Snowflake acquires Observe for approximately $1 billion against $450+ million in total funding, marking its second observability acquisition following TruEra in May 2024:

• Acquisition targets the $51.7 billion IT operations management software market by integrating observability directly into Snowflake’s AI Data Cloud platform

• Strongest differentiation exists in log management displacement targeting Splunk and Elastic customers already using Snowflake for business data

• Observe provides correlation-based AI SRE for anomaly detection but lacks the deterministic, causation-based root cause analysis of mature observability platforms

• Acknowledged gaps include real user monitoring, synthetics, session replay, mobile application monitoring, code-level profiling, and application security capabilities

• Acquisition raises questions about Snowflake’s relationship with existing observability partners who have built solutions on its data platform

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The News

Snowflake recently announced a definitive agreement to acquire Observe, an AI-powered observability platform built on Snowflake’s infrastructure. Valued at approximately $1 billion, this is Snowflake’s second observability-related acquisition, after TruEra in May 2024.

These acquisitions challenge the traditional separation between observability infrastructure and data platforms. By treating telemetry data (logs, metrics, traces) as first-class data within Snowflake rather than requiring specialized observability infrastructure, the combined offering promises to reduce observability costs while enabling full-fidelity data retention.

However, this raises important questions about Snowflake’s suitability for real-time analytics, Observe’s enterprise maturity after seven years, and the impact on Snowflake’s observability partner ecosystem.

Who is Observe?

Observe entered the observability market in 2017, differentiating itself by building its platform entirely on Snowflake’s data infrastructure rather than developing proprietary storage and compute systems. This allows Observe to leverage Snowflake’s lakehouse economics for telemetry storage and focus development on the observability application layer.

The company has raised over $450 million from investors such as Frank Slootman, Snowflake Ventures, Michael Dell, and Capital One. CEO Jeremy Burton, formerly EMC’s CMO, maintains strong relationships with both Dell and Slootman.

Several of Observe’s marquee customers appear connected through these executive relationships and shared Snowflake deployments.

Despite significant funding and market presence since 2017, Observe has not matched the enterprise traction of established observability leaders such as Dynatrace, Datadog, or New Relic.

Recently, the company has focused its go-to-market strategy on displacing Splunk and Elastic in log management, especially among existing Snowflake customers.

This shift indicates Observe has been more successful competing against legacy log management solutions than in full-stack observability, where it lacks several enterprise-grade features such as real-user monitoring, synthetic monitoring, session replay, mobile application monitoring, code-level profiling, and application security.

Observe’s technical foundation relies on a unified context graph that correlates logs, metrics, and traces within Snowflake’s data platform. The company has developed what it calls an “AI SRE” (Site Reliability Engineer) that performs correlation-based anomaly detection and root cause suggestions.

However, this correlation-based approach differs fundamentally from the deterministic, causation-based root cause analysis provided by mature observability platforms that maintain real-time dependency graphs.

Strategic Fit & Rationale

Snowflake’s acquisition of Observe addresses key strategic goals but also raises new questions about the company’s platform evolution.

Economics & Data Gravity

The primary rationale centers on economics and data gravity. Traditional observability platforms require separate, specialized infrastructure for telemetry data, with different storage, compute, and retention economics than business data platforms.

This separation forces organizations to use sampling and short retention windows to control costs, which Snowflake argues creates blind spots in production systems.

Consolidating observability data within Snowflake’s lakehouse could allow customers to retain complete telemetry data at lower cost while applying consistent governance, analytics, and AI across operational and business data.

This convergence aligns with broader market trends, as modern data platforms with lakehouse economics and AI increasingly absorb observability workloads. However, this does not guarantee superior real-time operational capabilities, which remain essential for production observability.

Increased Share-of-Wallet

The acquisition also gives Snowflake a tactical entry point against established log management vendors. Observe has developed targeted messaging for Splunk and Elastic customers, positioning Snowflake-based log retention as a more economical alternative.

With significant overlap between Snowflake’s customers and organizations seeking to leave costly legacy logging platforms, this creates immediate cross-sell opportunities without requiring Snowflake to build observability capabilities from the ground up.

Unified Observability Story

From a product portfolio perspective, the acquisition complements Snowflake’s May 2024 purchase of TruEra, which focuses on AI observability for large language models and machine learning pipelines.

Together, these acquisitions enable Snowflake to offer unified observability for both traditional applications (via Observe) and AI/ML workloads (via TruEra) within a single data platform. This is increasingly important as organizations deploy AI agents and autonomous systems that blur the distinction between data applications and operational workloads.

Snowflake claims that combining Observe’s AI SRE with its data fidelity enables production issue resolution up to 10 times faster than reactive monitoring. However, enterprises with mature observability platforms using AI-driven root-cause analysis are unlikely to see this level of improvement.

Impact to IT Practitioners

For IT practitioners, the acquisition creates a mixed picture of potential benefits offset by implementation challenges and open architectural questions.

Improved Cost Through Consolidation

The primary operational benefit is improved data consolidation and retention economics. Enterprises running separate observability platforms alongside Snowflake face duplicated infrastructure costs, greater governance complexity, and analytical silos.

Consolidating telemetry within Snowflake provides unified governance, consistent security controls, and the ability to correlate operational events with business context without moving data.

This primarily benefits organizations already heavily invested in Snowflake that prefer to extend their existing data platform instead of adding specialized systems.

Improved Telemetry Capture

Snowflake claims organizations can retain “100%” of telemetry data rather than sampling, addressing a significant challenge. Modern distributed applications generate large telemetry volumes, and cost pressures often force organizations to sample metrics and traces, retaining only recent log data. This creates blind spots when investigating intermittent issues or conducting retrospective analysis.

If Snowflake’s lakehouse economics truly enable full-fidelity retention at acceptable cost, this offers significant value for complex troubleshooting and compliance requirements.

Question: Architectural Suitability

Notably, Snowflake’s OLAP architecture was not designed for real-time operational analytics. Observability platforms must process telemetry streams, detect anomalies, correlate events, and trigger alerts or automated responses within seconds. Even minor latency in detection or correlation can extend outage duration and increase business impact.

Practitioners should validate whether Snowflake can consistently deliver sub-second query performance at scale, especially for high-cardinality metrics and distributed tracing workloads.

Question: Capability Gaps

Enterprises must also address gaps in observability capabilities. For example, Observe lacks real user monitoring, synthetic monitoring, session replay, mobile application monitoring, code-level profiling, and application security monitoring.

These features are standard in mature observability platforms and are critical for comprehensive production visibility. Practitioners using Observe would need supplementary tools to address these gaps, which undermines the consolidation value.

Question: Cost Model

Cost modeling requires careful consideration. While Snowflake’s storage economics may be more favorable than some specialized observability platforms, practitioners must account for compute costs from continuous queries, especially for real-time dashboards and automated alerting.d pricing means that intensive analytical workloads against high-volume telemetry data can generate substantial compute charges.

Potential customers should conduct proof-of-concept deployments with representative data volumes and query patterns before committing to a full migration.

Limitation: Reliance on Correlation-based AI

Relying on correlation-based AI for root cause analysis is another practical limitation. Observe’s AI SRE uses statistical correlation to suggest potential root causes from telemetry data patterns.

While correlation can highlight suspicious coincidences, it does not establish causation and results in higher false-positive rates than deterministic methods.

Mature observability platforms (such as Dynatrace and Datadog) maintain real-time dependency graphs that map actual causal relationships among services, infrastructure, and user interactions. When issues occur, these platforms can definitively trace impact through dependency chains rather than simply suggesting correlations.

IT practitioners should recognize that correlation-based approaches require more manual validation and expertise to distinguish signal from noise.

Limitation: Manual Instrumentation

Manual instrumentation can add operational friction. Although Observe promotes simplified deployment, its users report that achieving production-grade observability often requires significant manual configuration, custom instrumentation, and ongoing tuning.

Mature observability vendors provide automatic discovery of application dependencies, automatic instrumentation of common frameworks and languages, and automatic baselining of normal behavior.

Organizations adopting Observe should allocate engineering time and expertise to build and maintain observability practices, rather than expecting a turnkey deployment.

Analysis

With the Observe acquisition, Snowflake treats observability data as a core data platform workload rather than relying on specialized infrastructure. The strategy is sound: removing the separation between telemetry and business data enables unified governance, correlated analysis, and improved economics through lakehouse storage. For enterprises heavily invested in Snowflake seeking to consolidate log management and extend retention, this acquisition delivers tangible value.

Competitively, Snowflake enters observability leveraging customer relationships and data platform strength, rather than observability maturity or technical depth.

Snowflake’s main advantage in this market is its installed base and data gravity. Organizations already using Snowflake for analytics, data warehousing, and AI/ML can more easily add observability capabilities than deploy separate specialized systems.

Sales efficiency and procurement simplification also support platform consolidation when requirements are met. Snowflake can leverage existing customer relationships, contracts, and platform investments to drive observability adoption without new vendor relationships or infrastructure.

Snowflake’s pricing model offers a competitive advantage in certain scenarios, as enterprises with large-scale, petabyte-range telemetry retention needs may face prohibitive costs on traditional observability platforms. Snowflake’s object storage and consumption-based pricing can deliver substantial savings for customers focused on long-term retention and periodic analysis. This creates opportunities in cost-sensitive segments and use cases where analytical depth is prioritized over operational speed.

However, Snowflake faces significant competitive challenges from established observability leaders who have spent years refining their platforms for production workloads.

Dynatrace, Datadog, New Relic, and Cisco‘s Splunk Observability all offer mature capabilities across the full observability stack with proven performance at enterprise scale. These vendors provide automatic instrumentation requiring minimal manual configuration, real-time dependency mapping that establishes causal relationships rather than statistical correlations, comprehensive coverage including real user monitoring and synthetics, and deterministic root cause analysis that precisely identifies issues without requiring extensive expertise to interpret results.

Purpose-built observability platforms retain a significant advantage. They are designed for high-velocity telemetry ingestion, real-time stream processing, and sub-second queries across billions of time-series data points, using specialized data structures, indexing, and query optimizers tailored for observability workloads.

Snowflake’s OLAP foundation is optimized for analytical scan queries rather than operational point lookups and real-time aggregations. Addressing this architectural mismatch will be a primary technical challenge for Snowflake.

The acquisition also introduces uncertainty for Snowflake’s observability partners. By offering observability as a first-party solution, Snowflake signals it views this market as a platform opportunity rather than a partner domain. Vendors with Snowflake integrations and joint customers must now navigate a partner-competitor dynamic.

IT organizations with investments in both Snowflake and third-party observability platforms should monitor how Snowflake balances its first-party observability offering against continued partnership and integration support.

Despite these uncertainties, the acquisition benefits the market by validating the convergence of observability and data platforms and introducing competitive pressure that should drive innovation and improved economics.

Enterprises gain another option for consolidating operational and business data, especially those already committed to Snowflake who prioritize platform simplification over specialized capabilities.

Competitive Analysis & Advice to IT Buyers

While the acquisition positions Snowflake to compete across specific observability market segments, such as log management and analytics, the company faces significant differentiation challenges in full-stack observability against established platforms…

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