Using LLM for Anomaly Detection

Anomaly detection has outgrown static thresholds and hand-tuned statistical models. Modern infrastructure emits unstructured logs, semi-structured traces, and high-cardinality metrics that are difficult to capture with rigid rules. Large language models can reason directly over these raw signals, identify subtle failure patterns, and produce human-readable explanations in a single inference pass. The practical barrier is cost. Feeding thousands of log lines or hours of telemetry into an LLM scales token usage quickly on traditional providers. Oxlo.ai removes that barrier with flat, request-based pricing that stays predictable even when you pack an entire incident window into the prompt.

Why LLMs for Anomaly Detection