I'm building a small voice-based AI interview application with a FastAPI backend and plan to deploy it on Render's free tier. I'm considering self-hosted tools such as Whisper or PocketSphinx for speech-to-text and Piper for text-to-speech, but I'm worried that running inference alongside the API could consume too much CPU and RAM and make real-time interviews sluggish. Has anyone tested this setup, and would it be better to use hosted STT/TTS services instead?
2 Answers
I wouldn’t run Whisper and a TTS model on the same free web instance as FastAPI for a real-time app. With roughly 512 MB of memory and very limited CPU, inference will compete with the API and likely add noticeable latency. The service may also sleep after inactivity, which makes the first request slower. For a demo you can try lightweight models, but I’d prototype with hosted STT/TTS first and benchmark self-hosting separately or move inference to a dedicated worker later.
For TTS, a service such as Edge TTS is mostly an outbound request: the server sends text to the provider and receives audio, so it uses far fewer local resources than running Piper or another model directly. The main costs are request latency, network reliability, provider limits, and whether the service’s terms fit your project. Hosted options from major cloud providers can also have generous trial quotas, while some specialist services have smaller free tiers.
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