Your AI outputs are only as good as the data behind them

Monte Carlo gives teams end-to-end AI model observability and AI model monitoring — from the data feeding your models to the outputs they produce — so you always know what you can trust and why.

Build AI you can actually stand behind

Trust the data your models run on

Monte Carlo monitors the pipelines and data sources feeding your ML and AI models so unreliable inputs don't silently corrupt outputs — before they reach a dashboard or a decision.

Catch model issues before stakeholders do

Get alerted to data and AI incidents across your entire pipeline — from source tables to model outputs — and resolve them fast, before they surface in a report or a meeting.

Spend less time chasing bad data, more time generating insights

Teams using Monte Carlo recover 30–50% of the time they'd otherwise spend investigating data and model issues — and redirect it toward work that moves the business.

Complete visibility across your entire data + AI pipeline

From the raw data feeding your models to the insights they generate, Monte Carlo monitors every step — so nothing slips through undetected.

Govern your agents and models

Assign owners, create models, streamline incident management, and troubleshoot in minutes with automated monitoring and lineage and the world's first observability agents.

Resolve AI incidents quickly

Knowing when AI breaks is one thing—resolving it is another. Monte Carlo equips data + AI teams with the context they need, to triage and resolve incidents fast.

Understand downstream dependencies at a glance

Leverage Monte Carlo’s catalog view to evaluate downstream dependencies before making field, table, or schema changes that might impact downstream users.

Easily identify your most critical business assets

Monte Carlo automatically identifies the most utilized BI views and dashboards in your organization to help your team ensure that they’re reliable and performant around the clock.

Reliability designed for the enterprise

Our customers scale trust, reduce risk, and deliver better business outcomes. See how you can too.

How JetBlue Improved Internal “Data NPS” By 16 Points YoY

The Challenge:

When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage.

The Solution:

Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes.

Nasdaq's Journey to Reliability with Monte Carlo

The Challenge:

Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable?

The Solution:

The team deployed Monte Carlo to monitor its entire data lake via a multi-step deployment.

Get started fast—scale faster.

Fast setup—even faster time to value. Connect to Monte Carlo in seconds, start monitoring out of the box and automatically scale with your environment.