AI Model Routing Tool Toto Helps Cut Expensive AI Costs

AI Model Routing Tool Toto Helps Cut Expensive AI Costs

Quick Reads

• Toto is a free AI model routing tool that works with OpenAI, Anthropic, and Google.
• It routes tasks based on speed, cost, or capability, users choose the priority.
• The founder previously worked at ElevenLabs and saw the routing problem firsthand.
• Toto continuously tracks each model’s changing strengths to improve routing decisions.
• It targets developer teams wasting money by defaulting to expensive flagship models.

A new AI model routing tool is making a compelling case for smarter spending. Toto, launched in 2026, automatically routes tasks to the best available AI model. The tool works with vendors businesses already pay for, including OpenAI, Anthropic, and Google.

Most teams today simply pick the most expensive flagship model for everything. However, that habit silently drains token budgets at scale. Toto argues that not every task needs a Ferrari, sometimes a Prius gets the job done just fine.

The AI model routing tool lets users optimize for three key values: speed, cost, or raw capability. Consequently, a simple summarization task goes to a faster, cheaper model. Meanwhile, a complex reasoning task goes to a more capable one. Toto handles that decision automatically, without the user lifting a finger.

The product also solves a fast-moving problem. AI models update so quickly that even seasoned engineers struggle to keep up with each model’s strengths and weaknesses. Alex Funk, the founder and a 10-year AI veteran, previously worked at ElevenLabs, where he watched top engineers guess at model recommendations for customers.

Toto therefore builds a live picture of each model’s “jagged” capabilities. It then matches those capabilities to the context of each incoming task. The result is smarter routing that adapts as the model landscape changes.

The tool currently sits in the AI Infrastructure and Unified API categories on Product Hunt, where it launched today. It is free to use and targets developer teams running high volumes of AI workloads.