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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Snowflake announced a definitive agreement to acquire Observe on January 8, 2026, then completed the transaction on February 2. The deal brings Observe’s AI-focused observability technology into Snowflake’s AI Data Cloud, with CEO Sridhar Ramaswamy positioning the combination as a way to manage logs, metrics and traces at enterprise scale.
The strategic rationale is clear, but the promised savings and faster incident resolution remain claims to validate against a customer’s workload, data-retention needs and existing tools.
What Snowflake actually acquired
Observe is an observability platform that ingests and correlates logs, metrics, distributed traces, application signals, infrastructure data and relevant business context. It was built on Snowflake, which gives the companies a closer technical relationship than a typical post-acquisition integration. TechCrunch described that foundation when reporting the transaction: Observe was built on Snowflake from its early development.
Snowflake initially disclosed no financial terms. Its later filings show both an approximately $650 million transaction value in the proxy and approximately $595.8 million in preliminary purchase consideration in accounting disclosures. Those figures are not contradictory prices for the same reporting purpose; they reflect different filing stages, adjustments and accounting treatment.
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| Milestone or figure | What it means |
|---|---|
| January 8, 2026 | Snowflake announced a signed agreement and intent to acquire Observe, subject to customary closing conditions. Snowflake announcement |
| February 2, 2026 | The acquisition closed, according to Snowflake’s SEC filing. SEC filing |
| Approximately $650 million | Consideration described in Snowflake’s proxy, subject to applicable adjustments. Proxy statement |
| Approximately $595.8 million | Preliminary purchase consideration reported for accounting purposes, primarily cash and Snowflake shares. SEC filing |
Why Snowflake sees observability as a data problem
Snowflake’s argument is that modern applications, AI agents and data pipelines generate too much operational telemetry for architectures built around heavy indexing, separate systems and short retention windows. Those designs can encourage sampling, fragmented context and repeated movement of data.
Snowflake wants telemetry treated as governed data alongside analytics and business information. Its acquisition announcement says Observe can correlate logs, metrics and traces, while the company’s post-close product update presents the offering as Observe by Snowflake, built on a telemetry lakehouse. The proposed advantage is the ability to retain and analyze more raw signals, then connect an incident with business impact rather than viewing an alert in isolation.
That is a strategy, not a universal price guarantee. Total cost depends on ingestion volume, storage duration, compute, query patterns, data movement, licensing and the architecture a customer already operates.
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What CEO Sridhar Ramaswamy says the deal enables
In the acquisition release, Ramaswamy said, “Reliability is no longer just an IT metric—it’s a business imperative.” The quotation appears in the official release.
On Snowflake’s February 25, 2026 earnings call, Ramaswamy characterized observability—particularly AI observability—as a market worth more than $50 billion. That is management’s estimate, not an independently established market measurement. He also argued that Observe fits naturally because it was built on Snowflake and that high-volume telemetry creates a significant cost challenge.
How Observe by Snowflake is supposed to work
AI SRE
The AI SRE is intended to detect abnormal behavior, correlate signals, investigate incidents, suggest likely causes and help teams move toward remediation. Snowflake says some issues can be resolved up to 10 times faster; that is a vendor claim, not an independently verified benchmark.
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Observability context graph
The context graph is designed to connect services, infrastructure, logs, metrics, traces and related entities. In principle, this lets an engineer investigate a service failure in system context instead of chasing disconnected alerts.
Telemetry lakehouse
Snowflake describes storage and compute in its platform as the foundation, with OpenTelemetry and Apache Iceberg positioned as open standards. OpenTelemetry can improve collection and portability, but it does not remove switching costs created by proprietary schemas, query engines, dashboards, context graphs, AI workflows or commercial contracts.
Snowflake’s May 5 update gives the post-close product positioning: Observe by Snowflake combines an AI SRE, context graph and telemetry-lakehouse foundation.
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What “enterprise-wide observability” means in practice
Rather than treating the phrase as a measured outcome, it is more useful to define the operating model Snowflake is targeting:
- Collecting telemetry across many accounts, regions, applications and environments.
- Keeping more high-fidelity logs, metrics and traces where economics and policy allow.
- Correlating application, infrastructure, data-pipeline and AI-agent behavior.
- Applying common governance, access controls and retention rules.
- Connecting incidents with outcomes such as conversion, churn or API performance.
- Supporting both human SRE teams and controlled automated operations.
Snowflake’s full-stack observability material gives examples such as relating application latency to conversion rates or API behavior to customer churn: Snowflake full-stack observability overview. Enterprise-wide does not mean every source is onboarded automatically or that existing monitoring products become unnecessary.
What customers could gain—and what they must test
Potential benefits
- Less duplicate movement when telemetry and business data already reside in Snowflake.
- Centralized governance and access controls.
- Longer retention of raw telemetry when storage and compute costs are acceptable.
- AI-assisted investigation and root-cause analysis.
- Native proximity to Snowflake, data applications and AI workloads.
- OpenTelemetry-based collection and routes to other backends.
Questions that can change the business case
- Data location: Is most telemetry already in Snowflake, or would Snowflake become a second copy?
- Economics: Model ingestion, storage, compute, egress, retention and existing-agent costs rather than assuming a lower bill.
- Coverage: Verify support for logs, metrics, traces, profiles, real-user and synthetic monitoring, databases, security data, AI-agent traces and data-pipeline signals.
- Workflow: Test integrations with PagerDuty, Slack or Teams, Jira, ServiceNow, runbooks and escalation systems.
- AI reliability: Require inspectable evidence, handling for incomplete telemetry, audit trails and policy controls before allowing automated remediation.
- Governance: Check masking, encryption, role-based access, audit logs, residency, deletion and cross-account controls.
- Portability: Confirm access to raw telemetry, OpenTelemetry export, Iceberg compatibility and practical retrieval rights after termination.
Snowflake documentation explicitly describes sending Native App telemetry to Datadog, Grafana, Elastic and Splunk, so coexistence is a supported possibility rather than an immediate forced replacement: Snowflake third-party observability integrations.
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Snowflake is not simply copying a dashboard product. Its proposed distinction is governed telemetry storage connected to business data and AI analysis.
| Platform | Typical strategic emphasis | How Snowflake differs |
|---|---|---|
| Datadog | Broad operational SaaS covering infrastructure, applications, logs, traces and security. | Snowflake emphasizes data-cloud storage, governance and cross-domain analysis. Datadog platform |
| Dynatrace | Enterprise monitoring, automation and business observability. | Snowflake leads with a lakehouse and AI Data Cloud foundation. Dynatrace platform |
| New Relic | Developer-oriented application and full-stack monitoring. | Snowflake targets broader telemetry consolidation and data economics. New Relic platform |
| Grafana Cloud | Open-source-aligned metrics, logs, traces, dashboards and OpenTelemetry. | Snowflake offers tighter governance and Snowflake workload proximity, but remains more platform-dependent. Grafana Cloud |
| Elastic | Search-driven logs, metrics, traces and security analytics. | Snowflake centers analytics and governed lakehouse data rather than the Elastic Stack. Elastic Observability |
| Splunk | Machine-data analytics, security operations and established enterprise deployments. | Snowflake is entering through its data and AI platform. Splunk Observability |
Risks behind the “built on Snowflake” advantage
- A lakehouse can be attractive for massive retention but unnecessarily complex for moderate telemetry volumes.
- Snowflake consumption costs can rise with high-volume ingestion and analysis.
- Keeping more telemetry may expose personal information, tokens, customer identifiers, source fragments or AI prompts.
- AI recommendations still require human review, rollback procedures and evidence.
- Open standards do not guarantee portability across proprietary workflows.
- Integration may be technically straightforward while packaging, billing, support and migration remain difficult.
Snowflake’s proxy also disclosed relationships involving former Snowflake director Jeremy Burton, who was Observe’s CEO and held an equity interest, and Snowflake director Michael Speiser, who had a relationship with Observe and an indirect equity interest. The filing provides governance context; it does not by itself establish wrongdoing.
Bottom line for buyers and investors
The Observe acquisition gives Snowflake a credible route into observability because the product was designed on Snowflake and can connect telemetry with governed business and AI data. The deal closed on February 2, 2026, and the product is now presented as Observe by Snowflake.
Whether it becomes a compelling alternative to established observability suites will depend on measurable economics, signal coverage, dependable AI assistance, open data access and compatibility with customers’ existing incident workflows. Snowflake’s claims about lower cost and faster troubleshooting should be treated as hypotheses to validate—not outcomes guaranteed by the acquisition.
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