What Are the Different Types of AI Models?
Not all AI models are built or used the same way. Some are massive and general-purpose, while others are lightweight or tuned for specific tasks or audiences. Understanding the types and categories of models is key to knowing what a given AI can or cannot do—and whether it’s the right tool for your use case. In this section, we’ll walk through the main ways people classify models: by how they’re trained, how they’re released, what languages they support, and how they’re tuned.Foundation Model
What it means: A foundation model is a large, general-purpose AI model trained on vast amounts of text, code, or images. It serves as the base for many downstream applications. Why it matters: These models are typically trained once, then fine-tuned or adapted for specific uses—making them the “foundation” for a whole ecosystem of tools. GPT-4 and Claude are examples. How it shows up in Gloo: Gloo integrates trusted foundation models from leading providers and layers organizational alignment, rights protection, and retrieval on top of them. These models provide the core language understanding that powers Chat for Teams, enrichment, semantic search, and Studio workflows.Frontier Model
What it means: A frontier model is a cutting-edge model representing the newest and most capable tier of AI performance. Why it matters: These models tend to push the boundaries of intelligence, safety, and scale. They’re often more powerful but also riskier if not carefully aligned. How it shows up in Gloo: Gloo routes you to frontier models when higher reasoning, deeper retrieval, or more complex text generation is needed. Frontier models offer stronger performance, and Gloo applies guardrails and theological alignment to ensure they operate safely in ministry and organizational contexts.Open-Weight Model
What it means: An open-weight model has its training parameters publicly available. Anyone can download it, run it, or fine-tune it. Why it matters: This promotes transparency, experimentation, and decentralized innovation. Models like Meta’s LLaMA or Mistral fall into this category. How it shows up in Gloo: Gloo offers options of open weight models for certain classification, enrichment, or lightweight tasks where transparency or on device execution is helpful. Regardless of model source, Gloo wraps each model in the same alignment and rights protections.Closed Model
What it means: Closed models are proprietary, with their weights, training data, and internal design kept private. Why it matters: While often highly capable, closed models like OpenAI’s GPT-4 can’t be audited or modified by the public. You only access them via API. How it shows up in Gloo: Depending on the need, some Gloo experiences rely on closed, high performing models such as GPT class or Claude class systems. These models connect through APIs and are always combined with content grounding, organizational alignment, and controlled system prompts.Multilingual Model
What it means: These models are trained to understand and generate content in multiple languages. Why it matters: Multilingual support makes models globally useful and more inclusive. However, performance can vary across languages. How it shows up in Gloo: With Gloo you benefit from multilingual model capabilities, allowing you to interpret and enrich documents in multiple languages. Chat for Teams can also answer questions in a variety of languages as long as the organization’s content is available for grounding.Instruction-Tuned Model
What it means: This is a model fine-tuned to follow human instructions, usually using examples of prompt-response pairs. Why it matters: Instruction-tuning helps models understand tasks framed as natural language instructions—making them easier to interact with. How it shows up in Gloo: Instruction tuned models form the backbone of Gloo’s conversational experiences. Gloo builds additional instruction layers on top to ensure that responses reflect organizational voice, safety requirements, and theological boundaries while still delivering helpful, actionable outputs.Next Up: What Kind of Hardware Powers AI, and Why Does It Matter? In the next section, we’ll answer: “What hardware does AI depend on, and how do specialized chips and infrastructure affect cost and performance?”