Possible lag series
From just 100 topics × 50 partitions × 20 consumer groups. Parseable brings high-cardinality Kafka telemetry into a columnar store, so teams can query lag, broker health, logs, and traces without stitching tools together.
Avg incident diagnosis time without correlated signals
A Kafka incident spans multiple servers, log files, and tools. Without a unified view, engineers spend hours doing manual detective work while production is still affected.
MTTR with unified Kafka telemetry
When logs, metrics, and traces live in the same queryable store, a single SQL query joins a latency spike, a broker queue event, and a leader election.
Why Parseable
Metrics, logs, and traces from your Kafka cluster land in one place, stored in columnar format and queryable with SQL. No more switching between JMX dashboards, log tailing, and trace UIs to find the same root cause.
Broker metrics from JMX, controller logs, and producer-to-consumer traces land in a single Parseable stream. Query consumer_group lag alongside broker_id health and partition offsets in one SQL statement.
Kafka's high-volume telemetry that creates storage overhead in row-based systems compresses efficiently in Parseable's columnar format.
Correlate consumer lag, broker queue pressure, state-change logs, and traces in SQL or plain English.
Send Kafka metrics, logs, and traces through your existing OTel Collector. Parseable accepts the OTLP format out of the box. No re-instrumentation, no new agents.
Choose Parseable Cloud or BYOC, while keeping Kafka telemetry aligned with your security and infrastructure model.
Use Cases
Monitor applications, databases, infrastructure, network, and cloud providers from a single platform. Parseable's columnar storage keeps high cardinality telemetry manageable without forcing you to drop labels or cap retention.
Monitor applications, databases, infrastructure, network, and cloud providers from a single platform. Parseable's columnar storage keeps high cardinality telemetry manageable without forcing you to drop labels or cap retention.
Leverage telemetry data from MCPs, LLMs, and Agents to build end-to-end visibility with confidence. Track token usage, latency per model, and cost across inference calls.
Total sessions
1,246
−12%
Total tokens
18.2M
+23%
Latency P95
5.32s
−7%
Error rate
2.4%
−17%
My order #88821 hasn't arrived and I'd like a refund...
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Understand user behavior, feature adoption, and performance to optimize the user experience. Correlate product events with infrastructure telemetry for a complete picture.
Capture and analyze user activity, system events, and security logs to ensure compliance and security. Columnar storage keeps audit queries fast even at billion-event scale.
2026-04-29T09:23:59.847
2026-04-29T09:24:01.203
Also from Parseable
Handle high cardinality logs, metrics, and traces at a fraction of the cost with columnar storage.
Monitor token usage, latency, cost, and agent behavior across your full LLM and MCP stack.
Billion-event audit trails with sub-second query performance and configurable retention policies.
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