Cloud data company Snowflake will buy AI observation leader Observe to expand its capabilities in the $50 billion IT operations management software market.
Snowflake fiddled with the purchase price because it’s a secret. But the idea is to offer a unified platform that combines data storage, AI deployment and monitoring.
Look at this
Market forces are well aligned at this point. The IT operations management software market will grow 9% to $51.7 billion in 2024, according to Gartner, while 54% of organizations adopted AI monitoring last year.
What makes this acquisition interesting isn’t just the numbers — it’s the history of the relationship. Observe has been built on top of Snowflake since its inception, and Snowflake invested in Observe in March 2024. They weren’t strangers who suddenly decided to merge—they were partners who have been working together for nearly two years on everything from application slowdowns to AI model performance issues.
An AI assistant that solves problems
The crown jewel of the acquisition isn’t traditional monitoring software—it’s Observe’s AI Site Reliability Engineer (AI SRE), an artificial intelligence chatbot that can resolve production issues up to 10x faster than traditional troubleshooting methods.
Consider this transformation: instead of spending hours or days searching for system failures, developers now engage in natural-language conversations with AI that diagnose problems in real-time. Users ask AI to identify the causes of failures and adapt its investigation approach for each unique scenario.
Behind this speed lies the O11y Context Graph, a data management tool that processes hundreds of terabytes of telemetry data every day. This creates indexes and materialized views that reduce lookup times and eliminate frustrating load delays.
In addition to routine monitoring, the platform tracks specialized metrics such as inferred costs for large language models, positioning it perfectly for the rapidly evolving enterprise AI landscape across industries.
What this means for enterprise AI operations
This acquisition eliminates a business problem: the cost of keeping observability data long enough for meaningful problem solving. Previously, organizations were forced to frequently delete telemetry data to avoid storage costs, making investigating older incidents virtually impossible.
Snowflake’s approach is to compress data and store it in low-cost object storage, which means companies can now keep observability data for longer periods of time without budget constraints. Christian Kleinerman, EVP of Snowflake, explained that the combined platform enables customers to manage enterprise-wide observability across petabytes of telemetry data.
The integration creates a unified, open-standard architecture based on Apache Iceberg and OpenTelemetry that directly challenges established players such as Datadog. For businesses already using Snowflake, the benefits are immediate: they can keep their observability data in their Snowflake account instead of sending it to third-party providers – a significant benefit for data management and security compliance.
The development of artificial intelligence Snowflake
The acquisition represents much more than just monitoring capabilities – it’s the culmination of Snowflake’s broader strategy to transition from a data warehousing company to an AI-focused platform. The deal builds on strategic moves including the acquisition of TruEra in May 2024 and the acquisition of Select Star and Datometry in November 2024.
Snowflake also unveiled an expanded partnership with Google Cloud that brings Gemini models directly to Snowflake Cortex AI. This dual sends an unmistakable message: Snowflake has announced itself as a central hub where data teams, model providers, and applications converge.
The acquisition is subject to regulatory approval and deepens Snowflake’s commitment to helping customers build and operate reliable AI agents and applications.
For enterprises watching AI workloads become critical to their operations, Snowflake is betting that they will choose a unified platform combining data storage, AI model deployment, and end-to-end monitoring of disconnected tool collections.
Experts predict that the year 2026 will bring less AI hype and better governance, delayed enterprise spending, the transition of AI to OT, smarter cyber attacks and faster cooling technologies.