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0What is AI1ML vs DL vs GenAI2LLMs3Transformers4Tokens5Tokenization6Embeddings7Similarity Search8Prompt Engineering9Temperature10Top P11Context Window12Hallucination13Model Providers14Open vs Closed Models
Generative AI Foundations
Phase 1Module 14 of 15

Model Providers

Training LLMs costs hundreds of millions of dollars. Model providers let you access state-of-the-art models via API without infrastructure investment. Choosing the right provider and model impacts cost, latency, quality, and capabilities.

Model providers are like cloud hosting companies for intelligence — instead of building your own data center (training models), you rent compute from AWS, GCP, or Azure based on your needs and budget.

Visual Workflows

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Key Takeaways

  • 1.Model providers are companies that host and serve LLMs via APIs — OpenAI, Anthropic, Google, Cohere, and others — each offering different models, pricing, and capabilities.
  • 2.Major providers: OpenAI (GPT-4o, GPT-4o-mini — general purpose, function calling, vision), Anthropic (Claude 3.5 — long context, safety, coding), Google (Gemini — multimodal, long context), Cohere (Command, Embed, Rerank — enterprise, RAG-focused), Mistral (open-weight models, EU hosting).
  • 3.Consider: model capability, context window, pricing (input/output per million tokens), latency, rate limits, data privacy, compliance (SOC2, HIPAA), and API compatibility.
  • 4.Use abstraction layers (LiteLLM) to switch providers easily.

Real Example

Scenario

Your app routes simple queries to GPT-4o-mini ($0.15/1M tokens), complex reasoning to GPT-4o ($2.50/1M), and embeddings to text-embedding-3-small — optimizing cost while maintaining quality.

What you would do

In Generative AI Foundations, apply Model Providers to this scenario: Your app routes simple queries to GPT-4o-mini ($0. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Practice Task

Open the Code Walkthrough below and run it locally. Change one parameter related to Model Providers (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Code Walkthrough

Highlighted lines show where Model Providers happens in the code.

Model Providers
1import litellm  # import dependencies2
3# Provider-agnostic API call4response = litellm.completion(5    model="gpt-4o-mini",  # or "claude-3-5-sonnet-20241022"6    messages=[{"role": "user", "content": "Hello"}],7)8
9# Route by task complexity10def select_model(task_type: str) -> str:  # define a reusable function11    routing = {12        "simple": "gpt-4o-mini",13        "complex": "gpt-4o",14        "coding": "claude-3-5-sonnet-20241022",15        "embedding": "text-embedding-3-small",16    }17    return routing.get(task_type, "gpt-4o-mini")  # return the result

Cheat Sheet

Quick recap

quick ref
  • •LiteLLM for abstraction
  • •gpt-4o-mini for simple tasks
  • •gpt-4o / claude for complex
  • •Cost = input + output tokens
  • •Model routing saves 50-80%
  • •Eval across providers

Common Mistakes

  • ✕Using the most expensive model for every query
  • ✕Hardcoding provider SDK calls without abstraction layer
  • ✕Not monitoring costs per model and endpoint
  • ✕Choosing provider based on hype, not eval results on your data