On-Premises Private

CARTO can be installed on a local server, or set of servers, either directly or using our commercial installer.

Introduction

All the components from the CARTO stack are packaged in RPM. The dependencies are bundled with a powerful set of management tools, and installation and configuration are completely handled by our CLI (Command Line Interface).

These are the CARTO stack components that are installed with our On-Premises offering:

  • PostgreSQL (+ PostGIS): Geospatial Database powering CARTO. It is required for both the User Data Storage and the Metadata Storage databases.
  • Redis: Cached metadata Database, used by most components of the CARTO application stack to store configuration and cache.
  • SQL API: SQL wrapper REST API that provides a node.js based API for running SQL queries against CARTO. For user documentation, view https://carto.com/docs/carto-engine/sql-api/.
  • Maps API: Tile server REST API. It provides a node.js based API that allows you to generate maps based on data hosted in your CARTO account, by applying custom SQL and CartoCSS to the data. For user documentation, view https://carto.com/docs/carto-engine/maps-api/.
  • Builder: Web mapping application of CARTO. For user documentation, view https://carto.com/learn/guides.
  • Varnish: Cache layer that works as a web application accelerator and caching HTTP reverse proxy.
  • Nginx: Free, open-source, high-performance HTTP server and reverse proxy server. It is used to proxy and control the behavior of HTTP requests to backend applications. It’s also used as HTTP termination.
  • LDS: Set of Location Data Services offered by CARTO. For more information, view https://carto.com/developers/data-services-api/.

CARTO On-Premises allows you to make use of a full production-ready platform with optimal performance by running our stack in one single instance, or via several distributed instances within your private infrastructure.

You can choose one of our available tiers depending on your needs (concurrency, security policies, data volume, data structure complexity, performance…).