Frequently Asked Questions

An LLM (Large Language Model) API is a web service that allows developers to send text prompts and receive AI-generated responses programmatically. Instead of downloading and running massive neural networks locally (which requires expensive GPU infrastructure), you simply make HTTP requests to a cloud service.

Think of it like using Google Maps API instead of building your own GPS system. The heavy computational work happens on the provider's servers, and you just integrate the results into your application.

Yes, they are genuinely free — but it's important to understand how they're free. There are four main categories:

Basic programming knowledge is recommended, but the barrier to entry is lower than you might think. Most LLM APIs follow RESTful conventions and can be accessed with simple HTTP requests.

💡 Pro Tip: Start with platforms that have interactive playgrounds (like Google AI Studio or Groq Playground) to test prompts before writing any code. Many providers also offer copy-paste code snippets in multiple languages.

ChatGPT is a user interface built on top of OpenAI's GPT models, while an LLM API is the underlying engine that powers such interfaces. Think of it like this:

• ChatGPT: A pre-built chat application you access via browser or mobile app. You type messages, it responds. No coding required.
• LLM API: Raw programmatic access to the AI model. You send prompts via code and integrate the responses into your own applications, websites, or workflows.

With an API, you can build your own ChatGPT-like interface, automate tasks, process data at scale, or embed AI into existing software. The API gives you control, customization, and the ability to productize AI features.

The context window is the maximum amount of text (measured in tokens) that an LLM can process in a single request. This includes both your input prompt and the model's output.

Why it matters: If you're building a chatbot that needs to remember 20 messages of conversation history, or if you want to summarize a 50-page PDF, you need a model with a large enough context window to fit all that data.

Here's a minimal example using Groq (one of the fastest free APIs) with Python:

import requests

url = "https://api.groq.com/openai/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}
data = {
    "model": "llama-3.3-70b-versatile",
    "messages": [{"role": "user", "content": "Explain LLMs in one sentence"}],
    "temperature": 0.7
}

response = requests.post(url, headers=headers, json=data)
print(response.json()["choices"][0]["message"]["content"])

Pro Tip: Most modern LLM APIs follow the OpenAI format, so code written for one provider often works with others by just changing the endpoint URL and API key.

Tokens are the atomic units that LLMs process. They're not exactly words — they're pieces of words, determined by how the model was trained.

Common words like "the" or "and" are usually 1 token each. Longer or less common words might be split into multiple tokens (e.g., "unhappiness" could be 2-3 tokens).

💡 Use a tokenizer: Tools like OpenAI's Tokenizer let you paste text and see exactly how it gets split into tokens.

Absolutely! Running models locally is one of the best ways to get unlimited, private, and truly free AI access. Here are the most popular tools:

Advantages: Complete privacy (no data leaves your machine), no rate limits, works offline, unlimited usage.

Disadvantages: Slower than cloud APIs (unless you have high-end hardware), requires storage space (1-50GB per model), limited to open-source models.

Rate limits control how many API requests you can make within a specific time window. They prevent abuse and ensure fair access for all users.

Based on current offerings as of February 2026, here are the top contenders:

💡 Pro Strategy: Use multiple providers and rotate between them. Set up a fallback system in your code to switch APIs if you hit rate limits.

It depends on the provider. Here's a breakdown of common policies:

Here are practical strategies to maximize your free tier usage:

// Before: 500 tokens/request × 100 requests = 50,000 tokens
// After:  200 tokens/request × 50 requests = 10,000 tokens (5x reduction!)

It depends on both the provider's terms and the model's license. There are two separate legal considerations:

💡 Safe Bet: Use models with Apache 2.0 or MIT licenses (like Llama 3, Qwen, Mistral) via providers that explicitly allow commercial use (Groq, Together AI, Replicate).

Not always. Privacy policies vary dramatically between providers. Here's what you need to know:

A 429 error means you've exceeded the provider's rate limit. Here's how to handle it:

import time
import requests

def call_api_with_retry(url, headers, data, max_retries=3):
    for attempt in range(max_retries):
        response = requests.post(url, headers=headers, json=data)
        
        if response.status_code == 200:
            return response.json()
        elif response.status_code == 429:
            retry_after = int(response.headers.get('Retry-After', 60))
            print(f"Rate limited. Waiting {retry_after} seconds...")
            time.sleep(retry_after)
        else:
            response.raise_for_status()
    
    raise Exception("Max retries exceeded")

Slow responses can have multiple causes. Here's a troubleshooting checklist:

data = {
    "model": "llama-3.3-70b-versatile",
    "messages": [...],
    "stream": true  // Get chunks as they're generated
}

response = requests.post(url, json=data, timeout=120)  // 120 seconds