This is not true. You almost never need to perform logistic regression on individual observations. Consider that estimating a single Bernoulli rv on N observations is the same as estimate a single Binomial rv for k/N. Most common statistical software (e.g. statsmodels) will support this grouped format.

If all of our covariates a discrete categories (which is typically the case for A/B tests) then you only need to regression on the number of examples equal to the number of unique configurations of the variables.

That is if you're running an A/B test on 10 million users across 50 states and 2 variants you only need 100 observations for your final model.