AI-powered observability platform storing logs, metrics, and traces in a unified data lake
Observe builds an AI-powered observability platform designed to consolidate logs, metrics, and traces into a single data lake—Snowflake + Trino + ClickHouse architecture—eliminating the tool fragmentation typical of enterprises running separate log aggregation, APM, and metrics systems. The engineering-heavy hiring composition combined with active work on query optimization and streaming pipelines suggests the company is addressing a core pain point: reducing query execution times and costs for customers dealing with high-volume telemetry.
Observe delivers observability software for enterprises managing distributed applications across highly regulated environments. The platform ingests telemetry data—logs, metrics, traces—through a unified storage layer (data lake) rather than separate systems, reducing operational complexity and infrastructure costs. The company serves mid-market to enterprise buyers who prioritize incident detection speed and operational cost efficiency. Engineering focus areas include core observability features, automated system tuning, streaming data pipeline improvements, and query performance optimization.
Observe's platform is built on Snowflake, Trino, ClickHouse, and Apache Iceberg for storage and querying; Kubernetes, Docker, and Terraform for infrastructure; React, TypeScript, and GraphQL for frontend; and integrates with Prometheus, Jaeger, OpenTelemetry, and compatible with Datadog and New Relic exports.
Current projects include interactive data-rich UI improvements, automated system parameter tuning, token indices features, guided user flows, streaming data pipeline enhancements, and computation platform benchmarking to reduce query execution times and costs.
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