Observe by Snowflake: AI-Powered Observability at Scale for the Data Cloud

Why It Matters

The integration of AI‑powered, programmatic observability with open‑format storage reduces operational costs and accelerates mean‑time‑to‑repair, giving enterprises a competitive edge in reliability engineering.

Key Takeaways

• Observe CLI offers programmatic, agent‑friendly access to telemetry
• Iceberg support lets teams store observability data in cheap object storage
• Context Graph links logs, metrics, traces, and business data for accurate insights
• Snowflake credits can be used for Observe, simplifying cost management

Pulse Analysis

The launch of Observe’s AI‑powered CLI marks a shift from traditional UI‑centric monitoring to a developer‑first, programmable model. By exposing reusable skills through a command‑line interface, engineers can embed observability directly into CI/CD pipelines, IDEs, or AI agents such as Claude Code and ChatGPT. This approach not only streamlines routine investigations but also enables complex, context‑aware troubleshooting without leaving the coding environment, a capability increasingly demanded by modern DevOps teams.

A key differentiator is the platform’s native support for Apache Iceberg tables. Observability data can now be written to Iceberg‑formatted lakehouse storage, allowing organizations to keep telemetry alongside other business datasets in low‑cost cloud object stores. This eliminates costly data duplication, reduces vendor lock‑in, and lets analysts query logs, metrics, and traces with the same performance as native Observe storage. The move aligns with the broader industry trend toward unified data architectures that treat telemetry as a first‑class citizen.

Underlying these innovations is Snowflake’s Telemetry Lakehouse Foundation, which separates compute from storage and leverages Snowflake’s scalability and security. Coupled with the Observability Context Graph, the system delivers high‑precision, low‑latency insights that power AI‑driven Site Reliability Engineering (SRE) agents. For enterprises, this translates into faster mean‑time‑to‑repair, lower storage spend, and the ability to build custom, agent‑centric workflows that scale with petabyte‑level telemetry volumes. The synergy between AI, open data formats, and Snowflake’s credit model positions Observe as a strategic asset for any organization seeking cost‑effective, scalable observability.