Snowflake’s intent to acquire Observe is more than a tuck-in. It’s a signal that “data platforms” and “operations platforms” are converging fast. In a world where AI agents are increasingly embedded in customer-facing experiences and core business workflows, the health of applications, pipelines, and data products is no longer just an IT concern. It’s a revenue, trust, and decision-quality concern.
The underlying message from this deal is clear. Telemetry is now more than a tactical dataset. Logs, metrics, traces, events—these streams are exploding in volume and value as systems become more distributed and AI-driven. Snowflake and Observe are positioning observability as something you query, govern, retain, and operationalise at scale. It’s not just about a dashboard you check when things break.
Snowflake’s messaging emphasises economics and scale. Ingesting and retaining “100% of telemetry data” cost-effectively is the right foundation for better troubleshooting and automation. You can’t fix what you can’t see, and you can’t see what you can’t afford to store.
Observe’s pitch, especially when coupled with Snowflake, leans heavily into AI-assisted incident investigation and faster root-cause analysis (including claims of materially faster resolution).
But here’s the reality many teams are learning the hard way.
AI without context doesn’t produce clarity. It produces confident noise.
In observability, that “noise” can mean wasted cycles, misdiagnoses, and a higher risk in production. In data, it can mean bad decisions at scale.
So the question isn’t “Can AI summarise an incident?” It’s:
Can AI reliably connect symptoms to the right causal chain across systems, data assets, transformations, and business semantics without hallucinating?
That’s where metadata becomes the differentiator.
If telemetry is the raw signal, metadata is the meaning. To move from "monitoring" to "understanding," you need active metadata to provide:
In short, observability becomes exponentially more valuable when it’s anchored to active metadata. Metadata that is continuously updated, queryable, and actionable in real time, not trapped in static documentation.
This is the key takeaway from the acquisition. The market is moving from monitoring systems to understanding systems. And understanding requires metadata.
What’s exciting about the Snowflake<>Observe pairing is the implied shift from reactive operations (alert → triage → fix) toward proactive systems (detect → explain → recommend → resolve).
But automated remediation only works when three things are true:
Without Governed Context and the Control Plane, “AI ops” can turn into “AI improvisation.” That’s not something businesses will trust in production.
At TimeXtender, we believe the future belongs to platforms that connect signal to meaning and meaning to action. Observability is most powerful when it’s integrated into the same foundation that delivers decision-grade (and AI-grade) data in the first place:
In other words, observability shouldn’t sit beside the data estate as a separate “tool.” It should be woven into the way data is integrated, prepared, governed, and served; because that’s where trust is either built or broken.
If this acquisition resonates, it’s worth pressure-testing your own strategy with a few practical questions:
The winners in the AI era won’t be the teams with the most alerts. They’ll be the teams with the fastest path from signal → context → decision → action.