NOAN vs Lindy

Lindy's promise is speed: assemble an assistant from templates and triggers this afternoon, no engineers required. That promise is real, and for personal-assistant workloads, email triage, scheduling, meeting notes, it is probably the right buy. The costs of the model arrive later and compound. Your agents live in a runtime you cannot read, their knowledge is whatever you uploaded per agent rather than a shared governed brain, and every action meters against a credit pool that resets monthly.

The ownership model matters most at exactly the moment agents become core infrastructure. When an agent misbehaves, owners read the code and fix it; renters file a ticket. When a better model ships, owners change one config line; renters wait for the platform. When volume grows tenfold, owners pay their model provider at cost; renters buy a bigger credit tier.

NOAN's position is that the agent should be the cheapest, most replaceable part of the stack, plain scripts any coding agent can write in an afternoon, and the brain should be the durable asset: verified facts, contacts, and a task board that any agent, on any model, reads over one API. Our own fleet of eighteen runs exactly this way, and the pattern is documented for anyone to copy.

Lindy's Pro tier is $99.99 per user per month with 15,000 credits, and everyday tasks run 2 to 250 credits each. Take a modest working agent doing 100 tasks a day at an average of 20 credits: that is 2,000 credits a day, roughly 60,000 a month, four times the Pro allowance for a single busy agent, before teammates or complex builds at 1,000 to 2,500 credits each.

The same workload on NOAN costs the plan price and your model tokens. NOAN meters nothing per task, and a grounded agent action with prompt caching typically costs a cent or two in tokens paid directly to your model provider. At hobby volume the difference is noise. At fleet volume it is the difference between a flat platform fee and a bill that scales with every action your company automates.

Plenty of teams rightly start on Lindy: it proves what agents can do without an engineering bet. Graduation day is when a flow becomes load-bearing, when you need it grounded in company truth, auditable on a board, and immune to per-task pricing. The path is not a migration tool, it is a rebuild that takes hours, not weeks: write the facts the agent needs into NOAN, hand our Agent Planner the flow you want, and let a coding agent build it against the API. The Lindy version keeps running until the owned version has earned trust.