We present a novel method for near real-time snow avalanche detection using Distributed Acoustic Sensing (DAS). A ∼10 km long telecommunication cable permanently installed along the avalanche-prone Flüelapass road (Swiss Alps) was continuously monitored over a full winter. Avalanches, including events that did not physically reach the cable, were clearly recorded and confirmed with photographic evidence. To discriminate avalanches from anthropogenic signals, we introduce a dual-frequency short-term over long-term average attribute that produces coherent high-value spatio-temporal signatures for avalanches, while vehicles predominantly generate negative values with pronounced move-out. The workflow consists of (1) a quasi-instantaneous threshold-based trigger to detect onset time and location, followed by (2) a rapid waterfall image analysis to estimate event extent and invalidate traffic-induced alerts. The first step issues alerts with millisecond-scale latency and meter-scale spatial resolution. The second step introduces additional latency, as it requires the event to sufficiently develop in order to assess its spatio-temporal morphology and confirm or discard the initial trigger. Our system issued alerts only 4.5 ‰ of the time when the road pass was open (i.e. 2.5 hours over 23 days), demonstrating the robustness against traffic, and 0.36 ‰ of the time when the pass was closed (i.e. 55 minutes over 108 days). Among those, a total of 73 potential avalanches were identified, most of them occurring during three independently documented avalanche episodes. These findings demonstrate that DAS represents a viable and cost-effective solution for operational real-time avalanche monitoring, with potential applicability to broader natural hazard detection.