For an interactive browser voice agent, stream audio rather than waiting for complete recordings, transcripts, model replies, and audio files. Use WebRTC for browser media, let FastAPI handle application and session setup, run speech and model work as streaming stages, and use PostgreSQL for durable state—not for each audio frame. Modal can host the application and inference services, but cold starts, WebSocket concurrency, and database connection pools all need to be designed and tested for your workload.
What the pipeline should do
A voice conversation is a sequence of stages, not one request that starts after the user finishes speaking. The aim is to move useful data forward as soon as it is available, then play the first response audio without waiting for the entire answer to be generated.
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- Capture: The browser captures microphone audio after the user grants permission.
- Transport: Send interactive browser audio over WebRTC.
- Speech processing: Transcribe or otherwise process incoming audio incrementally.
- Response generation: Pass the relevant transcript and context to the response model, and stream its output where supported.
- Speech output: Synthesize response audio incrementally and return it for playback.
- Persistence: Store required session, account, action, or summary data in PostgreSQL separately from the media stream.
Modal’s published Pipecat example describes a flow that receives audio over WebRTC, transcribes it, queries documentation, assembles context, generates and parses a response, synthesizes speech, and returns audio and related data over WebRTC. It uses separate inference deployments and WebSockets over Modal Tunnels for communication with speech services. That is one documented design, not a requirement for every deployment.
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WebRTC and WebSockets solve related but different problems. For a browser-facing audio-first experience, WebRTC is the natural media transport. WebSockets are useful for persistent bidirectional connections between services or for application events; they are not automatically a substitute for a media transport optimized for interactive audio.
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| Transport | Good fit | Documented considerations |
|---|---|---|
| WebRTC | Interactive browser microphone input and generated audio on negotiated media tracks. | OpenAI’s browser guide describes media tracks and a data channel for events; the browser requires microphone permission and HTTPS or localhost. Modal’s FastRTC example also demonstrates WebRTC with an ASGI application. |
| WebSocket | Persistent, bidirectional service communication, such as the STT/TTS connections in Modal’s voice example. | Modal web functions support WebSockets, but a connection occupies a function call. Concurrency, connection lifecycle, and cold starts therefore matter. |
FastAPI can own authorization, request validation, routing, session setup, and application WebSocket endpoints. Keep media transport and inference stages explicit so that the application layer does not become a bottleneck or obscure where delay occurs. Keep provider credentials on trusted server infrastructure; the browser session flow described in OpenAI’s guide exchanges setup information through the application’s trusted server.
How to split the work across Modal and FastAPI
Keep the application boundary responsible for control
Use the FastAPI layer to authenticate the caller, associate a session with the right tenant or account, validate setup data, and coordinate the session. Do not treat a live socket or a particular container as the permanent record of the conversation.
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Make inference stages stream-aware
Where the speech and response services support incremental output, pass partial results onward instead of waiting for a complete transcript, full model response, and finished audio file. Modal recommends streaming to improve perceived responsiveness and defines model streaming measures such as time to first token (TTFT) and time per output token (TPOT). These are useful stage measures, but they do not replace measuring when the user first hears audio.
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Choose Functions or Servers for their actual behavior
| Modal deployment option | Documented behavior | Design implication |
|---|---|---|
| ASGI/WebSocket Function | Modal supports WebSockets on web functions, including ASGI applications. One function call is maintained per WebSocket connection; a container can handle concurrent inputs if configured, and additional containers can scale out. A function with no active container can incur a cold start. | Load-test concurrent sessions and reconnects, and account for cold starts in the user experience. |
| Modal Server | Optimized for low-latency HTTP to a process in a container; the process is expected to handle concurrency, and autoscaling is configured separately. A request that takes the service from zero active containers can receive HTTP 503. | Make retries safe at the client or gateway, or maintain suitable warm capacity if that zero-to-one rejection behavior is unacceptable. |
Modal’s FastRTC demo uses ten concurrent streams and a ten-minute time limit in its sample configuration. Those are example values for that demo, not production defaults. Set capacity based on measured CPU or GPU use, per-session memory, upstream provider limits, and tail latency. Modal also documents session affinity for voice and video workloads, subject to session-lifetime and concurrency constraints. Affinity may help keep a live session near its work, but it should not be the sole location for durable conversation state.
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How to measure latency that users actually feel
Measure from the user’s first relevant input to the first audible response, not just the time taken by one model call. Record stage timings so a slow response can be attributed to setup, network, speech processing, model generation, synthesis, or playback buffering.
- End-to-end: time to first audible response, total response completion, and time to recover after an interruption.
- Speech recognition: time to the first partial transcript and time to the final transcript.
- Model: TTFT and TPOT for streamed output.
- Speech synthesis and playback: time to first audio (TTFA) and any buffering before playback.
- Deployment behavior: cold-start delay, reconnect behavior, and latency at expected concurrent-session levels.
Modal’s engineering article describes a general conversational voice-AI target of around one second or less and says this is difficult, particularly when computation is distributed across machines. The same 2025 article describes some natural conversational voice-to-voice latencies as short as 100 milliseconds, while noting that context and interruption behavior matter. Both are vendor-published illustrative statements—not an SLA, a benchmark for this Modal/FastAPI/PostgreSQL stack, or a promise for a particular deployment. No end-to-end benchmark for this exact combination is established here.
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Compare architecture changes using representative audio, the actual region and model versions, realistic concurrency, and the same measurement method. Include both time to first audio and complete response time: improving the first may not reduce the time required to finish the answer.
How PostgreSQL fits without slowing the audio path
Use PostgreSQL for durable session metadata, user and account state, tool or action records, and summaries that need to survive container replacement. Do not make each audio frame wait on a synchronous database round-trip. Keep transactions short, and acquire a database connection only for the operation that needs it; do not hold a transaction or checked-out connection while waiting for speech processing or model generation.
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PostgreSQL uses a process-per-client connection model. Its PostgreSQL 18 documentation says max_connections is typically 100 by default, though machine capability can make the default lower. Treat that as the database’s documented typical default, not a safe pool size for every deployment.
Plan for aggregate connections across the whole service, not only one container. A useful capacity estimate is:
active containers × pool size per container + migrations, administration, and other services
This is a planning equation, not a performance benchmark. Set explicit per-container pool limits, choose pooling appropriate to the database hosting setup, and load-test the aggregate against the actual database limit. Horizontal scaling can multiply pools quickly even when each individual pool appears modest.
What to test before treating it as production-ready
- Verify browser microphone access on HTTPS or localhost, and test the intended browsers and audio devices. A USB microphone is optional for local voice testing; it does not change the backend architecture.
- Measure time to first audio and interruption recovery with representative speech, not only synthetic or unusually short prompts.
- Exercise cold starts, dropped connections, reconnects, and any safe retry path. In particular, account for Modal Server’s documented zero-to-one 503 behavior if using Servers.
- Increase simultaneous sessions until tail latency, resource limits, or provider limits become unacceptable; do not copy the FastRTC demo’s concurrency setting as a target.
- Check the total possible PostgreSQL connections at peak container scale, including non-application clients.
- Keep authorization, tenant routing, validation, and secrets management at trusted application boundaries, and verify that durable state remains available if a container is replaced.
The architecture guidance above is not a complete security or compliance design. Exact latency, concurrency, cost, provider availability, and suitable pool sizes depend on deployment choices and require measurement under the expected workload.
Quick Recap
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