There's a conjugate updating solution for Poisson regression, if your model terms are for categorical variables. I.e. whenever you can summarize the results with sufficient statistics (which in this case are number of events for each combination of categories and, if time periods differ between observations, also the total follow-up time). This is basically straight from the theorems on when conjugate priors exist.
The difficult thing is of course, whether you actually get a conjugate prior that is analytically tractable.
Chen and Ibrahim have some results on how to construct priors for generalized linear models (Chen, M.-H. and Ibrahim, J. G. (2003). Conjugate priors for generalized linear models. Statistica Sinica, 13(2):461–476.). I used those in Section 5 of my PhD thesis to provide the analytical solution for conjugate updating for exponential time-to-event with right-censoring (which is also the solution for Poisson regression, because up to constants the likelihoods are the same) comparing two groups (i.e. there is just a single categorical variable in the regression model) in randomized trial (fully analytically tractable, just a bit hard to get specific priors your might want for the regression coefficient - I proposed one particular solution for that in Section 5.3). This solution is equivalent to assuming independent Gamma priors for the rates in the treatment group and for the control group. I gave formulations in terms of hazard (or rate) ratio + control group (hazard) rate, as well as log-ratio + control rate, and while I didn't give it you should be able to get the log-ratio + log-control-rate parameterization via change of variables (no promises that that's nicely tractable). However, once you do a meta-analysis - i.e. you have two categorical variables, one with 2 levels (=treatment vs. control group) and one with many levels (different studies) - you still have conjugate solution, but you are left with an integral that, as far as I am aware, does not have a nice analytical solution and needs to be evaluated numerically.
I also once tried to do this for logistic regression and I believe that's actually less tractable than the Poisson situation (I'd love to hear otherwise, if someone knows a nice treatment of the topic). As far as I know, you are also in trouble, if you try to add random effects to commonly used GLMs. To be honest, I have not encountered many real-life problems, where conjugate solutions for regression models were really an option. Luckily we have really good MCMC samplers nowadays.