Logswiz applies LLM reasoning to 100% of your infrastructure events, every deployment signal, runtime anomaly, and performance drift, before it wakes someone at 3am.
No credit card required · Up and running in minutes
Every pod, service, and deployment generates signals. To manage costs, teams sample, filter, and drop the majority. The anomaly that causes the outage is almost always in the pile they skipped.
40+
Avg. minutes MTTR
When the signal existed in sampled-out data hours before the alert fired. Full coverage means detection in minutes, not after damage is done.
<10%
Of log volume typically indexed
The rest is dropped to control observability costs. Context lives in the unindexed 90%, making postmortems guesswork.
100%
Logswiz event coverage
Every log line, every metric, every trace reasoned over by LLM intelligence in real time. Nothing dropped.
What becomes possible when LLM reasoning runs over 100% of the data.
A global e-commerce platform reduced mean time to resolution from 40 minutes to 3 minutes by applying LLM reasoning to 100% of infrastructure events — eliminati...
Read More →A European SaaS provider shifted from reactive incident response to proactive prevention — catching 94% of would-be production incidents at the drift stage, bef...
Read More →A financial data provider reduced the time each SRE spent on postmortem analysis from 11 hours per week to under 45 minutes — by replacing manual log triage wit...
Read More →Ingest from Kubernetes, Docker, cloud providers, and any OTEL-compatible source. 100% of events, nothing sampled away.
LLM intelligence classifies each signal, detects drift patterns, and correlates anomalies across services in sub-millisecond latency.
Instead of raw alerts, Logswiz delivers contextualized intelligence: what changed, why it matters, and where to look first.
Push enriched intelligence to PagerDuty, Jira, Slack, or any webhook. Fits your existing incident workflow without replacement.
1000× y
Cost to reason over x volume of infrastructure events
y
Same x volume. Same reasoning. Fraction of the cost.
Inference cost ratio
1000×
less to reason over the same data
Which means
Full coverage
becomes viable
// SAME MODEL. SAME OUTPUT. 1000× THE ROI.
The value Logswiz delivers comes from two compounding factors: the volume of infrastructure events it reasons over that were previously sampled away, and the reliability of the output that makes LLM inference genuinely trustworthy.
Together they produce a return on investment that reframes the question entirely, not "can we afford to do this?" but "what has it been costing us not to?"
Get Started Free →See what 100% infrastructure event coverage looks like for your stack, with a model reliable enough to put into production.