Cohere launches Model Vault for secure, isolated, and scalable enterprise-grade AI inference

Cohere announced Model Vault, a fully isolated SaaS platform that allows enterprises to run AI models with high security, guaranteed performance, and predictable scalability. The proposal addresses one of the main bottlenecks in corporate AI adoption: serving models in production without exposing sensitive data or bearing the operational burden of infrastructure. Technically, Model Vault combines the best of both worlds. Client applications run inside a secure VPC, while Cohere model inference runs in a dedicated, isolated, and managed cloud environment. This eliminates common multi-tenant environment issues such as workload interference, rigid rate limits, and unpredictable latency. The launch recognizes a structural shift in enterprise AI use. Inference is rapidly becoming the main computational cost, surpassing model training as AI is incorporated into multiple workflows, products, and teams. With recurring and scalable workloads, predictability of performance and cost becomes a critical decision factor. Traditional SaaS platforms prioritize operational simplicity but sacrifice isolation, control, and compliance. Self-hosted solutions offer full control but require high investment in hardware, engineering, and maintenance. Model Vault emerges as an intermediate architecture, reducing total cost of ownership without compromising rigorous security and governance requirements. This model becomes even more relevant in agentic applications, such as those built with North, Cohere's corporate platform. Agentic workloads are inherently unpredictable, with demand spikes and multiple chained calls, making fixed infrastructure provisioning inefficient. The inference abstraction offered by Model Vault allows scaling on demand without compromising SLAs or compliance. Model Vault marks a strategic advance in the maturity of enterprise AI, showing that the future of large-scale adoption depends less on training models and more on operating inference efficiently, securely, and sustainably.