Uptrace
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Log management

Find, group, and understand your logs

When an alert fires, you need to find the relevant log lines fast, not scroll through thousands of identical error messages. Uptrace groups similar logs into patterns automatically, lets you search by any attribute in sub-second, and links every log line to its trace with a single click.

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Find any log in seconds

Full-text search across billions of log events with AND/OR logic, attribute filters, and regex; results return in sub-second regardless of volume

  • Full-text search with AND/OR operators across all indexed fields
  • Filter by severity, service, host, environment, or any custom attribute
  • Attribute-scoped queries: service_name:api, -level:debug, host:prod-*
  • Volume histogram shows log density, so you spot anomalies before you search
  • Groups view surfaces recurring patterns with frequency and last occurrence

Structured logs as first-class data

Every attribute in your structured logs is indexed and filterable at ingest: JSON, logfmt, or plain text, no field mapping required

  • All structured attributes indexed at ingest time, no schema to define upfront
  • Support for JSON, logfmt, and unstructured plain-text formats
  • Automatic severity normalization across different logging frameworks
  • Up to 90% storage savings via ClickHouse compression
  • Detail panel with five tabs: attributes, group, JSON, context, tags

See patterns, not an endless stream

Similar log messages are fingerprinted and grouped automatically so you see a handful of recurring patterns instead of thousands of individual lines

  • Automatic grouping of similar messages by fingerprint at ingest time
  • Grok-style rules to normalize dynamic parts: IPs, request IDs, numbers, timestamps
  • Groups view shows frequency and last occurrence for each pattern
  • Timeseries chart shows pattern frequency over time, so you catch regressions early
  • Jump from any group into its individual log records in one click

From log to trace in one click

Every log line that carries a trace ID links directly to its span: no copying IDs between tools, no separate query

  • Automatic log-to-trace correlation via trace_id and span_id
  • CONTEXT tab shows neighboring log entries from the same trace
  • Click any trace_id in a log line to open the full trace waterfall instantly
  • Drill from a trace waterfall into its logs without leaving the view
  • Same service and environment filters apply across logs and traces

Collect logs from any pipeline

Uptrace ingests logs from OpenTelemetry, Vector, FluentBit, CloudWatch, Heroku, and more, using the pipeline you already run

  • OpenTelemetry Collector for structured logs from any OTel SDK
  • Vector with VRL transforms for high-throughput, pre-processed pipelines
  • FluentBit for lightweight container and Kubernetes log collection
  • AWS CloudWatch and Heroku platform logs via native receivers
  • Loki bridge for teams migrating from Grafana Loki

Alert on log events

Set monitors on log error rates, specific patterns, or volume thresholds, and get notified before users report the problem

  • Log monitors based on error count, pattern frequency, or volume change
  • Channels: Slack, Teams, PagerDuty, Opsgenie, Telegram, webhook
  • Alert body includes log samples for immediate context
  • Combine log alerts with metric and trace conditions in one monitor
  • Silence windows for planned maintenance

Better together

Each signal is more powerful when correlated with the others. Uptrace stores traces, logs, and metrics in one place, no tool-switching, no data gaps.

Distributed tracing

Find the exact span that broke your request, across every service and database call.

Metrics

PromQL-compatible metrics with no cardinality limits and automatic dashboards.

Common questions, clear answers

What log formats does Uptrace support?
Uptrace accepts structured logs (JSON, logfmt), unstructured plain-text logs, and OpenTelemetry log records. Ingestion is available via OpenTelemetry SDK, OpenTelemetry Collector, Vector, FluentBit, AWS CloudWatch, Heroku, and the Loki bridge.
How does log-to-trace correlation work?
When your application writes a log line with a trace_id and span_id (which OpenTelemetry SDKs do automatically), Uptrace links that log entry to the corresponding span. From any log record you can jump directly to the full trace waterfall, and vice versa.
Can I set up alerts on specific log patterns?
Yes. Log monitors can trigger on error count, pattern frequency, or volume change over a configurable window. Alerts are delivered via Slack, PagerDuty, Microsoft Teams, Opsgenie, Telegram, or custom webhook, and include log samples for immediate context.
How is Uptrace different from an ELK stack or Grafana Loki?
ELK requires three separate systems (Elasticsearch, Logstash, Kibana) that you operate independently. Loki requires Grafana for visualization and lacks native trace correlation. Uptrace is a single binary that stores logs, metrics, and traces together: log-to-trace correlation and pattern grouping work out of the box without plugins.
How do I send logs from Kubernetes?
The most common approach is deploying FluentBit as a DaemonSet to collect container stdout and stderr and forward them to Uptrace via the OpenTelemetry Collector. Alternatively, you can use the OpenTelemetry Operator to auto-inject the OTel SDK into your pods for structured log output.

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