Cribl vs Datadog
Side-by-side comparison of AI visibility scores, market position, and capabilities
Observability pipeline platform routing, transforming, and reducing machine data before it reaches analytics and security tools; reduces data volumes 30-60% cutting Splunk and cloud SIEM ingestion costs while providing vendor flexibility for data routing.
About
Cribl is a San Francisco-based data infrastructure company that provides an observability pipeline platform — a layer of software that sits between data sources (logs, metrics, traces, security events) and downstream analytics and security tools. Organizations use Cribl to filter, route, transform, enrich, and compress machine data before it reaches expensive destinations like Splunk, Elastic, or a cloud SIEM, reducing data volumes by 30-60% and significantly lowering ingestion and licensing costs. Cribl's platform also provides vendor flexibility by routing the same data to multiple destinations or migrating between analytics platforms without re-instrumenting data sources. The company's Stream and Edge products handle large-scale log pipeline management, while its Search product enables federated query across data wherever it lives. Founded in 2017 by former Splunk engineers, Cribl raised over $400M at a $3.5B valuation from investors including Sequoia Capital, CRV, and IVP. It has grown rapidly as organizations face escalating costs from observability data growth outpacing their tools' affordability.
Cloud observability leader with $2.68B ARR; 750+ integrations; expanding into AI/LLM monitoring as enterprises instrument generative AI workloads at scale in 2025.
About
Datadog is a cloud-native monitoring and security platform founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, headquartered in New York City. The company went public on Nasdaq (DDOG) in September 2019 and has grown to serve over 29,000 customers as of FY2024, generating $2.68 billion in annual recurring revenue, representing approximately 26% year-over-year growth. Datadog's platform spans infrastructure monitoring, application performance management (APM), log management, security monitoring, and AI observability, positioning it as the unified observability stack for cloud-scale engineering teams.
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