We consider a budgeted variant of the problem of learning from expert advice with $N$ experts. Each queried expert incurs a cost and there is a given budget $B$ on the total cost of experts that can be queried in any prediction round. We provide an online learning algorithm for this setting with regret after $T$ prediction rounds bounded by $O\!\!\left(\sqrt{\tfrac{C}{B}\  \log(N)T}\right)$, where $C$ is the total cost of all experts. We complement this upper bound with a nearly matching lower bound $\Omega\!\left(\sqrt{\tfrac{C}{B}\ T}\right)$ on the regret of any algorithm for this problem. We also provide experimental validation of our algorithm.