The complex derivative shouldn't be thought of as representing a "gradient" as such, but rather as a set of instructions for approximating a function near a point where it is differentiable.
Suppose $f\colon\mathbb{C}\to\mathbb{C}$ is complex differentiable at a point $z_{0}\in\mathbb{C}.$ This means that the limit
$$\lim_{h\to0}\frac{f(z_{0}+h)-f(z_{0})}{h}$$
exists and is finite. In turn, this means there must be some "error" function $\epsilon,$ with $\epsilon(h)\to0$ as $h\to0,$ such that
$$f(z_{0}+h)-f(z_{0}) = h[f^{\prime}(z_{0}) + \epsilon(h)].$$
Rearranging this, we find that, for small $\lvert h\rvert,$
$$f(z_{0}+h) = f(z_{0}) + hf^{\prime}(z_{0}) + h\epsilon(h).$$
Since $\epsilon\to0$ as $h\to0,$ we can make an approximation of sorts: when $\lvert h \rvert$ is small, so too is $\lvert \epsilon(h) \rvert.$ Then $\lvert h\epsilon(h) \rvert = \lvert h \rvert \lvert \epsilon(h) \rvert$ must be very small indeed. Thinking of complex numbers as vectors in the plane (as in this image), we have
\begin{equation}
f(z_{0}+h) \approx f(z_{0}) + hf^{\prime}(z_{0}),
\end{equation}
where by "$\approx$" I mean the pointy ends of the vectors are close together. So what does this all mean?
Recall that if you multiply a complex number $a$ by another complex number $b,$ the product $ab$ has magnitude $\lvert a \rvert \lvert b \rvert$ and has argument $\arg(a)+\arg(b).$ The approximation above tells us that if $z_{1}$ is close to $z_{0},$ so that $z_{1}=z_{0}+h$ for some "small" number $h,$ then
$$f(z_{1}) \approx f(z_{0}) + (z_{1}-z_{0})f^{\prime}(z_{0}).$$
That is, to compute $f(z_{1})$ as a vector, you take $f^{\prime}(z_{0})$ as a vector, stretch it (that is, multiply its magnitude by $\lvert z_{1}-z_{0}\rvert$), rotate it (that is, add the arguments), and finally you add on the vector $f(z_{0}).$
So, what is the complex derivative? It's a complex scaling factor, just like the real derivative is a real scaling factor. The only difference is that complex scaling factors introduce rotations. It shouldn't be thought of as representing a "gradient" as such, but rather as a set of instructions for approximating a function near a point where it is differentiable.
To take a specific example, the function $f\colon z\mapsto z^{2}$ is differentiable at the point $1+i$, and it's derivative there is $2(1+i)$ (it's also differentiable everywhere else, I just want a concrete example). By the above, this means that if I have a complex number $z_{1}$ which is close to $1+i,$ then $z_{1}^{2}\approx (1+i)^{2} + (z_{1}-1-i)(2(1+i)),$
i.e., $$z_{1}^{2} \approx -2i + 2(1+i)z_{1}.$$
We can check this numerically: let's agree that $1.1+i$ is close to $1+i.$ Then, setting $z_{1} = 1.1+ i$ gives
$$z_{1}^{2} = 0.21 + 2.2 i,$$
and meanwhile
$$-2i + 2(1+i)z_{1} = 0.2 + 2.2 i.$$
I think if we agree that $1.1+i$ is close to $1+i,$ then we should also agree that $0.2+2.2i$ is close to $0.21+2.2i.$