Klag vs AKHQ

Klag and AKHQ are frequently mentioned together but do different jobs. AKHQ is an interactive web UI for exploring a Kafka cluster; Klag is a headless metrics exporter for consumer lag. AKHQ shows you lag in a browser right now; Klag turns lag into time-series metrics you alert and trend on.

What each tool is

• Klag is a lag metrics exporter. No UI — it publishes metrics to Prometheus, Datadog, or OTLP for dashboards and alerting.
• AKHQ is a Kafka web UI. It lets you browse and search topics and messages, view and manage consumer groups (including resetting offsets), inspect schemas and ACLs, and see current lag interactively.

At a glance

	Klag	AKHQ
Category	Lag metrics exporter	Web UI / console
Interface	Metrics endpoint	Interactive browser UI
Message browsing	❌	✅
Time-series lag & alerting	✅	❌ (point-in-time view)
Runtime	Java / Vert.x / GraalVM	Java / Micronaut
License	Apache 2.0	Apache 2.0

Feature comparison

Feature	Klag	AKHQ
Browse topics & messages	❌	✅
Manage groups / reset offsets	❌ (read-only)	✅
Schema registry & ACL views	❌	✅
Current lag in a UI	❌	✅
Lag as time-series metrics	✅	❌
Lag velocity, time-based lag	✅	❌
Retention / data-loss alerting	✅	❌
Prometheus / Datadog / OTLP export	✅	❌
Alerting on lag trends	✅ (via your stack)	❌
MCP endpoint for AI agents	✅	❌

When to use which

• Use AKHQ when a human needs to look inside the cluster — read messages, debug a payload, reset a group’s offsets, inspect a schema.
• Use Klag when you need lag as metrics: continuous history, dashboards, and alerts that fire before anyone opens a UI. AKHQ’s lag view is point-in-time and read-through-the-UI; it isn’t a Prometheus/Datadog metrics source.

These are complementary. A common setup is AKHQ for interactive exploration and Klag feeding lag metrics into Prometheus/Grafana for continuous alerting.

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