Ingest and search your logs.
Turn patterns into evidence.
Send logs directly to Epok with Fluent Bit, Vector, the OpenTelemetry Collector, syslog, CloudWatch or a curl script. Epok owns ingest, search and live tail, then turns patterns, new errors and rate changes into evidence items with receipts and coverage. Your team and agents use those facts to recognize incidents and investigate causes. Use open shippers — no proprietary Epok agent for server-side telemetry.
An error line shows what a service reported. Understanding its significance takes more context: whether the pattern is new, how its rate changed, which services share it, and which records remain available to check.
Epok keeps search and source records beside the evidence derived from them. Follow the receipts behind a pattern or anomaly, then inspect coverage for missing input, partial windows and checks that did not run. Your team and agents can distinguish an observation from an unresolved explanation. Inspect an evidence item or request MCP agent gateway access.
Full-text + structured search, seconds after POST
Query free text and structured fields together, with a live volume histogram and one-click facets that break any field down by service, level, host, or status. Typically searchable within seconds of arrival — no index to warm, no schema to define.
Live tail — arrival to your screen in ~14 ms
Stream matching logs in real time with a measured p50 of ~14 ms / p95 ~118 ms from arrival to your screen (N=40, our prod). Filter the stream live while it flows to watch a fix take hold.
Automatic pattern clustering — no rules
A million noisy lines collapse into a few hundred templates automatically: variable parts (IDs, timestamps, IPs) are normalized so "timeout after 30s" and "after 12s" become one pattern. You see the shape of your logs — and which shape just started surging — instead of the raw firehose.
New-error detection
Epok fingerprints your error messages and flags the ones that have never appeared in your recent history. "payment-service: FATAL: connection pool exhausted — first seen" — the earliest signal of a fresh failure, surfaced without a rule.
Rate anomalies — spikes, drops, flatlines
Log volume is watched against each service's daily and weekly normal. A spike, a drop, or a service that goes silent when it normally logs steadily — failure with no error, just absence — is flagged automatically, per stream.
Root cause across signals
Log evidence joins traces, metrics and RUM by shared context, service and time window. Analysis cites evidence IDs and commits a cause only when supported; otherwise it says what is missing and what to check next. Recovery recommendations start with change and blast radius while analysis continues. We recommend; you execute.
- ✓Full-text + structured search, live tail, and facets within seconds of pointing any shipper at Epok
- ✓Evidence items for new errors, surging patterns and silent services, with receipts and coverage including what was not seen
- ✓One flat bill across logs, metrics, traces, and RUM — no indexed/retained split, no per-query fee, no cardinality tax; 30-day retention, everything searchable
Your team inspects evidence in the app, Slack and PagerDuty. Agents query the same evidence through the MCP agent gateway, with access on request. The demo uses synthetic data.
Inspect the incident evidence. No signup.
The live demo uses synthetic data. Follow the evidence and its receipts, or send your own telemetry in the 14-day trial.