Snowflake's Observe platform combines unified telemetry storage with a context graph to accelerate AI-driven incident investigation. Customers saw productivity gains of 3x-10x.
The baseline for complex incidents is 370 minutes for root cause analysis with only 30% completion.
Most AI SRE tools lack deep data platform integration.
What happened
Snowflake's Observe platform found that AI assistance can speed up incident investigation by 3x-10x, with an average of over 4x and up to 10x for some customers. The gains come from a unified telemetry data lakehouse and a context graph that models relationships between systems.
Why it matters
Traditional incident investigation is slow and costly. Snowflake's data shows a complex incident takes 10 minutes to detect, 120 minutes to investigate, 15 minutes to remediate, and 370 minutes for root cause analysis, with only 30% completion. Most AI SRE tools fail because they are bolted onto architectures that don't support deep integration.
What to watch
The largest gains were in investigations requiring synthesis of large data volumes across multiple sources. Snowflake measured productivity using a methodology from Anthropic's framework, with 30% of interactions showing more than 5x improvement and 5% exceeding 10x. An automotive SaaS customer cut investigation time from over three hours to minutes.
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Incident investigation has not gotten faster despite more telemetry data, leading many to add an AI layer and call it AI SRE. Snowflake argues this rush often results in AI tools bolted onto architectures not designed to support them, producing fast output but missing crucial information due to lack of deep platform integration.
The company's baseline data shows the scale of the problem: hundreds of on-call engineers stitch together issues using multiple tools, writing custom queries, with root cause expertise concentrated in a few people. Structural challenges include data volume outpacing legacy platforms, complex microservice dependencies, and investigation work that is not easily automated.
Snowflake positions its Observe platform as built with all three required layers from the start, and claims its AI SRE results are grounded in analysis of 3,163 conversation spans from October to November 2025. While these are vendor-reported figures, the pattern of findings across customers supports the argument that architecture, not just the AI layer, determines whether an AI SRE accelerates investigation or merely provides a chat interface for telemetry.
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