Can I add Monetzly to an existing Streamlit app without rewriting it?

Mostly. Your Streamlit code stays the same; you add a gRPC client (generated from the shipped tps_alter.proto), or a small Node sidecar, that pipes your token stream through Monetzly and back.

Will ads hurt the Streamlit 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 Streamlit 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 Streamlit integration take?

Plan for a bit more than the JS path: you generate gRPC stubs from the proto and wire the bidirectional stream. TODO_VERIFY the auth handshake before you rely on a timeline.

Monetize by framework

How to Monetize a Streamlit AI App

Streamlit is a python framework for building data/AI web apps quickly with pure Python scripts. Teams reach for it to build AI demos, internal LLM tools, and chat prototypes with st.chat_message. 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 Streamlit usually looks like

A typical Streamlit project centers on AI demos, internal LLM tools, and chat prototypes with st.chat_message. 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 Streamlit 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 Streamlit 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 Streamlit

Streamlit is Python, so there is no native SDK. Drive the shipped tps_alter.proto gRPC service from Python (or a Node sidecar) and render the injected tokens with st.write_stream. Confirm the non-JS auth handshake before publishing.

Streamlit runs on Python, and there is no official Monetzly Python SDK. Integration goes through the gRPC service that ships with the package (tps_alter.proto), or a small Node sidecar that uses the SDK. The auth handshake for a direct non-JS client still needs confirming before you publish.

Integration snippet

TODO_VERIFY: Streamlit has no official Python SDK. This uses the shipped tps_alter.proto gRPC service, confirm the auth handshake (API key placement) for a direct gRPC client with Monetzly before publishing.

# No Python SDK. Generate stubs from tps_alter.proto and drive ProcessStream
# (see the FastAPI page for the full bidi loop), then:
#
#   import streamlit as st
#   st.write_stream(inject(llm_tokens, prompt, session_id, api_key))
#
# TODO_VERIFY: auth handshake (API key placement) for a direct gRPC client.