21 July 2026·6 min read
tutorial
streaming
Adding ads to a streaming LLM response
A walkthrough of wrapping an existing streaming chat endpoint with the Monetzly server SDK, using the same pattern this site's own demo runs on.
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What we learn building ad infrastructure for LLM apps: the per-conversation economics, the format constraints that keep sponsored content from wrecking an assistant, and the engineering details of doing it inside a token stream.
A walkthrough of wrapping an existing streaming chat endpoint with the Monetzly server SDK, using the same pattern this site's own demo runs on.
Read the post →Most turns in an AI conversation should carry no ad. Why a deliberately low fill rate produces more revenue over time than maximizing it, and how to read the metric.
Read the post →Disclosure, category control and relevance thresholds — the rules that keep sponsored content inside an AI assistant from eroding the thing that makes the assistant useful.
Read the post →A per-session model for AI apps — what a conversation costs in inference, what it can earn from a sponsored placement, and which levers actually move the result.
Read the post →What an in-conversation ad actually is, where it sits in the response stream, and the design constraints that separate a native placement from a banner in a chat window.
Read the post →Most AI apps copy the SaaS playbook and charge $10-20 a month. The conversion math and the per-conversation cost curve explain why that leaves the majority of usage unmonetized.
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