AI SRE Done Right: Why Your Data Foundation Matters

Incident investigation remains slow despite abundant telemetry data, with complex incidents averaging 10 minutes to detect, 120 minutes to investigate, 15 minutes to remediate, and 370 minutes for root cause analysis—only 30% of which is completed. Teams rely on hundreds of engineers manually stitching together data from multiple tools, writing custom queries, and reconstructing event chains. This inefficiency stems from legacy observability platforms struggling with modern distributed systems, where microservices and interdependencies complicate investigations. The volume and complexity of telemetry data have outpaced traditional tools, making automated root cause analysis difficult without a robust data foundation.

Most AI SRE solutions fail to deliver meaningful improvements because they are not deeply integrated into the underlying data platform. A chat-based interface or superficial layering over telemetry data does not address the core challenges of observability. Effective AI SRE requires accuracy, speed, and efficiency during active incidents, which depends on a data foundation capable of supporting autonomous operation. Without unified storage, semantic context modeling, and AI-optimized interfaces, AI tools risk producing incomplete or misleading results, compounding costs rather than resolving incidents faster.

Effective AI-driven observability relies on three interconnected layers: unified telemetry storage, a context graph modeling relationships, and an AI SRE designed to leverage both. Unified storage consolidates logs, metrics, and traces at scale without prohibitive costs, while context graphs provide semantic relationships between infrastructure, applications, and business data. The AI SRE layer must operate on this foundation to deliver accurate, low-latency results. Without these layers working in tandem, AI tools risk generating fragmented insights that fail to capture the full scope of an incident, leaving critical gaps in investigation.

Observe by Snowflake integrates these three layers, enabling customers to troubleshoot incidents up to 10 times faster, with an average improvement of over 4 times. The platform’s data lakehouse stores high-fidelity telemetry at low cost, while its context graph structures data with semantic relationships across business, application, and infrastructure layers. The AI SRE layer operates on this foundation, accelerating the investigation loop and making advanced observability accessible beyond expert users. This approach addresses the root causes of inefficiency in modern incident response.