Analyst(s): Brad Shimmin
Publication Date: January 26, 2026
Snowflake has signed a definitive agreement to acquire Observe to inject AI-powered observability directly into the Snowflake AI Data Cloud. The move promises full-fidelity telemetry retention, faster incident resolution, built on an open-standard architecture based on Apache Iceberg and OpenTelemetry.
What is Covered in this Article:
- Snowflake’s intent to acquire Observe
- Observability as a native data platform layer
- AI Site Reliability Engineering (SRE) shifting from visibility to control
- Open standards changing interoperability dynamics
- Competitive and economic implications for vendors
The News: Snowflake announced on January 8, 2026, that it signed a definitive agreement to acquire Observe, a provider of AI-powered software observability. The company intends to integrate Observe’s platform natively into Snowflake’s AI Data Cloud to help enterprises ingest, retain, and analyze telemetry alongside business data. The combined approach emphasizes open standards, including Apache Iceberg and OpenTelemetry. By positioning telemetry as governed, queryable data, Snowflake aims to move teams from reactive monitoring to proactive, automated troubleshooting using Observe’s AI Site Reliability Engineer features.
“By bringing Observe’s capabilities directly into the Snowflake AI Data Cloud, we are empowering our customers to manage enterprise-wide observability across terabytes to petabytes of telemetry with an open, scalable architecture and AI-powered troubleshooting workflows,” said Sridhar Ramaswamy, CEO of Snowflake. The transaction is expected to close following regulatory approvals and customary closing conditions.
Snowflake Acquires Observe: Operationalizing the Data Cloud
Analyst Take: Snowflake’s planned acquisition of Observe pushes observability into the data platform layer, changing how enterprises think about telemetry retention, analytics, and AI-assisted incident response. Rather than routing logs, metrics, and traces into proprietary backends for analysis after the fact, Snowflake’s strategy brings telemetry into the same storage, compute, and governance plane as enterprise data. This directly challenges cost structures that have traditionally forced sampling and short retention windows. It also strengthens Snowflake’s position in operations for emerging Agentic software, where pressures on troubleshooting and explainability are rising. The result is a competitive reset in which AI-powered observability becomes a core data workload instead of a discrete tool category.
Observability Becomes a Data Platform Layer
Bringing Observe into Snowflake collapses observability storage and analytics into the core data plane. Treating logs, metrics, and traces as governed tables shifts deliberation from capacity limits to query design and policy. This alignment enables cross-domain joins between telemetry and business data without duplicative pipelines or self-imposed restrictions on the amount of data that can be collected and analyzed. It also changes buyer expectations around retention, since the same object storage and elastic compute can serve both workloads. Vendors that depend on separate backends may find their value propositions reframed as add-ons rather than foundations. The implication is that companies will increasingly evaluate observability as a first-class data workload rather than a standalone stack.
AI SRE Pushes From Visibility to Control
Observe’s AI Site Reliability Engineer centers reasoning over correlated signals instead of manual dashboard triage. By operating on a unified context graph, the system can highlight causal chains across services and infrastructure, greatly reducing the time it takes to identify and resolve issues. This approach favors proactive detection, faster isolation, and targeted remediation in complex, distributed environments. As AI agents and data apps proliferate, incident patterns become less deterministic, raising the premium on explainability. Embedding these capabilities where telemetry and enterprise data co-reside positions Snowflake to present a shared operational picture to SRE, data, and app teams. The takeaway is that control-loop automation becomes a platform attribute rather than a feature of a discrete monitoring tool.
Further, we are seeing a shift where observability isn’t just for human SREs anymore; it’s rapidly becoming the feedback loop for autonomous agents. If an agent breaks, it needs telemetry to self-heal. Snowflake owning the data and observability layers puts them in the driver’s seat for the emerging “Agentic OS.”
Open Standards Reshape Vendor Lock-In
The emphasis on Apache Iceberg and OpenTelemetry anchors observability in open table formats and collection frameworks. Standardized schemas reduce ingestion friction and enable customers to preserve optionality across vendors and clouds. Open formats make it easier to apply the same governance, lineage, and security controls already used for analytic data. They also streamline interoperability for AI agents that must traverse multiple services and deployment targets. While this clarity benefits buyers, it intensifies competitive pressure on products tied to proprietary data paths. The conclusion is that the open standards that currently make up open data lakehouse architectures are fast becoming a prerequisite for observability at AI scale.
Competitive and Economic Implications
Integrating observability into the data platform challenges pricing models that treat telemetry as specialized, premium data. Positioning telemetry alongside existing lakehouse economics promises fewer trade-offs between fidelity and cost. The move pushes incumbents to justify external data planes as enterprises consolidate pipelines and governance. Pairing system observability with model evaluation assets from prior acquisitions widens the Snowflake platform surface area. If successful, Snowflake can compete as a cross-functional control layer spanning data pipelines, models, and runtime infrastructure. The implication is that the basis of competition tilts toward data gravity and platform leverage over point-feature breadth.
What to Watch:
- Speed and depth of product integration and branding post-close. Since Observe was architected natively on Snowflake, technical debt should be minimal compared to typical acquisitions. The litmus test for success will be how quickly Observe’s interface becomes a native “tab” within Snowsight rather than a separate console.
- Customer adoption patterns across existing Observe and Snowflake accounts. It is unlikely enterprises will immediately rip out entrenched APM tools for traditional apps. Instead, watch for adoption in “greenfield” AI deployments—where Large Language Model (LLM) traces, vector store metrics, and GPU utilization logs are generating massive, cost-prohibitive data volumes.
- Competitive pricing and packaging responses from observability incumbents. Expect traditional observability vendors to react defensively. We will likely see a bifurcation in the market: Incumbents will scramble to move up-stack, emphasizing “business observability” and specialized APM agents that are hard to replicate, while downplaying raw log storage.
- Evidence that open standards reduce migration friction at scale. The promise is that OTel agents and Iceberg tables make migration seamless. The reality is often messier. Watch for case studies proving that an enterprise can actually repoint its telemetry firehose from a proprietary backend to Snowflake without breaking its operational dashboards.
- Outcomes from combining system and model observability in practice. With the previous acquisition of TruEra (2024) and now Observe, Snowflake can theoretically link a drop in model accuracy (TruEra) directly to a spike in system latency or a specific infrastructure failure (Observe). Watch for product demos that visualize this causal chain.
See the full press release on Snowflake’s news announcement on Snowflake’s website.
Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.
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.
Other insights from Futurum:
5 Reasons Snowflake Acquiring Observe Sets the Tone For 2026 – Futurum
SAP and Snowflake Redefine Enterprise Data for AI: Is Your ETL Strategy Already Obsolete?
Snowflake Summit ’25: Accelerating AI with Unified Data & Compute
Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
