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IJONIS
Enterprise AI

Enterprise AI Platform

An Enterprise AI Platform is a centralized technical infrastructure that consolidates all AI activities of a company: model hosting, data pipelines, experiment tracking, deployment, monitoring, and governance. It replaces isolated individual solutions with a unified platform where AI applications are developed and operated in a standardized manner.

Why does this matter?

Without a central platform, AI silos emerge: each department uses its own tools, data is duplicated, governance is impossible, and scaling fails. An Enterprise AI Platform reduces costs per AI application by 40-60% because infrastructure, data pipelines, and security features are shared — like a common foundation for all AI projects.

How IJONIS uses this

We build Enterprise AI Platforms on top of your existing cloud or on-premise stack: Kubernetes for container orchestration, MLflow for model management, LangServe for LLM deployments, and Grafana for monitoring. The platform is built modularly — you start small and expand as needed without restructuring the architecture.

Frequently Asked Questions

At what level of AI usage does an Enterprise AI Platform pay off?
From three to five productive AI applications, a central platform becomes economically sensible. Below that, project-specific setups suffice. The tipping point comes when you notice infrastructure setup and maintenance cost more time than actual AI development — then it is time for platform standardization.
Cloud or on-premise for the Enterprise AI Platform?
That depends on your data privacy requirements and usage volume. Cloud (AWS, Azure, GCP) offers maximum flexibility and fast start. On-premise provides full data control but requires your own GPU hardware. We often recommend a hybrid approach: non-critical workloads in the cloud, sensitive data locally.
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