How did you model AI costs BEFORE shipping?
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As a solo dev, I can't afford to ship something and then realize it's unprofitable. But estimating token usage before launch is hard.
Do you just... guess? Build a prototype first? Or is there an actual way to forecast what users will actually cost you?
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I’d avoid guessing as much as possible.
What has worked for me is building a thin prototype first, then logging cost per real workflow, not just per prompt. For example: input tokens, output tokens, retries, tool calls, embeddings, background jobs, and failed runs.
The hidden cost is usually not the “happy path” request. It’s users retrying, uploading messy input, asking follow-ups, or running the same task multiple times.
I’d model three cases before launch:
normal user
heavy user
abusive/power user
Then set pricing and limits around the heavy-user case, not the average one.
@prashant_patil14 yep working at the workflow level sounds wise.
The thing that surprised me modeling this for an agent product: the LLM tokens weren't the scary line, the tool calls were. Most of my per-run cost turned out to be web search calls, not the model, so pure token math would've badly underbid it.
If your thing calls out to anything (search, scraping, embeddings, a vendor API), meter each external call separately from tokens. The mix can be the opposite of what you'd guess, and you can't optimize a cost you've lumped into one 'AI' number.
@mesut_temizkan Yes, exactly. Tool calls are where the accounting gets opaque fast.
LLM providers usually give you a baseline for token behavior and pricing, but once the agent starts calling external resources, that cost is no longer just “model input/output.”
A tool call may have its own hidden unit economics: request count, result depth, retries, latency, rate limits, quota tiers, or downstream compute.
And there isn’t really a unified disclosure standard for “tool effort.” Inside a tool or MCP server, you may not know how much work is being done, how many subcalls happen, or how attention/context is being allocated before the result comes back.
So I agree: token math is only the floor. For agent products, each external capability needs its own meter, budget, and failure mode. Otherwise you’re pricing the visible LLM call while the real margin leak is happening outside it.
I thought token pricing was the hard part.
Turns out forecasting agent behavior was much harder.
The same task can cost 10x more depending on how many iterations it goes through. One reporting script ended up costing me over $170 in API usage. That was an expensive lesson.
Honestly, It IS very hard to estimate accurately before shipping.
As a solo dev, you may already be paying $100-200/month for your own dev tools and AI subscriptions before the product earns anything.
If your product is $5/month, you need 20-40 loyal paying users just to cover that baseline, before hosting, API usage, support, and your own time.
So don’t start by “attracting users” from zero if you have limited risk tolerance or marketing capacity. Go where users already are. Build for an existing community, app ecosystem, forum, plugin marketplace, or workflow where people already have stable usage habits, clear pain, and a product-buying mindset. In that case, you’re not guessing abstract demand. You’re attaching a paid solution to an existing behavior.
You still need to prototype and measure token cost, but the bigger survival question is distribution. For solo devs and small teams, predictable access to real users often matters more than perfect cost forecasting.
id say build a rough prototype first and actually measure real token usage on real sessions... guessing never works because the way users actually use it is always different from how you imagine they will.
run like 10-20 realistic sessions, log the input and output tokens per session, then multiply by your expected usage volume.. that gives you a real cost-per-user to model against.
Add a generous buffer for the power users who'll hammer it way harder than average, they're the ones who blow up your margins.
@zack_g2 thanks, and i imagine this also helps understand any edge cases of usage
@davem_0 I am working on an AI writing tool, and I constantly think about API costs.
Ran calculations with AI atleast 3-4 times, Changed model from Haiku to Gemini Flash 2.5, usage limits, etc.
Eventually I have decided to move to an early access->trial->premium-only model from freemium.
This way I can control costs and get better feedback.
If it picks up traction, I will think about introducing a freemium tier.