As large language models are increasingly deployed in high-stakes domains, it is essential that their outputs reflect not average human preference, but diverse perspectives. Achieving such pluralism remains challenging: existing pluralistic alignment methods rely on prompt-level interventions, represent only a limited set of values, or are compute-heavy, lacking explicit value control and representation. Furthermore, existing steering methods require manual value selection and cannot dynamically adapt to scenario-dependent value requirements, limiting their scalability and generality. To address these limitations, we introduce VISPA, a training-free pluralistic alignment framework that performs automatic, context-driven value selection and modular value-steered generation. VISPA identifies scenario-relevant values dynamically and applies activation steering to produce value-conditioned responses representing distinct perspectives. These responses enable support for three pluralistic alignment modes: Overton, Steerable, and Distributional. VISPA supports the addition of new values without finetuning and is compatible with diverse steering instantiations and steering models. Across extensive experiments spanning multiple models and evaluation settings, VISPA demonstrates strong performance across all pluralistic alignment modes in healthcare and beyond.