TNO069: Network to Code Gets Enterprise Open-Core Right with Nautobot (Sponsored)

Network to Code founders Jason Edelman (CTO) and Jeff Bradbury (VP of Marketing) discuss the company's strategic pivot from purely open-source and services-based business model to a hybrid open-core approach. The company, founded in 2014, has spent over a decade learning from enterprise customers through large-scale professional services projects, which informed the development of their Nautobot platform launched in 2021.

The company maintains clear principles about what remains open-source versus commercial: the open-source core (the "engine") continues to evolve independently and powers all commercial offerings, while new commercial software focuses on turnkey, pre-built automation solutions for specific customer outcomes. All existing open-source projects remain open-source, and the core platform components necessary to support future development remain freely available to the community.

The Nautobot 3.1 release, launched in April, introduced commercial packages (Professional and Enterprise editions) alongside the free community edition. The transition was motivated by customer feedback indicating that fixed, predictable software costs provide better financial planning than variable services-based pricing models. The release has generated positive reception, with enterprises appreciating the ability to capitalize software costs and the clarity of what they're purchasing.

Regarding AI integration, the company takes a measured, trust-focused approach. They've already released Nautobot GPT (assistive AI layer) and are progressing toward more advanced agentic capabilities while maintaining safety guardrails. Rather than pursuing autonomous operations, they emphasize AI as a tool that recommends actions while deterministic jobs and scripts execute the actual changes. Critical governance concepts include authentication/authorization per agent, comprehensive logging, time-of-day restrictions, idempotency verification, and geographic constraints.

The company frames their AI strategy around the progression from assistive AI to analysis to operations, with the ultimate goal of trustworthy autonomous capability. They stress that data quality is foundational—AI requires clean, trusted data to function effectively. Compliance and traceability are non-negotiable, requiring audit trails and human-in-the-loop capabilities where appropriate.

Nautobot 3.2 priorities include UX improvements, real-time search, and enterprise governance features. A particular focus is enhanced reporting capabilities with audit-ready reports for different personas (engineers, leaders, auditors) and a report engine allowing customization. The release also includes orchestrated automation capabilities enabling job chaining.

The company emphasizes pragmatism and grounding their approach in customer reality rather than hype. Both leaders stress the importance of staying practical, understanding what can actually be deployed in production with confidence, and taking phased approaches to adoption that match real customer capabilities.