Snowflake integrates Observe telemetry

Observability is the software that tells you what your software is doing — logs, metrics, traces, all the noisy evidence left behind when systems behave or break. Snowflake is trying to make that evidence live in the same place as the rest of a company’s data. That is the point of Observe by Snowflake, the company’s new observability product built from its acquisition of Observe and now positioned as a native part of the AI Data Cloud. The bet is simple: if AI systems are generating more operational noise than humans can parse, the observability stack has to become more like a data platform. ### What actually launched? Snowflake announced its intent to acquire Observe on January 8, 2026, then closed the deal in February. Now it is rolling the product out as “Observe by Snowflake” — a first-party observability offering that sits directly on Snowflake and is aimed at production data systems, apps, and AI workloads. ### Why does Snowflake care about telemetry? Because observability is really a data problem. Every service emits logs, counters, events, and traces. AI agents make that worse — more calls, more handoffs, more retries, more weird failure modes. Snowflake’s pitch is that telemetry should not be sampled down and thrown into a separate expensive silo. It should be stored at full fidelity and queried like any other large dataset. (snowflake.com) ### What did Observe bring to the table? Observe was already built on Snowflake from the start, which made the integration unusually straightforward. Its core value was not just storing telemetry, but correlating it — connecting traces to logs to infrastructure state to application context so engineers can move from “something is broken” to “here is the dependency that failed.” Snowflake is keeping that architecture and wrapping it in its own platform story. (snowflake.com) ### What is the new architecture? Snowflake describes three main pieces. There is a Telemetry Lakehouse Foundation for ingesting and retaining large telemetry volumes. There is an Observability Context Graph for relating components and events across systems. And there is an AI SRE layer that helps investigate incidents and automate troubleshooting steps. Underneath, Snowflake is leaning on Apache Iceberg and OpenTelemetry as the open-standard backbone. (snowflake.com) ### Why is “keep 100% of telemetry” a big deal? Because older observability tools often force teams into ugly tradeoffs. Either keep everything and pay a fortune, or sample aggressively and lose the exact clue you need during an outage. Snowflake is arguing that lakehouse economics change that math. If telemetry lands in cheap, scalable storage and can still be queried fast, then “telemetry debt” becomes a searchable asset instead of discarded exhaust. (snowflake.com) ### Why does this matter for AI agents? Agent systems fail in messier ways than normal apps. One model call can trigger tools, databases, APIs, and other agents. When something goes wrong, the root cause may sit in a prompt path, a routing decision, a timeout, or a downstream data issue. Snowflake is explicitly framing Observe by Snowflake as infrastructure for those next-generation AI applications, where telemetry volume and cross-system debugging both spike. (snowflake.com) ### Is this just monitoring for Snowflake itself? No — that existed already in narrower form. Snowflake has event tables for logs, metrics, and traces, plus internal observability features for apps and workloads. The new move is broader. It turns those telemetry primitives into a full product path, with cross-account centralization, richer investigation workflows, and an AI-assisted interface layered on top. (snowflake.com) ### What’s the real strategic angle? Snowflake wants to own more of the operational stack around data and AI, not just the warehouse underneath it. Observability pushes Snowflake into a large IT operations market and gives customers one more reason to keep operational data, business data, and AI workflows on the same platform. Basically, the company is saying the future control plane for AI is not just storage or compute — it is visibility. (docs.snowflake.com) The bottom line is that Snowflake is turning Observe from a close partner into a native product and using observability as a wedge into AI operations. If the integration works, engineers get one place to store telemetry, investigate failures, and connect incidents back to the data systems that caused them. (snowflake.com 1) (snowflake.com 2)