Model Serving
Recent items mentioning Model Serving across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
NewsDatabricks Apps vs Model Serving: Authentication, Cost, and Performance Compared
Databricks Apps are now the recommended first choice for deploying agents due to their flexibility in handling full-stack applications with multiple components, offering faster iteration and local testing compared to Model Serving. Model Serving remains suitable for use cases prioritizing high QPS, governance features like AI Gateway, inference tables, and guardrails, or when scaling to zero is acceptable for cost optimization.
MLflow 3.11.1 introduces AI-powered issue detection for agent traces, budget alerts and limits for AI Gateway spending, and a new interactive graph view for visualizing trace hierarchies. It also enhances security with pickle-free model serialization and improves dependency management with native UV support.