Snowflake's Observe: AI Troubleshooting Up to 10x Faster With Unified Data

Incident investigation has become a bottleneck for engineering teams even as data volume has grown. The article identifies three structural reasons: telemetry data outpaced legacy observability platforms' capacity, modern systems have more complex dependencies due to microservices, and root cause expertise is concentrated in a small number of engineers. The investigation process itself—navigating traces, analyzing logs across time windows, and synthesizing findings across services—resists easy automation.

Observe addresses this by rejecting the "AI layer bolted on top" approach. Instead, it builds three layers in tandem from the ground up. The unified telemetry storage removes data silos that cause teams to query multiple tools and write custom queries manually. The context graph adds semantic structure so the AI understands not just what happened but why and where else it connects. The AI SRE then operates on both, using agent-optimized interfaces designed for the underlying architecture rather than generic chat. The largest gains cited in the article came from investigations that required synthesizing large volumes of data across multiple sources quickly—exactly the workload where a unified platform with context relationships provides the most advantage. Real customer examples span location intelligence, sports and entertainment, and automotive SaaS, suggesting the speedup holds across diverse technical stacks.