Stop Paying Too Much for AI: Use Multiple LLM Providers Like a Pro

vaasav kumar — Sun, 12 Apr 2026 09:32:35 +0000

If you're getting started with AI, one of the biggest challenges is cost and flexibility.

Most tutorials lock you into a single provider. But in real-world projects, you should be able to:

Switch models easily 🔄
Compare outputs ⚖️
Use free providers when possible 💸

In this blog, I’ll show a simple way to use multiple AI providers in one setup.

💡 Learn AI Without Spending Money

One of the biggest blockers for developers getting into AI is cost.

Not everyone can afford paid APIs — and that’s completely fine.

That’s why tools like:

🧠 Cerebras (free-tier models)
🔓 Open/free LLM providers
🧪 Community-hosted APIs

are game changers.

pip install openai

Generate API Keys

OpenAI → https://platform.openai.com/
Cerebras → https://cloud.cerebras.ai/
ArliAI → https://www.arliai.com/account

Beginners can use Jupyter Notebook to try the following code while others can follow in their own code editors.

🔑 Set API Keys

import os

os.environ['ARLIAI_API_KEY'] = "xxx"
os.environ['CEREBRAS_API_KEY'] = "yyy"
os.environ['OPENAI_API_KEY'] = "zzz"

⚙️ Configure Multiple LLM Providers

# Set LLM providers to change models easily
llm_providers = {
    "arliai": {
        "api_key": os.getenv("ARLIAI_API_KEY"),
        "base_url": "https://api.arliai.com/v1",
        "model": "Llama-3.3-70B-ArliAI-RPMax-v3",
    },
    "cerebras": {
        "api_key": os.getenv("CEREBRAS_API_KEY"),
        "base_url": "https://api.cerebras.ai/v1",
        "model": "llama3.1-8b",
    },
    "openai": {
        "api_key": os.getenv("OPENAI_API_KEY"),
        "base_url": "https://api.openai.com/v1",
        "model": "gpt-4o-mini",
    }
}

🧠 Helper Function to Switch Providers

from openai import OpenAI

def set_llm(provider_name):
    provider = llm_providers[provider_name]
    client = OpenAI(
        api_key=provider["api_key"],
        base_url=provider["base_url"]
    )
    return client

🧪 Test with Cerebras (Free Model)

llm = set_llm('cerebras')

name = llm.invoke("I want to open a restaurant for Indian food. Suggest a fancy name for this.")
print(name.content)

✅ Sample Output (Cerebras)

Here are some fancy name suggestions for an Indian restaurant:

1. **Maharaja's Mosaic**: This name evokes the grandeur and richness of Indian royalty, while also hinting at the diverse flavors and cuisines that Indian food has to offer.
2. **Tandoori Nights**: This name captures the essence of Indian food, with the tandoor oven being a staple of Indian cuisine. It also has a romantic and exotic connotation.
3. **Kashmiri Kitchen**: This name suggests a high-end, sophisticated dining experience, with a focus on the rich culinary traditions of the Kashmir region.
4. **Dhaba Royale**: A dhaba is a type of Indian restaurant, but by adding "Royale" to the name, you're implying a luxurious and upscale dining experience.
5. **Saffron & Spice**: Saffron is a highly prized spice in Indian cuisine, and by combining it with "Spice", you're highlighting the complex and aromatic flavors that Indian food is known for.
6. **Mumbai Magic**: This name captures the energy and vibrancy of India's largest city, while also hinting at the magic and wonder of Indian cuisine.
7. **Garam Gourmet**: "Garam" means "hot" in Hindi, and "Gourmet" implies a high-end dining experience. This name suggests a restaurant that serves spicy, flavorful food in a sophisticated setting.
8. **Rajasthani Rhapsody**: This name suggests a restaurant that serves the rich and regal cuisine of Rajasthan, with a focus on the region's unique flavors and traditions.
9. **Spice Route Bazaar**: This name captures the essence of Indian cuisine, with its complex blend of spices and flavors. The word "Bazaar" also implies a lively and vibrant atmosphere.
10. **Ayurvedic Eats**: This name suggests a restaurant that serves food that is not only delicious but also healthy and nourishing, with a focus on the principles of Ayurvedic medicine.

I hope these suggestions help inspire you to find the perfect name for your Indian restaurant!

🔁 Test the Same with OpenAI

llm = set_llm('openai')

name = llm.invoke("I want to open a restaurant for Indian food. Suggest a fancy name for this.")
print(name.content)

✅ Sample Output (OpenAI)

Here are some fancy name suggestions for your Indian restaurant:

1. **Saffron & Spice**
2. **Curry Couture**
3. **The Royal Masala**
4. **Tandoori Treasures**
5. **Noble Naan**
6. **Mystic Flavors**
7. **Bollywood Bites**
8. **Spice Symphony**
9. **Heritage Haveli**
10. **Kashmiri Delight**

Feel free to mix and match or modify any of these names to better suit your vision!

🚀 Why This Approach Matters

This is not just a simple example — it’s a real-world pattern.

With this setup, you can:

🔄 Switch between providers instantly
💸 Use free models like Cerebras
⚡ Compare outputs across models
🧪 Experiment without vendor lock-in

🧑‍💻 For Developers

Don’t just read about AI — build with it.

Start small:

Try different prompts
Compare model outputs

With the right setup, any developer can build AI-powered applications using both free and paid models.

So:

Try multiple providers
Experiment freely
Build something useful

In our next blog, we’ll build helper functions using LangChain to make this setup even more powerful.

DEV Community: vaasav kumar