On-premise Deployment Options
=============================

Scaleout Edge is designed to scale from a single laptop to a multi-region cloud cluster. To support this flexibility, we offer two primary deployment architectures:

* **Starter (Docker Compose)**: For R&D, proof-of-concept, and small-scale pilots.
* **Production (Kubernetes)**: For mission-critical, high-availability enterprise deployments.

1. Starter: Docker Compose
--------------------------

The Starter deployment runs the entire Control Plane (UI, Controller, Combiner, Database) on a single machine using Docker Compose.

Use Cases
~~~~~~~~~

* **R&D & Prototyping**: Rapidly iterate on model architectures and client logic locally.
* **Small Fleets**: Manage up to ~50 edge nodes for a pilot study.
* **Air-Gapped Demos**: Run a complete, self-contained network without internet access.

Architecture
~~~~~~~~~~~~

In this mode, all services run as containers on a shared Docker network:

* **Postgres**: Stores metadata, users, and audit logs.
* **MinIO**: Acts as a local S3-compatible object store for models and compute packages.
* **Controller & Combiner**: Run as lightweight services exposing ports 8090 (API) and 12080 (gRPC).

Getting Started
~~~~~~~~~~~~~~~

To spin up a Starter instance:

.. code-block:: bash

    # 1. Clone the repository
    git clone https://github.com/scaleoutsystems/scaleout-stack.git
    cd scaleout-stack

    # 2. Start the stack
    docker compose up -d

    # 3. Access the UI
    # Navigate to http://localhost:8090

2. Production: Kubernetes (K8s)
-------------------------------

For enterprise scale, high availability, and strict security requirements, Scaleout Edge should be deployed on Kubernetes. We provide official Helm Charts to automate this process.

Use Cases
~~~~~~~~~

* **Large Scale**: Orchestrating thousands or millions of edge nodes.
* **High Availability**: Redundant Controllers and Combiners to ensure zero downtime.
* **Enterprise Integration**: Connecting to external identity providers (OIDC/LDAP), managed databases (RDS/CloudSQL), and observability stacks (Datadog/Prometheus).

Architecture Changes
~~~~~~~~~~~~~~~~~~~~

Moving to production introduces several architectural enhancements:

.. list-table::
   :header-rows: 1
   :widths: 25 35 40

   * - Component
     - Starter (Docker)
     - Production (K8s)
   * - Orchestration
     - docker-compose.yml
     - Helm Charts
   * - Storage
     - Local MinIO Container
     - Managed Object Storage (AWS S3, Azure Blob, GCS)
   * - Database
     - Local Postgres or MongoDB Container
     - Managed Database (OEM or Self-Hosted)
   * - Ingress
     - Direct Port Exposure
     - K8s Ingress Controller (NGINX/Traefik) with TLS termination
   * - Scaling
     - Single Instance
     - Horizontal Pod Autoscaling (HPA)

Deployment Workflow
~~~~~~~~~~~~~~~~~~~

Deploying to a K8s cluster (e.g., EKS, AKS, GKE) typically involves:

1. **Configure values.yaml**: Define your domain, external database credentials, and resource limits.
2. **Install via Helm**:

.. code-block:: bash

    helm repo add scaleout https://charts.scaleoutsystems.com
    helm install scaleout-edge scaleout/scaleout-edge -f values.yaml

3. **Scale**: As your fleet grows, simply increase the ``replicaCount`` of the services (or use Horizontal Pod Autoscaling) to handle more concurrent connections.