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Please reach out to us at enterprise@bolna.ai or schedule a call https://bolna.ai/meet for the Docker images and pricing info.

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1. Introduction

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Why on-premise?

Deploying Voice AI infrastructure on your own server (on-premises or self-managed cloud infrastructure) instead of relying entirely on third-party SaaS solutions has several strategic, technical, and operational advantages, especially for companies focused on privacy, control, and performance.

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Security

With an on-premises deployment, all data remains within your corporate network, ensuring enhanced security as it is not transmitted over the Internet. This setup helps in complying with strict data privacy and protection regulations.

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Components

Bolna Architecture Diagram

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Prerequisites

Docker: Install Docker on your system to manage the containerized application. 
# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
 "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
 $(. /etc/os-release && echo \"$VERSION_CODENAME\") stable" | \
 sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

# Install the latest version of Docker
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

# Verify Docker is running
sudo docker run hello-world

# Install Docker Compose plugin
sudo apt install -y docker-compose-plugin

# Verify Docker Compose version
docker compose version
Hardware Specifications: Storage and compute requirements
  • Instance type: c6a.xlarge
  • Object storage: AWS S3
  • Relational Database: PostgreSQL 16.3, RAM: 8GB+
  • Caching layer: Redis 7.10, 4GB RAM (Instance type: cache.t4g.medium)
  • Message Queueing Channel: RabbitMQ 13.13.7, RAM: 8GB (Instance type: mq.m5.large)

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2. Deployment Environments

This documentation will cover specific instructions and considerations for deploying the services within an AWS environment, ensuring optimal configuration and performance.

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3. Self-Service Licensing & Credentials

Self-hosting key can be either generated from our dashboard or contact enterprise@bolna.ai

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4. Deploy All Services

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Login to Bolna’s ghcr

echo <GITHUB_PAT> | docker login ghcr.io -u <GITHUB_USERNAME> --password-stdin

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Pull images

docker pull ghcr.io/bolna-ai/api_server:v1
docker pull ghcr.io/bolna-ai/ws_server:v1
docker pull ghcr.io/bolna-ai/telephone_server:v1
docker pull ghcr.io/bolna-ai/q_manager:v1
docker pull ghcr.io/bolna-ai/q_worker:v1
docker pull ghcr.io/bolna-ai/arq_worker:v1

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Docker Compose File:

Create a docker-compose.yml File 
version: '3.8'
services:
  api_server:
    image: ghcr.io/bolna-ai/api_server:v1
    container_name: api_server
    ports:
      - "5001:5001"
    env_file:
      - .env
    restart: always
  telephone_server:
    image: ghcr.io/bolna-ai/telephone_server:v1
    container_name: telephone_server
    ports:
      - "8001:8001"
    env_file:
      - .env
    restart: always
  q_worker:
    image: ghcr.io/bolna-ai/q_worker:v1
    container_name: q_worker
    ports:
      - "5002:5002"
    env_file:
      - .env
    restart: always
  q_manager:
    image: ghcr.io/bolna-ai/q_manager:v1
    container_name: q_manager
    ports:
      - "5003:5003"
    env_file:
      - .env
    restart: always
  ws_server:
    image: ghcr.io/bolna-ai/ws_server:v1
    container_name: ws_server
    ports:
      - "5005:5005"
    env_file:
      - .env
    restart: always
  arq_worker:
      image: ghcr.io/bolna-ai/arq_worker:v1
      container_name: arq_worker
      env_file:
        - .env
      restart: always
      command: ["arq", "arq_worker.WorkerSettings"]

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Start docker compose:

docker compose up -d
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