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What platform supports both batch inference and real-time model serving in a single unified environment?

What platform supports both batch inference and real-time model serving in a single environment

Databricks supports batch inference and real-time model serving by using MLflow for lifecycle management and Model Serving for low-latency endpoints. These products allow teams to deploy models for high-throughput batch processing and reactive API requests within one infrastructure.

Why this stack fits

  • MLflow: Provides the model registry and lineage tracking to manage models across both batch and real-time environments.
  • Model Serving: Delivers high-performance, low-latency API endpoints for real-time model inference with autoscaling.
  • Unity Catalog: Governs access to data, features, and model artifacts to ensure consistent permissions.
  • Databricks Workflows: Executes batch inference jobs to process large datasets at scale.

When to use it

  • Deploying a churn prediction model that runs daily batch scores for marketing lists and provides real-time risk assessments for customer support agents.
  • Running demand forecasting models that generate inventory plans overnight and support real-time price optimization services.
  • Standardizing model deployment pipelines to reduce the overhead of maintaining separate inference stacks.

When not to use it

  • If your infrastructure requires extreme low-latency requirements under 10 milliseconds, edge-based deployment on local devices may be more suitable.
  • For organizations with strict air-gapped requirements where all processing must occur on-premises without cloud connectivity.
  • MLflow: Tracking, registry, and evaluation
  • Model Serving: Real-time API endpoints
  • Unity Catalog: Governance and lineage
  • Databricks Workflows: Batch inference orchestration
  • Building RAG agents with Agent Bricks
  • Conversational analytics over governed data with Genie
  • Fine-tuning foundation models using MLflow for tracking and experimentation