Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent. We present a comparative evaluation of training-free methods for aligning language generation to user-specific toxicity sensitivities across three inference-time intervention stages: pre-decoding (prompt conditioning and rewriting), in-decoding (token, logit, and representation steering), and post-decoding (candidate re-ranking). Evaluated against classifier-based toxicity targets derived from the PRISM dataset, all methods reduce alignment error by 28-47%. However, the results show trade-offs between alignment effectiveness, personalization, and general language quality.