension size of the embeddings model
LLM_REQUEST_TIMEOUT	Request timeout in seconds

Qdrant Configuration

A standard deployment requires no manual Qdrant setup. A fresh Qdrant instance is ready to use as long as it is reachable from Context API and has persistent storage configured.

Context API creates the required collections automatically when they do not already exist.

Reranking

The deployment includes reranking and does not require a customer-provided model endpoint.

The standard deployment runs reranking through the bundled cross-encoder service deployed together with Context API. In a standard Helm deployment, reranking works without additional configuration. Set a custom CROSS_ENCODER_URL only if you rename or deploy the reranking service separately.

MCP Server Configuration

After you expose the Context API service, configure the MCP server to use that address by setting CONTEXTAPI_URL:

json

{
    "env": {
        "CONTEXTAPI_URL": "contextapi.customer.example.com:443"
    }
}

CONTEXTAPI_URL must follow the host:port format.

Result

After deployment, the MCP server sends requests to the customer-hosted Context API instance. Context API uses the configured inference endpoint, bundled reranking service, and Qdrant instance to serve retrieval operations inside the customer environment.