we made quick comparison Because we kept seeing the same pattern: teams shipping LLM features were making model decisions with surprisingly little evidence.
Often, they were defaulting to the largest or most familiar model, relying on public benchmarks, or manually testing a couple to eliminate it. But in practice, this may mean spending far more than necessary on estimation without actually getting the best results for your use case.
The reality is that model choice is rarely one-dimensional. It’s not just about which model performs best. This too:
• Which model performs best on your signals and tasks?
• Where can you cut estimating costs without compromising output quality?
• When do cheaper models actually match or outperform expensive defaults?
• How do cost, speed, and performance change together?
For many teams, especially those building large-scale AI products, this has real business implications. Huge monthly estimate bills, slow experimentation, and too much guesswork in decisions that directly impacts margins, product experience, and speed to market.
That’s why we created QuickCompare.
QuickCompare helps teams compare models based on their own data in terms of quality, cost, and speed, so they can make a reliable decision based on their actual use case instead of generic benchmarks.
And we made it too ziggyTo make this much easier, our AI scientific assistants. You don’t need deep Ewells expertise to get started. Ziggy helps you set up and run comparisons in a more intuitive, no-code way.
The goal is simple: to help teams find the right model for the job, often dramatically cutting costs while maintaining or improving performance and speed.
If you’re building with LLM, we’d really love your feedback. Specifically, I would love to hear:
• How are you choosing models today
• Is estimating cost a major issue for you?
• Why does model evaluation seem slow, difficult, or inaccessible in practice?
🔗 Try QuickCompare for free: We’d love to hear what you think🙏
🎁 Product Hunt Bonus: Get an Extra $10 in Free QuickCompare Credit
Thanks so much for checking us out and supporting the launch!
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