Snowflake announced an agreement to acquire Observe, which specializes in AI-powered observability, positioning the combined entity to deliver next-generation observability capabilities built on open standards and designed for the scale, complexity, and economics required by modern AI-driven enterprises. The acquisition expands Snowflake’s capabilities in the $51.7 billion IT operations management software market, which grew 9% in 2024, by enabling enterprises to ingest and retain 100% of their telemetry data at substantially lower cost than traditional observability platforms.
Observe’s platform will integrate directly into Snowflake’s AI Data Cloud, allowing organizations to treat telemetry data as first-class data assets subject to the same governance, analytics, and AI capabilities applied to business data. By combining Observe’s AI Site Reliability Engineer with Snowflake’s high-fidelity data foundation, teams can move from reactive monitoring to proactive, automated troubleshooting, resolving production issues up to 10 times faster.
How AI-Powered Observability Changes Enterprise Operations
For technology executives managing complex AI applications and distributed systems, the integration of observability directly into data platforms fundamentally transforms how organizations monitor, troubleshoot, and optimize production environments. Traditional observability solutions force enterprises to choose between comprehensive telemetry coverage and acceptable costs, leading to data sampling and short retention windows that eliminate visibility into intermittent issues and long-term trends.
Observe addresses this by building its platform natively on Snowflake from inception, leveraging scalable object storage, elastic compute, and Apache Iceberg table formats to manage massive telemetry volumes at dramatically lower total cost of ownership. The architecture structures logs, metrics, and traces as entities with semantic relationships within a context graph, enabling drill-and-pivot exploration across signals without the data silos that characterize traditional observability stacks. Real-time ingest pipelines filter and enrich signals using OpenTelemetry data collection to avoid vendor lock-in, while incremental views and token indexes optimize query performance.
The AI SRE capability represents a shift from dashboard consumption to automated understanding and response, where models reason across correlated telemetry to surface root causes rather than symptoms. This positions observability as a first-class workload where AI agents detect anomalies earlier, identify root causes faster, and in some cases automatically remediate issues without human intervention, improving operational resilience as systems grow more distributed, dynamic and autonomous.
When evaluating observability platforms for enterprise AI and data environments, technology executives should prioritize solutions demonstrating native integration with existing data platforms to avoid creating new data silos and duplicative infrastructure. The platform must support open standards including OpenTelemetry for telemetry collection and Apache Iceberg for data storage, ensuring interoperability and avoiding vendor lock-in as telemetry volumes scale. AI-assisted troubleshooting capabilities should correlate across logs, metrics, and traces to identify root causes rather than merely aggregating symptoms into dashboards requiring manual interpretation.
What This Means for ERP Insiders
Observability becomes native data platform capability. Snowflake’s acquisition of Observe signals that leading data platform providers recognize observability as core functionality rather than a separate market addressed through partnerships or integrations. This architectural shift treats telemetry data with the same governance, retention and analytical capabilities as transactional and analytical data, fundamentally challenging vendors whose business models depend on proprietary observability infrastructure.
AI-powered SRE shifts operations to proactive automated remediation. Observe’s AI Site Reliability Engineer capability demonstrates that AI agents can correlate across logs, metrics and traces to identify root causes and in some cases automatically remediate production issues, fundamentally changing how enterprises staff and operate production support. This development pressures ERP and enterprise application vendors to embed similar AI-assisted troubleshooting capabilities or risk customers perceiving their platforms as requiring excessive manual intervention compared to competitors offering autonomous operations.
Open standards architecture enables interoperability, but reduces switching costs and vendor differentiation. Snowflake’s emphasis on Apache Iceberg and OpenTelemetry reflects broader industry movement toward open data formats that enable customers to avoid vendor lock-in while accessing best-of-breed capabilities across multiple platforms. This openness benefits customers through flexibility and reduced migration risk but challenges vendors whose competitive moats depend on proprietary data formats and closed ecosystems.
