Snowflake Acquires Observe for $1 Billion to Embed AI-Powered Observability Into Data Cloud

Snowflake Acquires Observe to Strengthen AI-Powered Observability

Snowflake acquires Observe in a strategic move to embed AI-powered observability directly into its platform, addressing the growing complexity of managing AI agents and data applications at scale. CEO Sridhar Ramaswamy confirmed the acquisition, stating that "reliability is no longer just an IT metric—it's a business imperative" as enterprises build increasingly complex AI systems 4

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. While financial terms were not officially disclosed, The Information reported the deal values Observe at approximately $1 billion, roughly two and a half times the funding the nine-year-old startup raised from investors 2

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The acquisition targets the rapidly expanding IT operations management software market, estimated at $51.7 billion according to Gartner 3

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. By integrating Observe's capabilities, Snowflake positions itself to capture a larger share of this market while addressing a critical pain point for enterprises deploying AI at scale.

Tackling the Observability Challenge in AI Operations

Observability has become essential as AI generates exponentially more data and introduces new operational complexities. Carl Perry, head of analytics at Snowflake, explained that "issues and bugs with AI-driven applications are harder to diagnose than in traditional software, increasing the pressure on enterprises to identify and resolve problems quickly" 1

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. The integration aims to combine Observe's telemetry, log, and trace analytics with Snowflake's AI Data Cloud to tackle these challenges head-on.

Observe's platform helps developers identify the root cause of application slowdowns, website outages, and other technical issues while tracking operational metrics like inference costs incurred by large language models 2

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. The company's AI Site Reliability Engineer product uses a unified context graph to correlate different telemetry signals, enabling teams to detect anomalies earlier and resolve issues faster in distributed and autonomous systems 3

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Cutting Costs While Managing Petabytes of Telemetry Data

One of the most compelling aspects of the acquisition is its promise to enhance observability capabilities while dramatically reducing costs. Snowflake stated that enterprises will be able to ingest and retain all their telemetry data—including logs, metrics, and traces—while lowering observability costs by up to 60% 3

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. This cost reduction stems from leveraging economical S3 storage, compression, and elastic compute resources 4

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The combined platform will enable customers to manage enterprise-wide observability across terabytes to petabytes of telemetry with a modern, scalable architecture 4

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. Snowflake compresses data and stores it in low-cost object storage services, allowing customers to implement longer data retention windows than would otherwise be practical 2

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. This extended retention is critical for troubleshooting, as technical issues that emerged months ago can be investigated without hitting against artificially short retention windows that force premature data deletion.

AI SRE and the O11y Context Graph Drive Faster Resolution

At the heart of Observe's platform is AI SRE, an artificial intelligence chatbot that helps developers diagnose technical issues through natural language interactions. Users can customize how AI SRE approaches troubleshooting tasks, such as instructing it to focus solely on error logs from the past 24 hours when analyzing a newly detected outage 2

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. This AI-powered troubleshooting capability represents a shift from reactive monitoring to proactive, automated incident response.

Powering AI SRE is the O11y Context Graph, a data management engine that ingests hundreds of terabytes of telemetry per day from customer environments. The engine links together related pieces of data, creates indexes to speed up information searches, and generates materialized views—cached copies of query results that cut loading times 2

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. Ramaswamy emphasized that the combination helps customers "move from reactive monitoring to proactive, automated, AI-powered troubleshooting—identifying root causes faster and resolving production issues up to 10 times faster" 3

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Open Standards and Strategic Positioning for AIOps

Observe offers customers choice through an open-standard architecture based on Apache Iceberg for storage and OpenTelemetry for ingestion 4

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. This approach aligns with Snowflake's strategy to handle massive telemetry volumes from modern AI agents and production-scale applications while maintaining operational visibility. Christian Kleinerman, Snowflake's executive vice president of product, noted that "an organization's ability to remain resilient is strictly limited by how much data it can afford to ingest, how it navigates the myriad of formats and silos, and the speed at which it can reason across it" 2

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The deal follows Snowflake's recent $200 million partnership with Anthropic to bring Claude AI models into its platform 4

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, signaling a broader strategy to position itself as infrastructure for AI at scale. Perry noted that "as AI agents generate exponentially more data, vertically integrated data and observability platforms become essential to running production AI reliably and economically" 1

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. Observe CEO Jeremy Burton stated that merging with Snowflake provides "the opportunity to accelerate our mission" and bring observability to more enterprises worldwide 4

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. The deal is expected to close later this year, with Snowflake holding cash and cash equivalents of $1.94 billion as of October 31, 2025 5

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