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

1. Introduction

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.

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.

Components

Bolna Architecture Diagram

Prerequisites

Docker: Install Docker on your system to manage the containerized application.
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)

2. Deployment Environments

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

3. Self-Service Licensing & Credentials

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

4. Deploy All Services

Login to Bolna’s ghcr

Pull images

Docker Compose File:

Create a docker-compose.yml File

Start docker compose: