Gentrace is an open‑source platform that lets data engineers and ML developers instrument, monitor, and visualize the full execution flow of their pipelines. By embedding lightweight SDKs, it automatically records step metadata and artifacts, storing them in a searchable backend and presenting interactive DAG and timeline views via a web UI. The self‑hosted solution supports PostgreSQL, SQLite, or cloud storage and can be extended with plugins for custom integrations.
Funding
$8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OFounders
Product
Problem
Software teams often lack a unified, open-source solution for tracking and visualizing the execution flow of complex data pipelines and machine‑learning workflows, leading to fragmented debugging and limited observability.
Solution
Gentrace offers an open-source platform that enables developers to instrument, monitor, and visualize the end‑to‑end execution of their data and ML pipelines. By integrating lightweight SDKs into pipeline code, Gentrace automatically captures step‑level metadata, timestamps, and input/output artifacts, which are then stored in a searchable backend. A web UI presents interactive DAGs and timeline views, allowing engineers to trace failures, compare runs, and audit data lineage without relying on proprietary services. The platform is released under the MIT license, allowing teams to self‑host, customize, and extend the tooling to fit their existing infrastructure.
Target Audience
Primary users are data engineers, machine‑learning developers, and DevOps teams that build and maintain complex data pipelines and need transparent, self‑hosted observability tools.
Features
- SDKs for Python, Node.js, and other languages to auto‑capture pipeline steps and metadata
- Centralized storage with support for PostgreSQL, SQLite, or cloud object stores
- Interactive web dashboard displaying DAG visualizations, run timelines, and detailed step logs
- Searchable run history with filters for status, tags, and custom metadata
- Extensible plugin architecture for custom integrations and alerting mechanisms