Infrahub is a graph-based data management platform for teams that run networks, data centres, and cloud infrastructure, and it gives them versioned, validated infrastructure data instead of fragmented records and one-off scripts.

What it is

Infrahub is an Apache-2.0 licensed, Python-based platform that acts as the data foundation for infrastructure automation. It keeps devices, services, IPs, VLANs, circuits, and the business context attached to them in a single graph-backed source of truth, with schema, data, and generated artifacts all stored, queried, and changed through one system. The platform is assembled from open-source foundations named in its own documentation: Neo4j as the graph database, Git for version control concepts, Prefect for workflow execution, GraphQL for query, and Python for extension.

The concrete problem it solves is the split between where infrastructure data lives and how automation consumes it. Teams typically hold inventory in spreadsheets or separate tools, then write scripts that read that data once and break when it drifts. Infrahub replaces that pattern with a schema-first model that enforces validation, first-class relationships, and full change history, so automation runs against data that is reviewed and consistent. It also federates rather than only replaces: Infrahub Sync pulls data from NetBox, Nautobot, and IP Fabric into the same graph.

Key capabilities

• Schema-first, graph-backed data models for devices, services, topologies, and business context, enforced with validation and queried through relationships on Neo4j.
• Version control applied to data, not only configuration: branches, peer review, and a queryable history of who changed what, when, and why across schema, data, and generated artifacts.
• Generators and transforms that render configuration and other artifacts from Infrahub data using Jinja2 or Python, stored version-controlled next to the data.
• A production change flow of branch, CI, review, merge, with idempotent re-runs that surface and correct deviations instead of forcing a full rebuild.
• Integrations and federation: connectors for Git, Ansible, and Nornir, plus Infrahub Sync for NetBox, Nautobot, and IP Fabric.
• GraphQL, REST, and a web UI for service catalogs, so internal teams and customers can request infrastructure resources directly.
• An MCP server and Infrahub Skills that expose validated, relationship-rich data to Claude, Cursor, and other AI agents.
• A Python SDK and the infrahubctl CLI from the infrahub-sdk-python project for building integrations and managing the platform.

Who uses it and how

• Network and data centre engineering teams that need one source of truth spanning devices, services, IP addressing, VLANs, circuits, and the business context tied to them.
• Platform and operations teams that publish a self-service catalog over GraphQL, REST, or the web UI, letting application teams request resources without manual ticketing.
• Automation teams that generate, validate, and deploy configurations across multiple vendors and sites, and that need full lifecycle workflows for provisioning, upgrade, and decommission.
• Teams building an AIOps knowledge graph, modelling dependencies across infrastructure to feed AI-driven reasoning, troubleshooting, and predictive operations.
• Organizations running production deployments on Kubernetes, using the opsmill/infrahub-helm chart and the installation guide rather than a single host install.

Getting started

Three paths are documented: a hosted sandbox at sandbox.infrahub.app with sample data and no installation, a guided browser lab on Instruqt covering branches, schemas, and unified storage, or the Quick Start guide at docs.infrahub.app/overview/quickstart for a local instance. Production deployments on Kubernetes use the opsmill/infrahub-helm chart alongside the installation guide.

How it compares

The facts provided do not list paid products that Infrahub replaces, but they do name adjacent tools: NetBox, Nautobot, and IP Fabric appear as data sources that Infrahub Sync federates from, which places Infrahub as a layer above those systems rather than a straight swap for them. Its distinguishing combination is native version control of the data itself plus a graph store, sitting on open foundations such as Neo4j, Git, Prefect, GraphQL, and Python.

When to use it — and when not to

A self-hoster takes on operating the platform and its Neo4j graph database, and production use on Kubernetes assumes Helm and cluster management capacity, so a small team without infrastructure operations experience should weigh that before adopting it. The repository shows 519 stars against 552 open issues, which suggests an active but heavy issue backlog, and the README excerpt ends mid-list for the ecosystem projects, so anyone evaluating it should read the full documentation rather than the summary. It is a poor fit for teams that only need a static inventory record and have no automation, validation, or review workflow to feed.