Observe Inc. (A Snowflake Company)

What the Company Does

Observe provides a Software-as-a-Service (SaaS) observability platform that helps DevOps engineers, site reliability engineers (SREs), developers, and IT operations teams troubleshoot problems across complex, distributed software environments. The central idea behind Observe is to treat all machine data — logs, metrics, and traces — as a single body of information that can be collected, related, and queried together, rather than being siloed across separate specialized tools.

Traditional observability approaches often require organizations to run multiple point products: one for log management, one for metrics monitoring, one for distributed tracing, and yet another for application performance monitoring. Observe's differentiator is its unified approach, which ingests all forms of telemetry into one place and then uses a data model to connect related events. This allows teams to move seamlessly from a high-level symptom to a granular root cause without having to jump between disconnected tools or lose context along the way.

A key architectural principle of Observe is the separation of data storage from compute. Because the platform is built on a cloud data warehouse foundation, customers are not forced to pre-decide what data to keep or discard based on indexing costs. Instead, large volumes of data can be retained affordably, and compute is spun up on demand when analysis is required. This model is intended to help organizations avoid the steep and unpredictable costs frequently associated with legacy observability tooling.

The Data Graph and Data Model

One of the concepts most closely associated with Observe is its "Data Graph." Rather than presenting raw streams of logs and metrics in isolation, Observe shapes incoming telemetry into curated datasets that represent real-world entities — such as containers, Kubernetes pods, hosts, users, services, and applications. These datasets are linked together so that relationships between components become explicit and navigable.

This entity-centric modeling means that when an engineer investigates an incident, they can navigate the connections between related resources. For example, a team member might start with an error in a particular service, then follow the relationships to the underlying containers, the host machines running them, and the deployments that introduced changes. This ability to trace cause and effect across related components is central to how Observe reduces the time it takes to resolve incidents.

Products and Capabilities

While Observe is delivered as a single integrated platform rather than as a collection of separately sold products, it offers a range of distinct capabilities and application areas within that platform:

Log Analytics — Observe ingests, stores, and analyzes log data at scale. Because storage is decoupled from compute, teams can retain detailed logs for long periods and query them retroactively when investigating historical issues.

Metrics Monitoring — The platform collects and visualizes time-series metrics from infrastructure and applications, enabling teams to build dashboards, track key performance indicators, and detect anomalies in system behavior over time.

Distributed Tracing — Observe supports the collection and analysis of distributed traces, allowing teams to follow requests as they move across microservices and identify latency bottlenecks or failures in service-to-service communication.

Kubernetes and Container Observability — Given the prevalence of containerized workloads, Observe provides dedicated visibility into Kubernetes clusters, pods, nodes, and workloads, helping teams understand the health and performance of cloud-native deployments.

Application Performance Monitoring (APM) — The platform delivers APM capabilities that give developers insight into how their applications are performing in production, surfacing errors, slow transactions, and resource issues.

Dashboards and Visualizations — Observe includes tools for building custom dashboards and visualizations, allowing teams to monitor the metrics and datasets most relevant to their operational needs.

Alerting and Monitoring — The platform supports the creation of alerts and monitors so that teams are notified when systems deviate from expected behavior, enabling proactive response before problems escalate.

OPAL Query Language — Observe provides its own query language, OPAL (Observe Processing and Analysis Language), which lets users transform, filter, join, and shape data. OPAL gives power users the flexibility to work with datasets in sophisticated ways beyond point-and-click interfaces.

Data Collection and Integrations

Observe is designed to be vendor-neutral in how it collects data. It supports open standards such as OpenTelemetry, which allows customers to instrument their applications and send telemetry without being locked into proprietary agents. The platform also integrates with a wide range of data sources and cloud providers, ingesting information from infrastructure, applications, cloud services, and third-party tools.

This broad ingestion capability means organizations can bring together data from across their entire technology stack — including public cloud environments, on-premises systems, and various software services — into a single observability platform for correlated analysis.

Artificial Intelligence and Automation

In keeping with broader industry trends, Observe has invested in applying artificial intelligence and machine learning to observability workflows. The platform incorporates AI-assisted features intended to help engineers investigate incidents more efficiently, surface relevant context automatically, and reduce the manual effort involved in querying and correlating large volumes of data. These capabilities aim to accelerate root-cause analysis and lower the barrier to entry for teams that may not have deep expertise in data querying.

Target Customers and Use Cases

Observe primarily serves engineering-driven organizations that operate complex, modern software systems. Its customers typically include technology companies, SaaS providers, and enterprises undergoing cloud transformation. The typical users within these organizations are DevOps engineers, site reliability engineers, platform engineers, software developers, and IT operations teams.

Common use cases for the platform include incident investigation and root-cause analysis, monitoring the health of cloud infrastructure, troubleshooting microservices and containerized applications, managing and analyzing log data at scale, and controlling the often unpredictable costs associated with observability tooling.

Business Model

Observe operates on a subscription-based SaaS model. A distinguishing feature of its commercial approach has been a usage-based pricing philosophy that seeks to align costs with the value customers derive from analyzing their data, rather than penalizing organizations for simply storing large volumes of telemetry. By leveraging the economics of a cloud data warehouse, Observe has positioned itself as a more cost-transparent alternative to legacy observability vendors whose pricing can escalate rapidly with data volume.

Relationship with Snowflake

Observe's technical relationship with Snowflake predates the acquisition. From its inception, Observe built its platform on top of Snowflake's cloud data platform, using it as the scalable, elastic backend for storing and processing telemetry data. This deep technical integration made the eventual acquisition a strategic fit. As a Snowflake company, Observe extends Snowflake's data cloud into the observability domain, bringing machine and telemetry data into the same ecosystem where enterprises already manage their business and analytics data. This convergence supports the broader vision of unifying operational and analytical data on a single platform.

Summary

Observe Inc. is an observability software company that unifies logs, metrics, and traces into a single platform built on a cloud data warehouse foundation. Through its Data Graph, entity-centric data model, OPAL query language, and AI-assisted investigation tools, it helps engineering and operations teams troubleshoot complex distributed systems quickly and cost-effectively. Now part of Snowflake, Observe continues to focus on delivering scalable, cost-transparent observability that connects operational telemetry with the wider data cloud ecosystem, serving the needs of modern, cloud-native enterprises.