Analyst(s): Mitch Ashley
Publication Date: January 9, 2026
Snowflake has announced its intent to acquire Observe to expand AI-powered observability capabilities within its data platform. The move positions observability as a native data and AI function rather than a standalone tool. The acquisition signals an important shift in the AI observability market for 2026.
What is Covered in this Article:
- How Snowflake’s acquisition of Observe reframes observability as a core data platform capability rather than a standalone tool.
- Why AI-driven systems are forcing a shift in observability economics, architecture, and competitive differentiation.
- What the deal signals for observability vendors, platform providers, cloud service providers, and enterprise buyers heading into 2026.
The News: Snowflake announced on January 8, 2026, its intent to acquire Observe to deliver AI-powered observability at enterprise scale. Observe, which has been built natively on Snowflake since its inception, provides a cloud-native observability platform for analyzing logs, metrics, and traces using full-fidelity data stored in Snowflake.
Snowflake stated that the acquisition will allow customers to unify observability data with their existing analytical and operational data, apply AI and machine learning to accelerate root-cause analysis, and reduce the cost and complexity of maintaining separate observability stacks across functions and platforms. Financial terms were not formally disclosed, though the transaction is expected to close subject to customary regulatory approvals and closing conditions.
5 Reasons Snowflake Acquiring Observe Sets the Tone For 2026
Analyst Take: The 2026 observability market is positioned for major acceleration and M&A events. AI-driven systems are significantly increasing telemetry volume, operational ambiguity, and scrutiny of production-readiness AI systems. Vendors are confronting their own challenges: observability is no longer a competition based solely on features.
AIOps, ITOps, SecOps, development, and FinOps are repositioning observability as platform infrastructure. Snowflake’s intent to acquire Observe is a signal to the market that observability is a strategic competitive advantage, and that vendors without a defensible data and AI observability strategy will be pressured as platform players redraw the competitive boundaries for production-grade AI.
Five reasons Snowflake’s acquisition of Observe sets the tone for observability in 2026
1. Observability is central to the AI platform era.
This acquisition moves Snowflake directly into the observability and IT operations market, placing Snowflake in direct competition with established observability vendors and cloud-native offerings. Observability is now a strategic priority for Snowflake’s AI platform strategy.
The long-standing relationship between the companies, including Snowflake Ventures’ investment and Observe CEO Jeremy Burton serving on Snowflake’s board, underscores that this reflects a deliberate platform direction, not opportunistic expansion.
2. Observability as a native data platform layer.
Observe was built from its beginnings on Snowflake, which allows Snowflake to internalize observability as a core platform function rather than an attached tool. Telemetry no longer needs to be exported into a separate backend with its own storage, pricing, and governance model.
In 2026, this reinforces a clear market direction: observability is moving into the data platform layer, where analytical, operational, and business data already converge.
3. AI-powered SRE shifts observability from visibility to operational control.
Observe’s AI-driven SRE capability reframes observability from dashboard consumption to automated understanding and response. Snowflake is positioning this as a first-class workload where models reason across logs, metrics, and traces to surface cause, not just symptoms.
This reflects a broader reality entering 2026: AI production systems demand observability that explains behavior and accelerates action, not more data, events, and charts.
4. Telemetry is treated as a first-class data domain.
Snowflake’s framing of observability as a data problem elevates telemetry from ephemeral exhaust to durable, governed data. Logs, metrics, and traces become queryable assets with long retention, full fidelity, and direct access for analytics and AI.
This directly challenges the traditional observability economics built around sampling and short retention times, which break down as AI systems dramatically increase the volume of signals.
5. Open standards at AI scale.
The combined architecture emphasizes Apache Iceberg and OpenTelemetry, aligning observability with Snowflake’s open data strategy. This matters because AI agents and modern data applications emit telemetry at volumes that cannot tolerate proprietary ingestion paths or closed schemas.
In 2026, open formats and shared governance are prerequisites for observability that spans teams, clouds, and workloads.
Competitive impact for 2026
Snowflake’s acquisition of Observe does not immediately out-feature incumbent observability vendors. It changes the basis of competition. Vendors that control the data plane and AI execution environment gain leverage, while vendors anchored in proprietary or third-party backends face growing pressure to explain why observability data should live outside the enterprise data platform.
For observability vendors and potential acquirers in 2026, the question is no longer who has the best dashboards or observability apps, but who owns the data platform that creates long-term strategic value from observability data.
Pure-play observability vendors such as Datadog and New Relic are pressured by a model in which telemetry storage, analytics, and AI reasoning converge on a single data platform. Proprietary backends and duplicated data planes become increasingly difficult to justify as enterprises reassess costs, governance, and architectural sprawl.
Hyperscaler-native observability tools face pressure from Snowflake’s cross-cloud positioning and its alignment with open standards. As enterprises standardize observability across hybrid and multi-cloud environments, platform-neutral telemetry strategies are gaining traction, positioned against cloud-specific tooling.
What to Watch:
- How Snowflake positions Observe in the market, specifically whether it is framed as a standalone observability platform or as a native capability that redefines observability as part of the data and AI stack.
- How incumbent observability vendors respond in 2026, including shifts in messaging toward open data, long-term telemetry economics, partnerships with data platforms, or defensive platform narratives.
- How Snowflake leverages its enterprise footprint, cloud provider relationships, and partner ecosystem to accelerate observability adoption as an extension of existing data, governance, and AI deployments rather than a net-new buying decision.
See the complete press release about Snowflake’s intent to acquire Observe on Snowflake’s website.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer's world of software engineering, cybersecurity, DevOps, cloud, and AI.
