Upper Bound on Runtime of Memoized DP Algorithms
A general trick that works for a lot of DP problems is to look at the parameters in your recursion -- the run time is then $O(n \times max(parameter_1) \times \dots \times max(parameter_k))$ where $n$ is the number of iterations (or, the number of inputs).

Consider for example a DP to solve the subset sum -- the recursion is given by $Q(i,s) := MIN(Q(i − 1,s), Q(i − 1,s − x_i)) $ where $0 \leq s \leq T$ and $T$ is the target sum.

The run time is then $O(n \times max(s)) = O(nT)$. And of course, this trick works with memoization only. If you have not done memoization, it is truly exponential (not pseudo-polynomial).

Why does this work? In each iteration $i$, we have to look at at-most $T$ different sub-problems (post memoization) before we hit the base case.