Log Management

Search logs fast. Keep the trace context.

Filter by service, severity, and field down to the exact log entry. The trace, service, and host behind it are right there, so you investigate from the log itself instead of jumping between pages.

Search across every service — KloudMate Log Explorer KloudMate · Log Explorer Log Explorer · prod Search across every service service=checkout-api level=error,critical +error.code Matched logs 2,184 Errors 39 Window 15m Log volume · error vs info 12:04:01.882 ERROR checkout-api timeout waiting on inventory after 1200ms 12:04:01.224 WARN inventory-worker reconnecting to postgres (attempt 2/3) 12:04:00.991 INFO frontend-proxy GET /api/checkout 200 · trace_id present

Slow search and missing trace context turn logs into noise.

KloudMate keeps logs useful by combining fast search, structured filtering, payload inspection, live streaming, and linked investigation context in one workflow.

What teams can do with Log Management

Use logs as investigation evidence, not as an isolated archive that still needs one more handoff into the rest of your observability stack.

Search with full text and structured fields

Find matching log entries quickly, then narrow them by service, host, severity, environment, and other indexed fields.

Inspect payloads and metadata

Open any entry to review its full body, labels, and attributes before deciding whether you need traces, alerts, or incident context next.

Reuse high-value searches

Save frequently used queries and keep them organized by team, service, or use case so investigations do not restart from scratch each time.

Stream new logs in near real time

Switch to stream mode when a deployment or ongoing incident needs live visibility into what is happening right now.

Search logs across services and environments

The goal is not just to find a line in a file. It is to move from broad log volume to the exact entry that explains the incident.

01

Start with search and filters

Query across your environment, then narrow results by time range, service, severity, host, or environment.

02

Inspect the log entry in detail

Review the full payload, metadata, and structured fields for the entry that best represents the issue.

03

Pivot into related telemetry

Use the linked trace or assistant investigation flow when the log line points to a broader request path or service-level regression.

04

Keep live watch when needed

Use streaming mode while a change rolls out or an incident is still active so the team can see whether the fix is holding.

Isolate the error pattern — KloudMate Log Explorer KloudMate · Log Explorer Filtered results · 2,184 matches Isolate the error pattern env=prod service=checkout-api level=error,critical +error.code 12:18:42.102 ERROR checkout-api upstream_timeout calling inventory 12:18:39.880 ERROR checkout-api upstream_timeout calling inventory 12:18:35.214 WARN checkout-api retry 2/3 succeeded after 840ms 12:18:30.067 ERROR checkout-api upstream_timeout calling inventory 12:18:24.553 WARN inventory-worker connection pool exhausted (max=20) 12:18:19.901 INFO checkout-api circuit breaker opened for inventory

Filter by attributes, severity, service, and time

Log Explorer combines search with field-based filtering so teams can isolate an error pattern without losing the surrounding context they need to explain it.

  • Use full-text search for broad discovery and structured filters for precise slices
  • Open saved queries when the same service or incident pattern repeats
  • Inspect the payload, labels, and metadata of any entry before you escalate
Error pattern → trace → incident context — KloudMate correlation Correlation path Error pattern → trace → incident context 01 Recurring log burst
checkout-api upstream timeouts
02 Trace opened
same request path shows slow inventory span
03 Assistant summary
probable cause and next checks prepared
04 Incident handoff
linked evidence preserved for responders
Related field trace_id and service labels no manual copy/paste needed Best next step Compare inventory logs same 15 minute window as timeout burst

Correlate logs with traces, metrics, and incidents

The valuable log line is usually the one that leads to a better trace, service, or incident view. KloudMate keeps those handoffs close so the entry remains evidence instead of a dead end.

  • Open error logs with the related request or trace already in scope
  • Use Assistant to summarize recurring log evidence and suggest what to inspect next
  • Carry the same log evidence into alerts, issue triage, or active incidents
KloudMate AI

Use KloudMate Assistant to summarize log evidence

Assistant can read an error cluster, summarize the recurring pattern, and point engineers toward the next trace, log filter, or service that deserves attention instead of leaving them to triage line by line.

  • Summarize Explain the recurring error pattern inside the selected log slice
  • Highlight Call out the services, fields, or time windows that look most suspicious
  • Suggest Point to the next trace, search filter, or incident view worth opening
Explore platform
What do these errors have in common? — KloudMate Auto-RCA Assistant on logs What do these errors have in common? Q
Summarize the top recurring errors in the last 30 minutes for checkout-api.
Assistant · likely cause
  • Most failures are upstream timeouts while waiting on inventory.
  • The errors cluster around two hosts and the latest checkout deployment version.
  • Open the related trace set and compare inventory-worker logs in the same window.
Most common pattern upstream_timeout appears in 64% of matching error logs Strong correlation inventory dependency same hosts and trace IDs recur Suggested next step Open linked traces filtered to the latest deployment version

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From telemetry to root cause,
in one platform.

Connect your OpenTelemetry pipeline, AWS integrations, or eBPF agent. Distributed tracing, log management, alerting, and AI-assisted investigation: unified, with predictable pricing.