A large general model answers anything, which is why it is expensive and slow. An everyday request does not need that. It needs the right action from a short list, with the details filled in correctly. Fernfly trains a small model to do that one job on your actions, and hands the result to your system to run.
Three costs that come from how the task is run, not from how hard it is.
A large general model has to be told what your app can do, every time you ask it anything.
Re-sent on every request, and charged for every time.
Registered once. Every request after that refers to it.
Agent platforms charge for the work rather than the software.
Between $0.50 and $0.99 for each resolved conversation.
A flat monthly price, whatever the volume.
"Refund order 62" has one right answer and nothing to weigh up.
Reasoning you are paying for and the task never needed.
One step: pick the action, fill in the details, hand it back.
Per-conversation figures are the published rates for Intercom Fin and HubSpot Breeze.
It is cheapest on the days nothing much happens, and most expensive on the day everything does. A flat price is the other shape.
For an everyday request that ends in one action. Open-ended work is a different question, and the large general model still wins it.
Fernfly a small model on your API | Large general model prompted per request | RAG pipeline retrieve, then prompt | |
|---|---|---|---|
| Cost per 1,000 requests | Flat, whatever the volume | Tokens in and out, on every request | Tokens plus retrieval |
| The list of actions is | Registered once, then referred to | Re-sent, and charged for, every time | Re-sent, plus whatever was retrieved |
| Typical response | Under a second | A few seconds | Retrieval, then a few seconds |
| On a repeatable request it is | Trained on your own actions | General, and prompted each time | Good at finding, not at doing |
| When it does not know | Returns nothing and hands off | Tends to produce something | Answers from the nearest document |
| Can run inside your network | Yes, on a CPU | Vendor cloud | Store yes, model usually not |
Five stages, all of them in the browser. Follow one example the whole way through, or switch it to the business you actually run.
Paste your address. Fernfly reads what you already have, works out what your business does, and proposes the actions your assistant should be able to run. You tick the ones you want.
The playground runs the same curated requests through a Fern use-case model and through GPT-4o, side by side, and reports what each one got right, whether the arguments would have been accepted by your app, how long it took and what the large model cost.
Run it yourselfOutside the actions it was trained on it returns nothing and hands off. On a repeatable task a wrong action costs more than a missing one.
Everything it could not handle is collected in one list. Approve, retrain, publish. The old version serves until you switch.
How many requests, how fast, which actions ran, how often it handed off, and where the people asking came from.
Trained on your own actions, versioned, and pinned to a deployment you control.
Free to start, no credit card, and the first one is live in an afternoon.