Model Agnostic

Model agnostic means a service isn't dependent on just one AI model — it's designed so you can pick from several models to use.

A restaurant analogy makes this easier to grasp. Some restaurants source ingredients from only one farm. If that farm runs into trouble, the restaurant has to close too. Other restaurants work with multiple farms and switch suppliers depending on the situation. A model-agnostic service works more like the latter. If Company A's model gets slower, more expensive, or drops in answer quality, users can switch to Company B's or C's model behind the scenes.

On the other hand, a service locked into one specific model is entirely at the mercy of that model's performance and pricing decisions. That's why many AI services now advertise 'choose from multiple models' as a selling point — because users want to avoid being tied down to a single company.

The article introduces 'Skydive,' an AI teammate service, highlighting model agnosticism as one of its three key features, noting that users can choose among multiple models including Fable, GPT, and DeepSeek. A common misunderstanding is that this doesn't mean better performance. Model agnosticism means you have the freedom to choose which model to use — it's not a guarantee that it's always superior to any specific model.

You can check this by asking the AI service you're currently using:

1. "Which AI model does this service use?"
2. "Can I switch to a different model and get an answer?"

If the answer names only one model with no option to switch, it's a model-locked service. If it mentions multiple model names and says you can choose, it's a model-agnostic service.