Goodbye limitations, hello data: How Qonto is rethinking observability with ClickHouse Cloud

ClickhouseClickHouse TeamMay 1, 2026

Source: Clickhouse
Published: May 1, 2026
Added to Yomu: Aug 28, 2026

Problem & solution

Summary

Qonto struggled with slow queries, system instability, and aggressive data sampling when handling distributed traces in Grafana Tempo. To enable wide-event querying without strict cardinality limits, the team migrated their telemetry storage to ClickHouse Cloud. Telemetry from applications, Kubernetes, and GitHub routes through OpenTelemetry collectors over AWS PrivateLink into ClickHouse, while Grafana remains the primary dashboard interface. By compressing 231 TB of uncompressed trace attributes down to 376 GB, Qonto achieved substantial storage savings and integrated an AI incident companion using the ClickHouse Model Context Protocol. The new architecture eliminated cardinality policing, allowed product and support teams to self-serve incident investigations, and enabled real-time streaming pre-aggregations using Apache Flink.

Context

Qonto relied on Grafana Tempo for tracing, but slow aggregations and crashes forced engineers to restrict queries to short time windows and sample data aggressively. SREs also had to heavily police metric and span cardinality, preventing teams from capturing high-cardinality metadata needed during incident investigations.

Approach / What changed

Qonto adopted ClickHouse Cloud as its columnar telemetry engine, ingesting data from applications, Kubernetes, and GitHub via OpenTelemetry collectors over AWS PrivateLink, visualized through Grafana. They integrated an AI incident companion using the ClickHouse MCP server with a 30-line Python read-only security layer to query data via natural language.

Takeaways

ClickHouse compressed 231 TB of uncompressed high-cardinality trace attributes into 376 GB, achieving a 99.84% compression ratio and saving an estimated $70,000 annually in S3 storage costs.
Telemetry routes through OpenTelemetry collectors directly to ClickHouse Cloud across AWS PrivateLink, eliminating cross-boundary data transfer fees and latency while standardizing schemas for LLMs.
Qonto implemented an AI incident companion using a ClickHouse Model Context Protocol (MCP) server with a 30-line Python read-only layer, allowing staff to investigate incidents and isolate root causes in natural language.

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Goodbye limitations, hello data: How Qonto is rethinking observability with ClickHouse Cloud

ClickhouseClickHouse TeamMay 1, 2026

Source: Clickhouse
Published: May 1, 2026
Added to Yomu: Aug 28, 2026

Problem & solution

Summary

Context

Approach / What changed

Takeaways

ClickHouse compressed 231 TB of uncompressed high-cardinality trace attributes into 376 GB, achieving a 99.84% compression ratio and saving an estimated $70,000 annually in S3 storage costs.
Telemetry routes through OpenTelemetry collectors directly to ClickHouse Cloud across AWS PrivateLink, eliminating cross-boundary data transfer fees and latency while standardizing schemas for LLMs.
Qonto implemented an AI incident companion using a ClickHouse Model Context Protocol (MCP) server with a 30-line Python read-only layer, allowing staff to investigate incidents and isolate root causes in natural language.

Read original post

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