Cribl targets telemetry for IT teams
Cribl
Cribl is not trying to become a general purpose warehouse for analysts, it is building the cheapest useful home for machine data that security and IT teams cannot afford to throw away. That means logs, metrics, and traces land in a system built for retention, replay, and fast search, with the workflow starting from SOC and ops teams deciding what stays in Splunk or another SIEM, and what gets pushed into lower cost storage without losing access.
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Cribl comes from the observability pipeline layer, where it already saves customers 30% to 90% on downstream platform bills by filtering and routing telemetry before ingestion. Lake extends that same budget control logic into storage, with up to 1TB per day free and pricing tied to data volume.
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Snowflake and Databricks sell broader data platforms. Snowflake positions its security data lake around long term log retention plus SQL analysis and partner apps. Databricks pushes a lakehouse for data engineering, warehousing, ML, and now AI. Cribl is narrower, with a turnkey lake tuned for unpredictable telemetry rather than enterprise wide analytics workflows.
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That narrower focus matters because the buyer is usually an IT or security team under pressure from exploding log volumes, not a central data platform team building shared models. Cribl already reaches those teams through Stream, Search, and Edge, which makes Lake a natural add on rather than a new platform sale from scratch.
The next step is Cribl turning from a cost saving control point into the default system of record for telemetry data outside the SIEM hot path. If that happens, it captures more of the storage budget, becomes harder to rip out, and pressures broad data platforms to serve security workloads with simpler packaging and lower effective costs.