Algolytics

The same ML use case can cost 20x more - depending on how it’s built.

We tested a typical production setup: AutoML models, ~150 queries per second.

Model accuracy was similar across platforms.
Cost wasn’t - in some cases over 20x higher.

Where does that difference come from?
◾ models with similar accuracy can have very different inference cost
◾ scoring can run through heavy runtimes and libraries - or as simple compiled code
◾ architecture - containerized Python workers and orchestration overhead vs JVM-based, event-driven ex*****on with Vert.x

At this scale, these differences repeat hundreds of times per second - and that’s what drives TCO.

Watch the full recording: https://eu1.hubs.ly/H0vtblG0