How-to guide
How to Inject Ads Into a Streaming Response in FastAPI
This guide shows how to inject ads into a streaming response in a FastAPI app, the goal being to wrap the LLM token stream so contextual ads appear inside the assistant's reply. It builds on the Monetzly gRPC service (no Python SDK exists for this stack), so the approach is specific to how FastAPI produces and streams responses.
Overview
This is the core of monetizing a FastAPI app: instead of returning the raw model stream, you pass it through Monetzly, which injects contextual, labelled ads into the token stream and hands you back the enhanced stream to forward to the client.
You keep your existing FastAPI model call untouched. Injection is additive — a wrapper around the stream you already produce, keyed on the session and the live prompt so the ad matches what the user is asking about.
FastAPI apps are usually built for LLM API backends, SSE/streaming chat endpoints, and agent microservices, so inject ads into a streaming response typically comes up while a user is mid-conversation, the moment where monetization has to be additive rather than disruptive.
How this works on FastAPI
There is no official Monetzly Python SDK — the package is Node-only. The supported path for a Python stack is the gRPC service the package ships as tps_alter.proto: generate Python stubs and open a ProcessStream bidi call (StartRequest, then a TokenRequest per LLM token, then StopRequest), reading back the ad-injected TokenResponses. Alternatively, run a tiny Node sidecar that uses the SDK and stream tokens to it. Either way, confirm the auth handshake for non-JS clients before publishing.
Since FastAPI is a Python stack with no official SDK, this task runs through the shipped tps_alter.proto gRPC service (or a Node sidecar); the auth handshake for a direct client is still TODO_VERIFY.
Steps
- Produce your FastAPI response as a token stream as you already do.
- Pass that stream into the Monetzly injection call with the session id and prompt.
- Forward the enhanced tokens to the client (SSE, WebSocket, or your existing transport).
Integration snippet
# 1) Generate stubs from the proto shipped in the SDK package:
# python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. tps_alter.proto
import grpc
import tps_alter_pb2 as pb
import tps_alter_pb2_grpc as rpc
async def inject(llm_tokens, prompt, session_id, api_key):
# TODO_VERIFY: how is the API key passed for a direct gRPC client?
# (metadata key name / channel credentials). Confirm with Monetzly.
creds = grpc.ssl_channel_credentials()
async with grpc.aio.secure_channel("your-server.com:443", creds) as ch:
stub = rpc.TPSAlterServiceStub(ch)
async def requests():
yield pb.StreamRequest(start=pb.StartRequest(
prompt=prompt, session_id=session_id, metadata={"api_key": api_key}))
async for tok in llm_tokens:
yield pb.StreamRequest(token=pb.TokenRequest(token=tok))
yield pb.StreamRequest(stop=pb.StopRequest(session_id=session_id))
async for resp in stub.ProcessStream(requests()):
if resp.HasField("token"):
yield resp.token.token # ad-injected token
# In a FastAPI route, feed your LLM's token generator into inject(...) and
# return the results with StreamingResponse.Frequently asked questions
Start monetizing in about 5 minutes
Wrap your existing LLM response stream with the Monetzly SDK and earn on every session, no paywall required.