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AI Controller Concepts

This section provides detailed information about the underlying concepts and architecture of AI Controller. Understanding these concepts will help you make the most of AI Controller's capabilities.

For practical implementation details and features, see the Features section.

Core Concepts

  • Architecture Overview: Learn about the components and design of AI Controller
  • Data Flow: How information moves through the AI Controller system
  • Governance: AI Controller's capabilities for controlling and monitoring LLM usage
  • Models and Providers: Understanding AI models and how AI Controller connects with different LLM services
  • Security Model: Understanding AI Controller's security architecture

Why Understanding AI Controller Architecture Matters

Having a solid understanding of how AI Controller is designed and functions can help you:

  • Make better integration decisions
  • Troubleshoot issues more effectively
  • Design more robust solutions that leverage AI Controller capabilities
  • Plan for scaling and future growth
  • Implement proper security controls
  • Optimize costs and performance

Key Terminology

  • Provider: A LLM service (like OpenAI, Anthropic, etc. or a model running on your own machine/server) that AI Controller can connect to
  • Model: A specific language model offered by a provider (e.g., GPT-4, Claude 3, etc.)
  • Rule: A configuration that controls access to models and providers
  • API Key: Authentication token used by applications to access AI Controller
  • Cache: Storage of responses to improve performance and reduce costs
  • Access Control: The system for managing who can access which resources
  • Request Routing: How AI Controller directs requests to the appropriate provider
  • Governance: The framework for controlling, monitoring, and securing LLM usage

Updated: 2025-05-15