According to a recent LinkedIn post from ClickHouse, the company is positioning its cloud platform as an enabler of a newer observability model based on so‑called “wide events” rather than separate logs, metrics, and traces. The post highlights a customer example from Qonto, which reportedly shifted away from a Grafana Tempo-based setup that imposed short query windows, mandatory sampling, and high costs for high-cardinality data.
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The post suggests that by adopting ClickHouse Cloud, Qonto expanded query windows from hours to weeks and was able to retain and exploit higher-cardinality data for observability. It also notes that, using the ClickHouse MCP server, Qonto built an AI-supported incident investigation tool that allows non-SQL users to query production incidents in natural language.
From an investor perspective, the content underscores ClickHouse’s effort to align its offering with data-intensive observability and AI operations use cases, areas that are attracting growing enterprise budgets. Demonstrated customer benefits such as broader data retention and AI-enabled incident response may strengthen ClickHouse’s value proposition versus legacy observability stacks, potentially supporting customer acquisition and cloud revenue growth.
The emphasis on replacing or augmenting incumbent observability tools could indicate that ClickHouse is targeting higher-value, mission-critical workloads where performance and scalability are key differentiators. If this positioning gains traction across similar digital-first customers, it may enhance the company’s competitive stance in the observability and data infrastructure markets and improve its long-term monetization prospects.