Can I add Monetzly to an existing LlamaIndex.TS app without rewriting it?

Yes. You keep your LlamaIndex.TS model calls as they are and pass the streaming response into Monetzly's SDK, so ad injection is additive rather than a rewrite.

Will ads hurt the LlamaIndex.TS chat experience?

Ads are contextual and clearly labelled, placed inside the response stream rather than as competing banners, so the conversation stays the focus.

Do free users of my LlamaIndex.TS app still earn revenue?

That is the point of the model: every session can earn through in-conversation ads, so you are not forced to paywall casual users to make the app viable.

How long does the LlamaIndex.TS integration take?

Because you are wrapping an existing stream with a documented SDK call, most teams wire it in during a single session. The SDK handles connection, injection, and fallback.

Monetize by framework

How to Monetize a LlamaIndex.TS AI App

LlamaIndex.TS is a data framework connecting LLMs to private data via indexing, retrieval, and query/chat engines (TypeScript edition). Teams reach for it to build RAG over private docs, knowledge-base search, and structured data QA. Those are exactly the always-on, conversational experiences where a subscription wall or a banner ad tends to feel out of place, which is where a contextual, in-conversation ad model fits better.

What building with LlamaIndex.TS usually looks like

A typical LlamaIndex.TS project centers on RAG over private docs, knowledge-base search, and structured data QA. The interface is a conversation, and value is delivered turn by turn rather than behind a checkout.

That shape is great for users and hard for revenue: the moment you gate it behind a paywall, casual usage, the majority of traffic for most LlamaIndex.TS apps, drops off before it ever converts.

Why subscriptions and display ads are awkward for this stack

Subscriptions force a paying decision before the user has felt the value, and they leave every non-paying session earning nothing. Display networks (banners, sidebars) were built for static pages, not for a streaming LlamaIndex.TS response, they compete with the conversation for attention instead of belonging to it.

Monetzly takes the other path: relevant, clearly-marked ads are placed inside the assistant's response stream, so free users can stay free while the app still earns on every session.

How Monetzly integrates with LlamaIndex.TS

Use the TypeScript edition (LlamaIndex.TS) so you stay in the Node runtime the SDK requires. A streaming chat/query engine yields response chunks; map each chunk's text to { content } and pass into sdk.inject(). Confirm the chunk field name against your LlamaIndex.TS version.

Monetzly's server SDK consumes any async stream of tokens, so you keep your existing LlamaIndex.TS model call and pass its streaming output into sdk.inject(), contextual ads are injected into the token stream keyed on the session and live prompt, with no UI rework.

Integration snippet

TODO_VERIFY: The Monetzly injection is verified; confirm the LlamaIndex.TS-specific stream detail flagged in the snippet against your version before publishing.

import { MonetzlySDK } from "@monetzly/server-sdk";
// import a streaming chat engine from "llamaindex"

const sdk = new MonetzlySDK({
  apiKey: process.env.MONETZLY_API_KEY!,
  serverAddress: process.env.MONETZLY_SERVER_ADDRESS!,
});
await sdk.connect();

const stream = await chatEngine.chat({ message, stream: true });

async function* asChunks() {
  // TODO_VERIFY: confirm the text field (e.g. .response / .delta) for your version.
  for await (const chunk of stream) yield { content: chunk.response ?? chunk.delta ?? "" };
}

for await (const t of sdk.inject(asChunks(), { prompt: message })) send(t.content ?? "");
await sdk.disconnect();