Every single time we design a new AI feature, our product managers ask the exact same question:
"What is this going to cost us at scale?"
It sounds like a simple request. But as any developer knows, calculating true LLM pricing is an absolute engineering nightmare. API prices change weekly, output tokens cost $3\times$ to $5\times$ more than input tokens, and multi-turn chat history scales quadratically ($O(N^2)$). Trying to map out complex workload shapes on a spreadsheet was breaking my brain.
After the third time a PM asked me to run a "what-if" scenario comparing a premium frontier model against a cheaper open-weights alternative, I decided to stop messing with Excel formulas and build a dedicated solution.
Say hello to ChooseAIModel (chooseaimodel.com) - a zero-auth, zero-code cost simulator built specifically to bridge the gap between engineering reality and business budgeting.
🚀 What it does:
Simulate Real Workload Shapes: Paste a real prompt, mock file sizes (like a 2MB PDF or CSV), and set your expected monthly volume to get an instant token calculation.
Trace Multi-Turn Conversation Math: Drop in follow-up chat turns to see exactly how your context grows, complete with automatic prompt caching discounts mapped out.
Compare 290+ Models Side-by-Side: Instantly see your projected monthly bill compared across 49 different providers - from ultra-premium reasoning engines to highly efficient mid-tier models.
It’s completely free, requires no SDK, no account, and stores absolutely zero data.
If you are tired of guessing your cloud bills or manually tracking vendor rate sheets just to answer a roadmap question, try running a quick scenario on the simulator. I'd love to hear your feedback! 👇
This is a useful framing. The part I would add is that the simulator needs a runtime ledger after the feature ships.
The estimate gets PMs aligned, but the production bill usually drifts because of retries, fallback routes, longer context, and different upstream models than the one the product name suggests.
We ran into the same problem while building Tokens Forge: cheap model access is only trusted if every run can explain API key/project, requested model, upstream model, primary/backup route, balance bucket, and final token usage. Otherwise the scale estimate is right for planning but still hard to reconcile later.
Honestly, the thing I'd be most careful with isn't the cost calculation itself.
I've seen teams get much better at estimating model costs and still end up making the wrong product decisions afterward.
The reason is that once the numbers become visible, people naturally start optimizing around them. Sometimes that's the right move. Sometimes it quietly pushes attention away from the thing users actually care about.
That's why I'd be interested in what surprised you most from the PM conversations. Was it the cost questions themselves, or the decisions people started making once they had the answers?