Cloud workloads waste up to 60% of their allocated resources because today’s autoscalers react too late or make inaccurate predictions. SYDA introduces a new paradigm—synaptic autoscaling. Inspired by neural learning, SYDA continuously adapts to workload patterns, proactively adjusting compute capacity before shifts in demand occur. It integrates seamlessly with existing telemetry without requiring code changes or exposing data, and works across major cloud providers. In live environments, SYDA has demonstrated cost savings of up to 60% while maintaining strict SLOs. Developed from advanced research in self-adaptive systems, SYDA transforms cloud scaling into a self-learning, performance-driven process.