Why the Next Era of AI Is About Infrastructure, Not Just Models
7 hours ago
- #Control Layer
- #Enterprise AI
- #AI Infrastructure
- The evolution of AI in enterprises has shifted from initial experimentation to production, with concerns now focusing on ROI, cost, and data governance.
- Production AI requires reliability, auditability, cost control, and governance, highlighting the need for infrastructure rather than just models.
- Recent changes include increased adoption velocity leading to fragmentation, cost opacity, and governance gaps as AI expands across sectors.
- Managing multiple AI providers creates operational complexity with custom failover, spreadsheets for cost tracking, and manual tuning, which is unsustainable.
- Cost visibility is critical; teams need to compare cost-per-outcome and set policies to manage spending effectively as AI scales.
- Control over AI infrastructure is becoming the new competitive advantage, emphasizing intelligent routing, observability, and policy enforcement.
- Mozilla built Otari as an open-source control plane to provide visibility, governance, and flexibility, addressing consolidation and opacity in AI systems.
- The agentic era requires systems to coordinate many AI agents, increasing complexity and necessitating a foundational control layer for scalable deployments.
- Otari aims to define a new category of AI infrastructure, enabling organizations to own their stack and avoid vendor lock-in in critical industries.