Hey Product Hunt 👋 I built ModelGate after noticing something that kept bothering me: as LLMs became easier to integrate, we started using them for more and more backend tasks — including some things that probably shouldn't require an LLM at all. Classification, routing, extraction, repetitive decisions... a single API call looks cheap, but thousands or millions of unnecessary calls add up. ModelGate is my attempt to make that visible. It helps teams understand how LLMs are actually being used in production, monitor cost and token usage, identify security risks such as prompt injection, and find repetitive patterns that may eventually be better handled by deterministic code. The goal isn't simply "use a cheaper model." It's to answer a more fundamental question: **Did this task need an LLM in the first place?** I'm still building and learning, so I'd especially love feedback from people running LLM APIs in production. What are you currently doing to control LLM costs, and where do you think you're wasting the most tokens? Thanks for checking out ModelGate 🙌

ModelGate

Stop paying an LLM to do a function's job.

Stop paying an LLM to do a function's job.