---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 07e8d616fe2489692549c9dbba12d1b4f3ac056c57b7c6618e758965e08382b8
  pageDirectory: concepts
  sources:
    - open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - genai-production-monitoring-with-llm-judges
    - GPMWLJ
    - Production monitoring with multi-turn judges
  citations:
    - file: open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md
title: GenAI production monitoring with LLM judges
description: A Databricks service that continuously evaluates a sample of production GenAI traffic using LLM judges and scorers, powered by production-scale trace ingestion stored in Unity Catalog tables.
tags:
  - genai
  - monitoring
  - evaluation
  - llm-judges
timestamp: "2026-06-19T19:49:57.453Z"
---

# GenAI Production Monitoring with LLM Judges

**GenAI production monitoring with LLM judges** is a managed service on Databricks that continuously evaluates a sample of production traffic from generative AI applications using automated LLM-based evaluators (judges) and scorers. This service is part of the broader [MLflow 3 for GenAI](/concepts/mlflow-3-for-genai.md) ecosystem and is powered by production-scale trace ingestion. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Overview

Production monitoring for GenAI applications requires ongoing assessment of model outputs to detect quality degradation, safety issues, or drift over time. Databricks provides a production monitoring service that automates this process by sampling production traffic and running evaluations using [LLM Judges](/concepts/llm-judges.md) — language models configured to score outputs against defined criteria. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

The service is built on production-scale trace ingestion, which stores traces to [Unity Catalog](/concepts/unity-catalog.md) tables. This integration allows monitoring data to be governed alongside other enterprise data assets. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Key Capabilities

### Continuous Evaluation

The monitoring service evaluates a sample of production traffic on an ongoing basis. Rather than evaluating every request, it selects a representative subset and applies LLM judges to score outputs against quality, safety, and relevance criteria. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### LLM Judges and Scorers

LLM judges are automated evaluators that assess model outputs without requiring human annotators. These judges can be configured to score outputs on dimensions such as:

- **Correctness** — Does the output accurately answer the user's question?
- **Helpfulness** — Is the response useful and relevant?
- **Safety** — Does the output contain harmful or inappropriate content?
- **Faithfulness** — Does the response stay grounded in the provided context?

Scorers are the specific evaluation functions that apply these judges to production traces. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

### Trace Storage and Analysis

All monitoring data is stored as traces in Unity Catalog tables. This enables:

- **Governance** — Traces are governed under Unity Catalog alongside models, feature tables, and other assets.
- **Analysis** — Users can query monitoring data using Databricks SQL or analyze it in AI/BI dashboards and Genie Spaces.
- **Auditing** — System tables provide usage and audit logs for monitoring activities. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Integration with the Databricks Platform

The production monitoring service is one of several production-oriented integrations available in managed MLflow on Databricks. Other related integrations include:

- **[MLflow 3 Deployment Jobs](/concepts/mlflow-3-deployment-jobs.md)** — Automate staged deployment of ML models using Databricks Workflows and Unity Catalog.
- **[Declarative Automation Bundles](/concepts/declarative-automation-bundles.md)** — Manage MLflow experiments, models, and monitoring configurations as infrastructure-as-code.
- **[MLOps Stacks](/concepts/mlops-stacks.md)** — Reference architectures for production ML workflows. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Benefits Over Open Source MLflow

While open source MLflow provides the core data model and APIs for experiment tracking and evaluation, the managed monitoring service on Databricks adds:

- **Fully managed hosting** — The monitoring infrastructure runs on production-ready, scalable servers with automatic updates.
- **Enterprise governance** — Monitoring data is governed under Unity Catalog with fine-grained access controls.
- **Platform integration** — Monitoring results can be analyzed alongside other data assets using Databricks SQL, dashboards, and notebooks. ^[open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md]

## Related Concepts

- [LLM Judges](/concepts/llm-judges.md) — Automated evaluators used to score model outputs in production monitoring.
- Production-scale trace ingestion — The infrastructure that captures and stores traces from production GenAI applications.
- [Unity Catalog](/concepts/unity-catalog.md) — The governance layer that manages monitoring data, models, and other AI assets.
- [MLflow 3 for GenAI](/concepts/mlflow-3-for-genai.md) — The broader framework for developing, evaluating, and monitoring generative AI applications.
- [Human feedback](/concepts/mlflow-human-feedback-collection.md) — The Review App and expert feedback UI that complement automated LLM judges.
- [Agent evaluation](/concepts/mlflow-agent-evaluation.md) — Evaluation workflows for AI agents that can be monitored in production.

## Sources

- open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md

# Citations

1. [open-source-vs-managed-mlflow-on-databricks-databricks-on-aws.md](/references/open-source-vs-managed-mlflow-on-databricks-databricks-on-aws-ce848b0f.md)
