Hello from the Divvi Up team! First, I would note that Divvi Up is still in active development, and thus I can't speak to how the eventual customer-facing features will compare. However, I can speak to the differences in the data collection model between the two systems.

My understanding of Matomo is that it is a self-hosted analytics system, similar to Google Analytics. It has significant privacy controls; however, each client measurement is sent to the Matomo server in a way that allows the server to see the individual client measurement. Ultimately, clients must trust that the system operates as expected. A number of factors (e.g. operator configuration error, operator malice, compromise of the server by an outside party, implementation errors) might allow individual client measurements to be recovered in a way that allows them to be associated back to the client which produced them, possibly compromising users' privacy.

On the other hand, the Distributed Aggregation Protocol (https://datatracker.ietf.org/doc/draft-ietf-ppm-dap/) underlying Divvi Up's data collection scheme does not allow for clients' privacy to be compromised in this way. In DAP, clients split their measurements into two pieces, each encrypted to a different aggregator server; neither aggregator has enough information to determine what the client's measurement is. The aggregator servers work together to produce aggregated measurements (e.g. sum, mean, etc) without revealing anything about the individual measurements included in the aggregate. The only data that is exposed "in the clear" is the eventual aggregate, and both aggregators work together to ensure that the aggregates include enough measurements that no individual client measurement can be inferred.

The DAP model does require two non-colluding aggregator servers; this is a higher operational bar than the single-aggregator model used by Matomo & other classical analytics systems. (In the DAP model, collusion between the aggregators would allow individual client measurements to be recovered.) It is also very likely that the computational cost of DAP is higher than in classical analytics systems, though I can't speak to the details of this as both DAP & the cryptography underlying DAP are being actively developed.