Snowflake to acquire Observe to unify analytics and telemetry data

Snowflake announced that it has entered into a definitive agreement to acquire Observe, the unified data experience platform built for all forms of analytics using the scale and performance of Snowflake’s Data Cloud.

Subject to regulatory approval, the acquisition would bring logs, metrics, and traces into Snowflake with Snowpark and help customers identify issues in AI-era data pipelines and production systems more quickly than existing solutions allow.

Why Snowflake’s acquisition of Observe makes strategic sense

Observe was built from the ground up on top of Snowflake, meaning companies would have a leg up when it comes to integration and performance tuning. That alignment lessens the typical friction that comes with platform acquisitions — there is no replatforming tax, no default data duplication, and no untested connectors. Snowflake has positioned the combination as a means to unify telemetry data with analytical data so that teams can correlate application incidents with user activity, schema changes, or upstream model outputs all within one queryable environment.

Snowflake has also been studying AI workloads, where telemetry volume is “exponentially growing.” When companies wire up agents and LLM-driven apps, they churn out torrents of traces and prompts intermixed with the old logs. Snowflake is betting teams will also be able to diagnose and tune performance faster by keeping observability data co-located with feature stores and model evaluation tables. The company says that customers could realize up to 10 times faster time to resolution when telemetry lives natively in context with operational and analytical data.

What customers can expect after Snowflake acquires Observe

Operationally, the purchase points to a single telemetry lake based on open formats and standards — Apache Iceberg and OpenTelemetry in this case — for organizations to ingest from common agents, store at scale, and query in situ using familiar SQL. Keeping observability data in the same place as business data also clamps down on egress and duplication, which, to put it mildly, has been a spiking pain point for growing enterprises that persist with separate observability silos.

For data and platform teams, the win is clear: a failed checkout correlates to a specific deployment, a slow inference correlates with a model update, and cost spikes correlate with (invasive) logging. That type of end-to-end chain-of-custody becomes hard to maintain when logs reside in one tool, traces in another, and business context within yet another.

Deal size and governance environment surrounding the acquisition

Terms were not officially announced, though the price was estimated at about $1 billion in various news reports. If that number holds, it would become Snowflake’s biggest acquisition yet, surpassing its $800 million purchase of Streamlit in 2022. Observe has raised about $500 million from investors like Sutter Hill Ventures, Snowflake Ventures, and Madrona. Both Snowflake and Observe have been incubated at Sutter Hill — and Observe CEO Jeremy Burton has sat on Snowflake’s board since 2015 (relationships that could help smooth out post-close integration but almost certainly will also raise eyebrows among governance peeps).

Observability market consolidation accelerates with this move

The move is part of a larger pattern of consolidation in observability and data infrastructure. Cisco’s $28 billion acquisition of Splunk and the take-private of New Relic by private equity underscored observability as important — as well as capital-intensive. Snowflake has been piecing together what it hopes will become a one-stop shop data and AI platform where the delta lakehouse layer (as Coretelligent calls it) is at the epicenter of that entire platform, with this acquisition extending out into real-time operations telemetry that incumbents like Datadog, Elastic, and Dynatrace have dominated.

The bet is that customers will find themselves preferring fewer platforms that cover data engineering, governance, AI, and observability together rather than maintaining separate data lakes and monitoring stacks with overlapping storage and ingestion costs. For large organizations, telemetry has become one of their fastest-growing line items; centralization of telemetry onto their data infrastructure holds the promise for better cost control and longer-term retention while maintaining good query performance.

Technical backbone and open standards underpinning the deal

Observe is instrumental in supporting Snowflake’s expansion to open table formats, as it enables the auto-creation of low-cost structures that are designed for large-scale storage and optimized for time travel across telemetry datasets (using Apache Iceberg).

OpenTelemetry integration: Customers can leverage a large ecosystem of collectors and SDKs without being tied to a vendor at the data emission layer. That combination — OTel in, Iceberg at rest, SQL for analysis — could make formerly niche use cases, such as correlating LLM prompts with downstream errors or monitoring SLOs across streaming and batch pipelines, much more attainable to data teams.

Key signals to watch as Snowflake integrates Observe

Signals to watch for are how Snowflake prices and packages observability data differently than traditional analytical storage, the speed at which Observe’s capabilities appear natively in Snowflake’s own UI and governance controls, and whether partners in the Snowflake ecosystem lean in or see this as competitive. To customers grappling with AI-driven telemetry growth, a tight integration might make Snowflake not only the system of record for data but also the first thing they reach for when production hiccups.