A LinkedIn post from ClickHouse highlights how digital bank Qonto is rethinking observability by moving from traditional logs, metrics, and traces to so‑called “wide events.” According to the post, this approach consolidates full-fidelity data into a single system and allows teams to derive the needed signals at query time rather than discarding or sampling data upfront.
Claim 55% Off TipRanks
- Unlock hedge fund-level data and powerful investing tools for smarter, sharper decisions
- Discover top-performing stock ideas and upgrade to a portfolio of market leaders with Smart Investor Picks
The post describes limitations in Qonto’s prior Grafana Tempo-based stack, including query windows reportedly capped at two to three hours, mandatory sampling, and high costs associated with high-cardinality data. By contrast, ClickHouse Cloud is presented as enabling query windows stretching from hours to weeks and encouraging higher cardinality, potentially indicating a stronger fit for data-intensive observability workloads.
In addition, the post notes that Qonto leveraged the ClickHouse MCP server to build an AI-driven incident companion, which lets employees investigate production incidents in natural language without using SQL. This suggests ClickHouse is positioning its cloud offering not only as a high-performance analytical database, but also as an enabler of AI-assisted operations and self-service analytics across non-technical users.
For investors, this use case may signal expanding adoption of ClickHouse in observability and reliability engineering, segments historically dominated by specialized monitoring vendors. If replicated across more customers, such deployments could increase recurring cloud revenue, strengthen ClickHouse’s competitive positioning in data infrastructure, and deepen its role in AI-enabled operational tooling.