Fixed-parameter algorithms and kernelization are two powerful methods to solve NP-hard problems. Yet so far those algorithms have been largely restricted to static inputs. In this article, we provide fixed-parameter algorithms and kernelizations for fundamental NP-hard problems with dynamic inputs. We consider a variety of parameterized graph and hitting set problems that are known to have f ( k ) n 1+o(1) time algorithms on inputs of size n , and we consider the question of whether there is a data structure that supports small updates (such as edge/vertex/set/element insertions and deletions) with an update time of g ( k ) n o(1) ; such an update time would be essentially optimal. Update and query times independent of n are particularly desirable. Among many other results, we show that F EEDBACK V ERTEX S ET and k -P ATH admit dynamic algorithms with f ( k )log O(1) update and query times for some function f depending on the solution size k only. We complement our positive results by several conditional and unconditional lower bounds. For example, we show that unlike their undirected counterparts, D IRECTED F EEDBACK V ERTEX S ET and D IRECTED k -P ATH do not admit dynamic algorithms with n o(1) update and query times even for constant solution sizes k ≤ 3 , assuming popular hardness hypotheses. We also show that unconditionally, in the cell probe model, D IRECTED F EEDBACK V ERTEX S ET cannot be solved with update time that is purely a function of k .