Snowflake signed a definitive agreement to acquire Observe, a provider of AI-powered observability.
With this acquisition, Snowflake will deliver the next generation of AI-powered observability, built on open standards and designed for the scale, complexity, and economics required by modern AI-driven enterprises.
“As our customers build increasingly complex AI agents and data applications, reliability is no longer just an IT metric – it’s a business imperative,” said Sridhar Ramaswamy, CEO, Snowflake. “By bringing Observe’s capabilities directly into the Snowflake AI Data Cloud, we are empowering our customers to manage enterprise-wide observability across terabytes to petabytes of telemetry with an open, scalable architecture and AI-powered troubleshooting workflows.”
From its inception, Observe was built on Snowflake and together, Snowflake and Observe will provide enterprises with:
- Agentic AI for faster troubleshooting: The combination of Observe’s AI-powered Site Reliability Engineer (SRE) with trusted data in Snowflake enables a shift from reactive monitoring to proactive, automated troubleshooting. Observe’s AI SRE leverages a unified context graph that correlates logs, metrics, and traces, allowing teams to detect anomalies earlier, identify root causes faster, and resolve production issues up to ten times faster, improving operational resilience as systems grow more distributed, dynamic, and autonomous.
- An open-standard architecture built for scale: The acquisition also establishes a unified, open-standard observability architecture based on Apache Iceberg and OpenTelemetry, standards which Snowflake has continuously contributed to. This approach allows enterprises to manage massive telemetry volumes using economical object storage, elastic compute, and interoperable standards, an essential foundation for operating next generation AI agents and applications at scale. By treating telemetry as first-class data within the Snowflake AI Data Cloud, enterprises can apply analytics and AI consistently across observability and business data, with greater flexibility, governance, and efficiency.
- Full telemetry data retention with efficient economics: As AI-driven applications generate unprecedented volumes of logs, metrics, and traces, enterprises have increasingly been forced to rely on sampling and short retention windows to manage cost. By unifying Observe’s AI-powered observability platform with Snowflake’s scalable and trusted data foundation, organizations can eliminate these tradeoffs and retain high-fidelity telemetry data, reducing observability cost substantially while improving visibility across their entire data estate.
“Observability is fundamentally a data problem, and Observe joining Snowflake is a natural extension of their AI Data Cloud, allowing us to accelerate our observability solution at true enterprise scale,” said Jeremy Burton, CEO, Observe. “As AI reshapes how applications are built, the bottleneck has shifted from writing code to operating and troubleshooting complex systems in production. Observe was built for this moment. By combining our AI-powered SRE with Snowflake’s AI Data Cloud, we can deliver faster insights, greater reliability, and dramatically better economics. Together, we’ll help enterprises run the next generation of AI applications and agents with confidence.”
Upon the closing of this acquisition, Snowflake will deepen its commitment to helping customers build and operate reliable agents and applications. Observe’s developer-friendly approach complements Snowflake’s existing workload engines by providing teams with real-time enterprise context, faster root-cause analysis, and AI-assisted troubleshooting - critical components for operating dynamic, autonomous systems at scale. Snowflake will also expand its presence in a rapidly growing IT operations management software market.
Closing of the acquisition is subject to receipt of required regulatory approvals and other customary closing conditions.
The Latest
Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...
Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...
I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...
The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...
For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...
The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...
44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...
Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...
77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...
In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...