“How much will this cost?”
It sounds like a simple question. In reality, it is one of the hardest ones to answer when you are building anything with AI agents.
We run into this problem repeatedly while scoping AI features and agent-based workflows. Before a single line was built, there were already too many unknowns. Model pricing. Token usage. Architecture decisions. Infrastructure choices. Everything affected cost, and everything depended on assumptions that were hard to validate upfront.
That frustration is what led to Price My Agent, another small experiment built during a QApilot hackathon.
The problem we were trying to solve
Most AI agent conversations start with capability.
What should the agent do?
How smart should it be?
What tools should it use?
Cost usually comes later, often when it is too late to change direction.
We noticed a few recurring patterns:
Teams underestimate costs early and get surprised later
Founders struggle to justify feasibility without rough numbers
PMs and engineers talk past each other when scoping AI work
Cost discussions are abstract until something is already built
There was no easy way to sanity-check an idea before committing to an approach.
The core idea behind Price My Agent
We were not trying to build a perfect pricing calculator.
The goal was simpler:
Take an AI agent idea written in plain English
Generate a few realistic implementation approaches
Attach rough cost estimates to each approach
Make the trade-offs visible early
Instead of asking “What is the exact cost?”, Price My Agent helps answer:
“What are my options, and how expensive could each path get?”
How we built it during the hackathon
Like the other Labs projects, this was entirely vibe coded.
No manually written backend code.
No elaborate pricing engines.
Just rapid iteration and learning.
The flow looks like this:
You describe the agent you want to build
The system interprets the scope and complexity
It generates three implementation plans, typically low, medium, and high cost
Each plan includes assumptions around models, usage, and architecture
The output is not meant to be precise accounting. It is meant to be directional and practical. We optimised for clarity over completeness.
What makes Price My Agent useful
Price My Agent works best early in the process.
It helps when:
You are evaluating whether an idea is feasible
You want to compare a lightweight MVP against a more robust setup
You need to communicate trade-offs to stakeholders
You want to avoid designing yourself into an expensive corner
Instead of debating abstract costs, you get something concrete to react to. That alone changes the conversation.
Launching it and learning from feedback
We launched Price My Agent publicly to test a simple hypothesis. Would people find value in rough cost estimates, even if they are not exact?
The response was encouraging and honest.
Some people found it useful as a sanity check. Others wanted more control over assumptions like scale or traffic. Several pointed out areas where costs tend to blow up in real systems.
That feedback confirmed two things:
Cost estimation is a real pain point
Even imperfect tools can be valuable if they help people reason earlier
You can try Price My Agent here:
👉 https://www.pricemyagent.com/
And here is the Product Hunt launch where the discussion is happening:
👉 https://www.producthunt.com/products/price-my-agent?launch=price-my-agent
