Selling 'AI employees': pricing is the hard part, not building
Someone spent a couple of months trying to sell OpenClaw-built AI agents to small businesses like law firms and real estate offices, and shares raw notes on pricing. Building the agent turned out to be the easy part; pricing it has been the real struggle. Per-seat pricing, borrowed from SaaS habits, failed because clients don't care how many agents are running — they care whether their invoices go out faster.
Charging by agent count shifts the client's attention to the seller's instead of their own problem, which backfires. What worked much better was calling the product an 'AI employee' and charging a flat like a salary. It's not technically accurate, but business owners already have a mental model for what a person costs, so the product ends up competing with hiring someone instead of competing with another software — a far easier sale.
Another trap was : taking plus and adding a margin. That approach misses the point when the tool might be saving a law firm from losing half a million euros they didn't even realize was at risk.
Key points
- Per-seat/per-agent pricing backfired because it made clients focus on the seller's , not their own problem
- Framing the product as an 'AI employee' billed monthly like a salary worked much better
- This framing taps into a mental model business owners already have: the cost of hiring a person
- (token + plus margin) was identified as a trap
- Pricing based on the value delivered (e.g., losses prevented) makes more sense than cost-based pricing