Observe

Emerging

San Mateo AI observability treating telemetry as structured data resources; $393M total ($145M Evolution Equity/Madrona Series B Sep 2024) from Sutter Hill Ventures founding with 50+ customers competing with Datadog for cloud-native observability.

Company Overview

About Observe

Observe Inc. is a San Mateo, California-based AI-powered observability platform — backed with approximately $393 million in total funding including a $145 million Series B in September 2024 led by Evolution Equity Partners with Madrona Ventures, plus Sutter Hill Ventures' founding investment — providing enterprise engineering and operations teams with a cloud-native observability solution that treats telemetry data as a structured data problem, curating logs, metrics, and traces into typed "resources" (users, sessions, help desk tickets, software builds, and other business objects) that enable sophisticated root-cause analysis and cross-stack troubleshooting beyond what traditional observability tools provide. Serving 50+ customers including Upstart Financial, OpenGov, and TopGolf, Observe is led by CEO Jeremy Burton (former C-suite at Oracle, Symantec, EMC, and Dell) with co-founders from Splunk, Snowflake, Wavefront, and Roblox. Founded in 2017 (Sutter Hill Ventures company).

Business Model & Competitive Advantage

Observe's resource-centric observability architecture addresses the signal-to-noise problem that makes traditional monitoring tools ineffective for complex distributed system debugging: conventional observability platforms (Datadog, New Relic, Splunk) ingest raw logs, metrics, and traces and provide search and visualization interfaces — but correlating a production incident (user-visible error) across the raw telemetry streams of 50+ microservices requires expert knowledge of which log patterns indicate causation versus correlation, and manual correlation across time-series in separate dashboards. Observe's structured resource model (automatically linking raw telemetry to the business object context — a specific user session, a specific software deployment, a specific transaction) enables analysts to start from "this user received an error at 3pm" and navigate directly to the microservice call, database query, and infrastructure event that caused it — reducing investigation time from hours to minutes.

Competitive Landscape 2025–2026

In 2025, Observe competes in the cloud observability, AIOps, and enterprise monitoring market with Datadog (NASDAQ: DDOG, full-stack monitoring, $2.4B revenue), Splunk (acquired by Cisco, SIEM and observability), and Honeycomb (structured observability for developers, $60M raised) for enterprise engineering team cloud-native observability and AI-powered incident investigation platform adoption. Sutter Hill Ventures' founding investment reflects the same model that produced Snowflake (Sutter Hill founded, $100B+ peak market cap) — deep expertise in founding and scaling data infrastructure companies. Evolution Equity Partners' Series B leadership reflects enterprise cybersecurity and observability infrastructure conviction. The 2025 strategy focuses on growing the AI-powered anomaly detection (correlating Observe's resource context with ML-based deviation detection for automated alert surface), building the product analytics use case (using Observe's user session resource model for product behavior analysis alongside infrastructure observability), and expanding enterprise deployments in financial services and e-commerce where incident response speed has direct revenue impact.

Founded
2017
Headquarters
San Mateo, California, USA
Revenue
$393M
Curated content • Fact-checked and verified

The Observe Story

Founded in 2017
San Mateo, California, USA
Founded by Jacob Leverich, Jonathan Trevor and 2 others

Founders

Jacob LeverichJonathan TrevorJon WattePhilip Unterbrunner
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Key Differentiators

Emerging Innovator

Observe is an emerging player bringing innovative solutions to the Infrastructure market.

Growth Stage

Observe has achieved $393M in revenue, demonstrating strong product-market fit.

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