When we designed Ottly, one of the earliest architectural decisions was: never lock into a single AI provider.

This was not a marketing decision. It was a technical one based on how AI models actually perform in production.

Different Models, Different Strengths

No single model is best at everything. In our testing:

GPT-4o excels at structured output, function calling, and code generation
Gemini 2.5 handles long-context tasks and multi-modal inputs exceptionally well
Claude is strongest at nuanced writing, analysis, and following complex instructions

If you lock into one provider, you are accepting mediocre performance on tasks where another model would be significantly better.

The Cost Factor

AI model pricing changes constantly. In the last six months alone, we have seen:

OpenAI dropping GPT-4o prices by 50%
Google offering aggressive Gemini pricing for high-volume users
Anthropic introducing new tiers with different cost structures

A multi-model architecture lets you route requests to the most cost-effective model for each task. Over time, this adds up significantly.

How Ottly Handles It

Every Ottly product uses our shared LLM abstraction layer:

Model selection — Users choose their preferred model, or let the system pick the best one for the task
Automatic fallback — If a provider has an outage, requests route to an alternative
Cost tracking — See exactly how much each model costs per task

The Vendor Lock-In Trap

We have seen teams build entire products on a single AI provider, only to discover months later that a competitor's model is better for their use case — but migration would require rewriting their entire prompt chain.

Ottly avoids this by abstracting the model layer from the application layer. Switching models is a dropdown change, not a codebase rewrite.

Our Recommendation

If you are building with AI, support at least two providers from day one. The upfront cost of abstraction is small compared to the flexibility it gives you as the AI landscape evolves.