Early access is open — join the waitlist at Logsense
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