Calculators and money-honest breakdowns for founders who run the numbers.
4 posts
Three pricing ladders beat flat per-seat pricing for AI products in our teardown of 40 launches: a credit ladder, a usage-with-floor ladder, and an outcome ladder. Each cleared a 7%+ trial-to-paid rate by aligning the price metric with the value metric and putting a visible cap on downside. Pick the ladder that matches how your users feel cost — tokens, runs, or results — and price the rung, not the seat.
The sticker price per token is the smallest line in your LLM bill. Once you add retries, embeddings, vector reads, orchestration, and the platform margin, a "simple" agent workflow lands around $0.42 per run — roughly 4× the raw model cost. This breakdown shows where the money actually goes and which three levers cut a workflow bill the fastest without touching quality.
Build-vs-buy isn't a values debate, it's a breakeven date. SaaS wins early because it converts a big upfront build into a small monthly fee. DIY wins once your usage-based SaaS bill exceeds the fully-loaded cost of owning the code — typically around month 18 for infrastructure-style tools. This piece gives you the formula, a worked example, and the three traps that make teams build too early.
Your headline rate is a vanity number. Your effective hourly rate — total income divided by every hour the business consumed, billable or not — is what you actually earn. For most freelancers it lands 35–55% below the rate on their invoice once you count admin, sales, and unbilled rework. This piece shows how to compute it, why it's always lower than you think, and the three moves that raise it without raising your rate.
