We introduce eigen-rational kernel interpolants, where the denominator coefficients are determined by the eigenvector corresponding to the largest eigenvalue of the kernel matrix. This construction ensures well-definedness through Perron’s theorem and creates a data-dependent rational basis that is strictly positive definite. We provide stability analysis via Lebesgue functions and error bounds showing convergence rates comparable to standard methods but with superior numerical behavior.