AI's Next Frontier Is Infrastructure Control, Not Models
Mozilla's Otari project argues enterprise AI's real bottleneck isn't model quality but cost visibility, multi-provider sprawl, and governance at scale.
Since 2022, enterprise AI has moved from experimental side projects to full production deployment, and now into hard questions about cost and data ownership. The ChatGPT-driven curiosity phase gave way to a 2024 culling of failed pilots, followed by a 2025-2026 production surge where budgets are blown through in months and sovereign-AI debates intensify.
Mozilla argues the real shift wasn't model capability but adoption velocity: teams now depend on dozens of models and providers simultaneously, creating three concrete problems — operational chaos from provider and API fragmentation, non-linear and opaque production costs, and governance gaps around tracking which model answered what, when, and to whom in regulated industries.
The takeaway is that competitive advantage no longer comes from which model you use, but from how reliably, cheaply, and safely you can operate AI at scale. That's why Mozilla built Otari, an open-source control plane sitting between applications and models, offering intelligent routing, real-time observability, org-wide policy enforcement, and provider independence to turn AI operations into a mature engineering discipline.