Reduce alert noise & LLM costs with lightweight triage + caching

Many mid-to-large enterprises—roughly 200,000 organizations that each spend about $150,000/year on observability and incident ops—are drowning in alert noise and paying for expensive inference on every incident candidate. Operations teams routinely see thousands of alerts per day with noise rates commonly exceeding 70–80%, driving fatigue, missed signals and high costs when teams route every item to always-on LLM services for enrichment or triage. You could build a lightweight triage and caching layer that sits in front of downstream incident tools to deduplicate, classify, and enrich alerts using deterministic rules and small local models, only escalating ambiguous or novel cases to larger LLMs. By caching canonical alert summaries and operator responses and using hybrid inference to run inexpensive models locally, the system could cut the volume of alerts surfaced to humans by multiple-fold and reduce LLM calls by a targeted 50–90% depending on the org’s profile. The timing is favorable: a $30B addressable market, an ongoing telemetry explosion, and growing AIOps maturity mean buyers are actively seeking ways to control noise and costs rather than adding yet another dashboard. The shift to hybrid inference and on-prem/smaller models lowers barriers for deploying local triage logic that meets latency, cost and privacy requirements. To stand out you’ll need pragmatic integrations (Datadog, Splunk, PagerDuty), strong caching semantics, and measurable ROI dashboards showing dollars and minutes saved; these are achievable strengths but require careful engineering. The main challenges are avoiding suppression of true positives, maintaining high ingestion fidelity, and competing with incumbent vendors and internal tooling—success will hinge on early enterprise pilots that prove reliability and cost reduction.