Filter Before You Pay for AI
Most of what a business sends to an AI model never needed it. Rules, a small local model and a cache can handle the routine inputs first, so the expensive model sees only the ones that need judgment.
Most of what a business sends to an AI model never needed it. Filter first, with rules, a small local model and a cache, and the expensive model sees only the inputs that need judgment.
In the example below that is one input in ten, and the model bill falls to a tenth. The work is in building filters you can trust.
What Sending Everything Costs
The obvious design sends every event, email and document straight to the model. Say a business processes 1,000 events a day at half a cent each (illustrative figures): that is $150 a month on model calls alone. It looks cheap until you see that most of those events were routine and needed a rule, and the bill grows with volume every month.
Cheap Checks Before the Expensive One
A well-built design treats the large model as the last step, kept for the inputs that need judgment.
Event Arrives
An email, a document or a page change comes in. Cost: zero.
Rules
Fixed rules handle the obvious cases. Cost: zero; no model involved.
Small Local Model
A lightweight model on your own server scores how complex the input is. Cost: negligible.
Large AI Model
Only the inputs that need judgment reach it. Cost: full price.
Cache
The answer is stored, so the same input next time costs nothing.
Each check removes inputs the expensive model does not need to see, so only the hard ones reach the part that costs money.
What the Filters Save
Illustrative: 1,000 events a day. The shares are an assumption that shows the shape, not a measurement.
Rules, the small model and the cache handle nine events in ten between them, and one in ten reaches the large model.
All 1,000 events go to the model. 1,000 × $0.005 = $5 a day, $150 a month, $1,800 a year. The bill grows in step with volume.
One event in ten goes to the model. 100 × $0.005 = $0.50 a day, $15 a month, $180 a year. The 900 filtered out never needed judgment.
The saving holds only if the filters are right. A rule that sets aside an event that needed judgment costs more than the model call it saved. Before you rely on the filters, run them against a sample of your own traffic and count what they miss.
Where Filtering Pays
How much of what you send to an AI model needed it?
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