Extract Metadata Retriever

Ardor Docs

Build, deploy and iterate on powerful agentic apps fast using natural language with Ardor Cloud

Welcome!

Our philosophy - all software should be agentic and build in minutes.

Philosophy

The Ardor Cloud Console is the central interface for managing AI agents, datasets, models, and related resources. It provides a structured environment for users to build, deploy, and maintain AI-powered applications efficiently.

About Console

The Canvas is the core interface for designing, configuring, and deploying AI agents within Ardor Cloud. It provides a visual environment where users can build agents by defining their architecture, connecting components, and managing various operational settings.

Canvas

The Account Settings page allows users to manage their personal information, login credentials, and account-related settings within Ardor's workspace. This section provides an overview of the available features and functionalities.

Manage Your Account

Learn how to add and manage team members in Ardor

Managing Your Team

Credentials provide API keys and tokens to use across your agentic applications. This page explains how to create, manage, and use different types of credentials in the Ardor platform.

Credentials

Ardor provides a transparent, usage-based pricing model that ensures you only pay for the computing resources your AI Agents consume. Unlike token- or credit-based systems, Ardor’s pricing is straightforward, based on real infrastructure resources (CPU, RAM, vRAM, and Storage).

Pricing and Billing Overview

Ardor's pricing is based on a metered usage model, ensuring that you only pay for the actual runtime of your AI Agents. When an AI Agent is not in use, no charges apply, making the platform cost-efficient and scalable.

Metered Usage and Billing

Nodes are the fundamental building blocks similar to LEGO, you can build a customized LLM ochestration flow, a chatbot, an agentic app with all the integrations available in Ardor or build your own custom nodes. They represent various microservices with specific functionalities.

Overview

Components Overview

The Analytics category contains nodes designed to integrate with various analytics and observability platforms for AI and Large Language Model (LLM) applications. These nodes help developers and teams monitor, analyze, and optimize their AI systems' performance, costs, and behavior.