Klag vs Burrow
Both Klag and Burrow monitor Kafka consumer lag with read-only access. They differ in philosophy: Burrow evaluates lag against built-in status rules and pushes notifications; Klag exports a richer set of lag metrics and leaves evaluation and alerting to your observability stack.
What each tool is
Section titled “What each tool is”- Klag is a lag metrics exporter. It polls the Kafka Admin API on an interval and publishes metrics to Prometheus, Datadog, or OTLP.
- Burrow is a lag monitor with an evaluation engine. It classifies each consumer group’s status (OK / WARN / ERROR) using a sliding-window algorithm and can push notifications over email and HTTP. It exposes an HTTP API; Prometheus metrics come via a separate exporter.
At a glance
Section titled “At a glance”| Klag | Burrow | |
|---|---|---|
| Category | Lag metrics exporter | Lag monitor + notifier |
| Runtime | Java / Vert.x / GraalVM | Go |
| Primary output | Prometheus, Datadog, OTLP | HTTP API + notifiers |
| Lag evaluation | In your alerting stack | Built-in status rules |
| License | Apache 2.0 | Apache 2.0 |
Feature comparison
Section titled “Feature comparison”| Feature | Klag | Burrow |
|---|---|---|
| Lag in messages | ✅ | ✅ |
| Consumer group state | ✅ | ✅ |
| Lag status evaluation rules | ❌ | ✅ |
| Built-in notifiers (email/HTTP) | ❌ | ✅ |
| Lag velocity (growing/shrinking) | ✅ | ⚠️ status only |
| Time-based lag (ms) + time-to-catch-up | ✅ | ❌ |
| Hot partition detection | ✅ | ❌ |
| Data-loss / retention alerting | ✅ | ❌ |
| Commit staleness (stuck consumers) | ✅ | ❌ |
| MCP endpoint for AI agents | ✅ | ❌ |
| Prometheus native | ✅ | ⚠️ separate exporter |
| Datadog / OTLP native | ✅ | ❌ |
| Read-only DESCRIBE ACLs | ✅ | ✅ |
When to use which
Section titled “When to use which”- Use Burrow if you want a self-contained tool that decides “is this group healthy?” and pushes a notification, without a metrics pipeline. Its status-window algorithm is its signature feature.
- Use Klag if you already run Prometheus / Grafana / Datadog and want lag as first-class metrics — plus velocity, time-based lag, retention risk, and hot-partition signals — that you alert on with your existing rules and dashboards. Klag also exposes a read-only MCP endpoint for AI agents, which Burrow does not.
Klag was inspired by the archived kafka-lag-exporter; if you’re coming from that project, the migration guide maps every metric.
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