HyperDX was built on a core belief that observability is fundamentally a data problem, and ClickHouse is the optimal database to solve it. The platform helps engineers quickly diagnose production issues by making it trivial to search, visualize, and correlate logs and traces on top of any ClickHouse cluster. Unlike traditional observability tools that silo logs, metrics, and traces into separate workflows, HyperDX presents a unified view that lets engineers follow a request from a user's browser through backend servers and async workers. The search experience supports both intuitive property filters like level:err and full SQL queries, giving novice and power users appropriate interfaces for their skill level.
The platform's architecture is OpenTelemetry-native from the ground up, meaning it works with any instrumentation that speaks the OTel standard. Built-in SDKs for Browser, Node.js, and Python simplify initial setup, while the OTel Collector integration supports any language or framework in the ecosystem. Key capabilities include session replay for visually debugging user-facing issues, distributed tracing with automatic service maps, log aggregation with blazing-fast full-text search, infrastructure metrics with anomaly detection, and exception tracking with stack trace correlation. All data flows into a single ClickHouse backend, enabling cross-signal correlation that traditional multi-store architectures struggle to achieve.
HyperDX was acquired by ClickHouse in March 2025, with the team joining to build the future of open-source observability as ClickStack. The acquisition was driven by ClickHouse's own experience building their internal LogHouse monitoring system, which achieved a 200x cost reduction over Datadog but lacked a polished frontend. HyperDX Cloud continues serving customers, and the open-source project remains actively maintained under MIT license. Deployment options include a single all-in-one Docker container for quick evaluation, Docker Compose for production self-hosting, or ClickHouse Cloud integration. The platform charges per data ingestion volume with no per-seat fees, making it significantly more cost-effective than proprietary alternatives for data-heavy organizations.