You can do random restarts, stochasticism, or population based stuff on gradient methods too.

Yes it does require at least an estimate of the gradient, but that's not too hard to get and it converges much faster. Meta-heuristics are only a last resort when you can't get a gradient.

Although they are a bit simpler so there is nothing wrong with using them if the search space isn't too big.

Well... just as evolutionary algorithms are essentially elaborate versions of hill-climbing which enable global search, likewise there are many well-established forms of gradient descent which are global searchers as well.

I think the key distinguishing feature is that gradient descent makes strong assumptions about the nature of the space being searched; and metaheuristics make much more general assumptions.

Meta-heuristics have the flexibility of providing good performance on all Combinatorial Optimisation Problems.

But meta-heuristics, can also be given domain knowledge. and/or be combined with a a heuristic. E.g. Ant Colony Optimisation techniques for Travelling Salesman Problems that combines ACO with Local Search.