How I Built AI-Powered Log Triage in Go (and Made It 100x Cheaper with Fingerprinting)

A developer built LogSense, an AI-powered log triage tool in Go that uses fingerprinting to deduplicate errors, reducing LLM costs by up to 100x. By normalizing and hashing error events, LogSense ensures only unique failures trigger AI analysis, making the system economically viable even during incident spikes. The tool is now in early access with a waitlist at Logsense.cloud.

Early access is open — join the waitlist at Logsense https://logsense.cloud I got tired of two things: So I built LogSense . LogSense = drop in an API key, get AI root-cause analysis on every error. No dashboards. No rules. The core idea is simple: Same error 1000 times = 1 LLM call. That one design choice changed the economics completely. Most pipelines treat every incoming log line as independent work. If one bug explodes during an outage, you might see thousands of near-identical stack traces. Naive flow: That gets expensive fast, and signal quality drops because you’re summarizing noise, not incidents. At a high level: This means volume spikes do not linearly increase AI cost. The fingerprinting layer does the heavy lifting. For each error event, LogSense normalizes unstable fields: Then it hashes the stable structure message + stack shape + service context . So these: panic: nil pointer at user id=12345 panic: nil pointer at user id=67890 collapse into the same canonical signature if they’re the same underlying defect. Result: one root-cause analysis for one issue, regardless of repetition count. During incident windows, repeated errors dominate traffic. Without dedup, your AI bill scales with chaos. With dedup, your AI bill scales with unique failures . That’s the model LogSense is built around: func process event LogEvent { normalized := normalize event fp := fingerprint normalized if cache.Exists fp { cache.IncrementCount fp return } analysis := llm.Analyze buildPrompt normalized cache.Store fp, analysis publish analysis } Early access is open — join the waitlist at Logsense https://logsense.cloud