Model Provider

A model provider is a company or service that hosts large language models and exposes them through APIs for application developers to use. Instead of running massive models on your own hardware, you send requests to the provider's infrastructure and receive generated responses in return.

How It Works

Model providers manage the entire inference stack: the trained model weights, the GPU clusters needed to run them, the API endpoints, authentication, rate limiting, and billing. As a developer, you integrate with a provider by obtaining an API key, choosing a model, and sending HTTP requests with your prompts. The provider runs inference on their hardware and returns the generated text, typically charging per token processed.

Major model providers include OpenAI (GPT series), Anthropic (Claude series), Google (Gemini series), and a growing ecosystem of providers hosting open-source models like Llama and Mistral.

Why It Matters

The choice of model provider affects nearly every aspect of your AI deployment: response quality, latency, cost, data privacy, uptime reliability, and available model capabilities. Different providers offer different models with varying strengths -- some excel at code generation, others at reasoning or multilingual tasks. Provider APIs also differ in features like streaming, function calling, and vision capabilities.

In Practice

When setting up an AI assistant for deployment, selecting a model provider is one of the first configuration steps. You supply your provider credentials (typically an API key) during onboarding, and the platform routes all agent interactions through that provider's API. Many deployment platforms support multiple providers, allowing you to switch models without changing your application code. Evaluating providers based on your specific use case -- considering factors like model quality, pricing tiers, rate limits, and geographic data residency -- is an important early decision in any AI project.