I will give some answers in terms of diffusion processes, since the examples are easiest for me to describe in that context. There are more general examples which follow the same pattern, but typically require additional care to state rigorously.

Suppose that you are interested in the Markov process

$$\mathrm{d} X = b(X) \, \mathrm{d} t + \mathrm{d} W,$$

with $b$ some vector field. If $b$ is contractive, in the sense that for some $K > 0$, the inequality

$$\langle b(X) - b(Y), X - Y\rangle \leqslant - K \| X - Y \|_2^2$$

holds globally, then it follows that the invariant measure of the process is unique and has sub-Gaussian concentration with a constant depending on $K$, see e.g. here for details.

Actually, suppose that it only holds that

$$\langle b(X) - b(Y), X - Y\rangle \leqslant - K ( \| X - Y \| ) \cdot \| X - Y \|_2^2$$

for some regular function $K$ which may be negative, but satisfies $\lim \inf_{r \to \infty} K(r) > 0$, i.e. 'distant contractivity'. In this case, sub-Gaussian concentration again holds for the invariant measure (under a few bonus regularity assumptions, and with a slightly harder-to-derive constant) as a consequence of the results in this work.

In another direction, suppose that the same diffusion satisfies a drift condition, i.e. there is some positive, coercive function $V$ with reasonable sub-level sets such that

$$LV \leqslant -\gamma V + C$$

for some $\gamma, C > 0$, where $L$ is the infinitesimal generator of the diffusion. Then, results along the lines of this work give that the invariant measure satisfies a Poincaré inequality, and hence admits sub-exponential concentration. Under stronger drift conditions, a subset of the same authors are able to obtain various stronger concentration properties; see this chapter for a collection of examples to this effect.

[ Note: I realised after initial posting that this latter example requires the reversibility of the process, which is often difficult to check without knowing the invariant measure of the process ]