Overview
Unify AI is an infrastructure platform that routes LLM requests to the optimal model and provider for each prompt. It compares quality, speed, and cost across 100+ models and endpoints, letting developers use a single API while automatically getting the best results.
Our take
Unify routes requests to whichever model best fits each prompt, with free credits and usage pricing. Routing is a genuinely good idea — most applications send easy requests to expensive models — and the saving can be substantial. What you accept is less predictability about which model answered, which matters when behaviour has been tuned to one.
Worth it if
Applications with a wide mix of request difficulty, where routing easy ones to cheaper models saves real money.
Look elsewhere if
Workflows tuned tightly to one model's behaviour, where substitution changes output quality.
✨ Features
Smart Routing
Automatically selects the best model for each prompt based on quality, speed, and cost.
100+ Models
Access GPT, Claude, Gemini, Llama, Mistral, and more through one API.
Benchmark Dashboard
Compare model performance across providers with real-time benchmarks.
Single API
One API endpoint replaces multiple provider integrations.
⚖️ Pros & Cons
Pros
Cons
💰 Pricing
Free
$0
- Free starter credits
- Access to all models
- Smart routing
- Usage dashboard
Pay-as-you-go
Usage
- Pay per token used
- No minimum commitment
- All routing features
- Priority support available
Categories
❓ Frequently Asked Questions
How much does routing actually save?
It depends entirely on your request mix. Applications sending everything to a frontier model when most requests are simple classification or extraction can see large reductions. Applications where nearly every request is genuinely hard see little. Instrument request difficulty before assuming the saving applies to you.
What are the risks of automatic model selection?
Output variability, principally. A prompt tuned against one model can behave differently on another — formatting, verbosity, refusal behaviour all shift. Keep an evaluation set you can re-run, and check whether the router lets you pin models for the paths where consistency matters more than cost.