This is really about power calculations, finding the sample sizes that maximise the probability that the test rejects the null hypothesis that the two conversion rates are equal, when in fact a specific alternative hypothesis is true.
It is sensitive to a large number of factors, including the precise test used, the statistical significance criterion used, and population rates, as well as the underlying distribution and parameters (here a binomial distribution - so discrete). There is also the question that you would be doing the test because you are not certain whether there is a difference, and so would not have a justification for making the sample sizes different if you do not have to.
The chart below gives my calculations for the power for different sample sizes for $B$ and the rest of the $10000$ for $A$, using a chi-squared test (no continuity correction) with significance $0.95$ if the true population probability of conversion is $0.015$ for $A$ and $0.01$ for $B$.
It suggests that the power in this case is maximised when the sample size for $A$ is slightly less than half, possibly because this gives $B$ more chance of producing some conversions close to the population proportion; this is not a simple variance argument, as the Bernoulli variance $p(1-p)$ is higher for $A$. It also suggests some local volatility: the power for $4572$ is about $0.6346$ while for $4573$ is noticeably smaller at about $0.6136$, but this is consequence of the precise values of $0.95$ and $0.015$ and $0.01$ and this step down could disappear if they were marginally different.
I think the message from this is that, given the choice, make the two sample sizes broadly equal. If for some reason that is not possible, then consider oversampling the less common scenario so as to make the effective sample sizes less unequal, in order to increase the power.