Monte Carlo for technology.

Use reliable data to run internal analytics and develop products more quickly and efficiently.

Trusted by the data teams at

  • Sofi
  • Opentable
  • Intercom
  • Affirm
  • Shutterstock
  • Payjoy
  • Rivian
  • Seatgeek
  • Toast
  • Weights & Biases
  • HubSpot

Learn how saved 50% of engineering time with Monte Carlo.

Data stack


data quality coverage with Monte Carlo compared to testing.


data sets monitored.


  • Lean two-person data team.
  • Over-reliance on manual testing.
  • Lack of visibility across data domains.
  • Lack of stakeholder trust.


  • Integrations with Datadog & PagerDuty for larger incident management workflow for data/engineering Teams.
  • ML-based, opt-in anomaly detection monitors.
  • Central UI to manage data incident resolution

Data observability is more than just data quality. It’s becoming a cultural fit and a cultural approach, how companies or teams are looking into their data. Because [companies] look at data as an essential resource that they want to use for their crucial operations, they understand that having high-quality data is becoming a crucial part of their business. And I hope that this trend will continue moving forward.

Martynas Matimas Senior Data Engineer

Use cases for SaaS.

Win with accurate data.

Avoid wasting time, money, and resources on research fueled by inaccurate data.

Tailor the user journey.

Ensure inventory and order fulfillment data is up-to-date and accurate.

Attract and retain customers.

Ensure data quality coverage can meet growing data volumes and evolving use cases.

Use cases for hardware and networking.

Improve ROI on research.

With lengthy and costly development cycles for hardware products, ensure that data stemming from your testing platforms is always within acceptable thresholds.

Solve HARDware problems.

Identify whether gaps in manufacturing and inventory numbers are truly business issues or actually data issues.

Consolidate field and cloud.

Use information from your devices in the field to proactively maintain or replace faulty hardware for a seamless production experience.

Data observability is the first step before any data incident management steps, including incident response and escalation, can happen.

Manu Raj Senior Director of Data Platform and Analytics

Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.

Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes.

Rich insights enable your team to proactively ensure data quality, and make better infrastructure investment decisions.