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Databricks Mosaic

Name: Databricks Mosaic
Price: Contact for Pricing USD
Author: Databricks

Backed By Databricks

Enterprise-grade agent platform for analytics and data workflows.

AI Agents, Governance & Orchestration

Overview

Databricks Mosaic is a production-ready agent framework enabling developers to build, deploy, and evaluate AI agents on enterprise data using RAG (Retrieval-Augmented Generation). Features include governance, quality evaluation via MLflow, telemetry/observability, security guardrails, and cross-model support. Widely employed in data analytics use cases across industries.

Use Cases

Enterprise analytics agents

Automated report generation

Data-driven decision agents

Production-quality RAG applications

Top Integrations

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Platform

☁️

API

Product Info

Deployment Method

API-basedSaaS

Model Type

Closed Source

Support

Pricing

AI Guardrails

Safety Filtering (via Meta Llama 3)

Inference Tables (Payload Logging)

Monitor and audit request/response data being sent to model APIs

Usage Tracking

Monitor operational usage on endpoints

Alternates Research On The Product

Top Features

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Unified Access to any model (Foundation, External, Custom)

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Permission and Rate Limiting (User and Group-based)

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Payload Logging to Inference Tables (for audit/monitoring)

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Usage Tracking to System Tables (for cost and operational monitoring)

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Traffic Routing (Load balancing/Fallbacks)

Strengths

Provides a single, unified interface for accessing all AI models (internal and external), eliminating integration friction.
Centralized governance and security controls over all LLM consumption.
Full observability and cost tracking in Unity Catalog/Lakehouse.
Enables quick experimentation and model switching without code changes.
Built-in AI Guardrails (Safety and PII detection) for compliance.

Weaknesses

AI Guardrails for PII detection rely on automated mechanisms (Presidio) and may not guarantee detection of all sensitive information.
Some advanced features (Custom Guardrails) are still in private preview.
The MLflow integration is currently incomplete for configuring the Gateway.
Usage-based pricing for paid features (logging, guardrails) can lead to unpredicted costs if not controlled by rate limits.
AI Guardrails and Fallbacks are not supported on custom model serving endpoints (Only rate limiting and usage tracking are supported for non-route-optimized custom models).

Reviews From The Web

Real user experiences from across different platforms

Mosaic AI Gateway allows us to securely consume any LLMs, be they OpenAI or other models hosted on Databricks, while ensuring LLM traffic is properly governed and tracked.

Harisyam Manda

Recent

Databricks Website

Recommendations

Enterprises that need to enforce strong governance, compliance (PII, safety), and cost controls over a mix of internally hosted and external LLMs (e

, compliance (PII, safety), and cost controls over a mix of internally hosted and external LLMs (e.g., OpenAI).

Organizations already invested in the Databricks Lakehouse and Unity Catalog for data and AI governance

akehouse and Unity Catalog for data and AI governance.

Individuals or small teams that only use a single, external LLM and don't require enterprise-level governance

external LLM and don't require enterprise-level governance.

Users requiring a free or fixed-cost solution

Users requiring a free or fixed-cost solution.

Final Verdict

Provides centralized governance, full observability, and built-in safeguards to securely and compliantly manage all GenAI model consumption (including external models) at scale without needing to rewrite application code for model switching or integration.

Best For

•Enterprises that need to enforce strong governance, compliance (PII, safety), and cost controls over a mix of internally hosted and external LLMs (e.g., OpenAI).
•Organizations already invested in the Databricks Lakehouse and Unity Catalog for data and AI governance.

Watch Out For

•If the security/governance features need to be applied to workloads not supported by the current feature matrix (e.g., specific custom model endpoints without guardrails).
•If the payload logging limit of 1 MB is insufficient for auditing large requests/responses.
•AI Guardrails for PII detection rely on automated mechanisms (Presidio) and may not guarantee detection of all sensitive information.
•Some advanced features (Custom Guardrails) are still in private preview.

AI Guardrails and Fallbacks are not supported on custom model serving endpoints (Only rate limiting and usage tracking are supported for non-route-optimized custom models). AI Guardrails and Fallbacks are not supported for Mosaic AI Agents (yet). When AI Guardrails are used, the request batch size cannot exceed 16. AI Guardrails are not applied to requests and intermediate responses of function calling (only the final output response). Text-to-image workloads are not supported. Any request/response over 1 MB will not be logged in Inference Tables. The AI Guardrails moderation service is limited to regions that support Foundation Model APIs pay-per-token.

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