Databricks demonstrates how Model‑as‑a‑Service (MODaaS) can be delivered at enterprise scale by combining standardized model access, unified governance, and deep observability across the AI lifecycle. Built on open platforms such as Unity Catalog and MLflow, the Databricks approach enables organizations to confidently deploy, manage, and evolve AI models while maintaining control over cost, quality, and risk. At the core of the solution is a unified access layer that standardizes how applications consume AI models, making it easier to swap, compare, and evolve models as the ecosystem rapidly changes. Centralized discovery and cataloguing allow teams to find the right model for each use case, enriched with metadata, tags, and performance signals, while access controls ensure models are governed consistently across the enterprise. Operational control and resilience are supported through the Databricks AI Gateway, which provides traffic routing, rate limiting, and failover capabilities, complemented by open telemetry‑based observability. End‑to‑end traceability captures inputs, outputs, token usage, and performance metrics, creating a transparent audit trail that supports compliance, troubleshooting, and optimization. Quality, lifecycle management, and cost visibility are embedded through MLflow and platform system tables, enabling continuous evaluation, monitoring against golden datasets, and detailed FinOps reporting. Together, these capabilities provide a practical, standards‑aligned blueprint for MODaaS—helping enterprises move faster with AI while maintaining trust, governance, and operational confidence.