While working on a self-hosted speech translation platform, we assumed translation quality would be the biggest challenge.

It turned out not to be.

What surprised us most was how important conversation flow is. Even when a translation is accurate, a delay at the wrong moment can make a discussion feel awkward or unnatural.

One lesson we learned early on was that translating complete sentences often produces much better results than translating word-by-word. The extra context helps the model better understand meaning, intent, and phrasing.

That introduces an interesting trade-off.

You can reduce latency by translating smaller chunks of speech, but you often lose context and translation quality. We decided to prioritize natural conversations over chasing the lowest possible latency.

Right now, our end-to-end latency is typically under 2 seconds depending on the setup, and we’re continuing to experiment with ways to improve both speed and quality. We’re also exploring additional context-aware translation improvements and tone adaptation.

We’re curious how others approach this trade-off in real-time systems.

Would you prioritize lower latency or better context if it meant a more natural conversation experience?

For anyone interested in the project we’re building, it’s called PolyTalk: https://polytalk.io/