Monte Carlo

Monte Carlo is a San Francisco-based software company that sells data observability (also described as data reliability) software to enterprises. Its platform monitors data pipelines and data quality across cloud data warehouses, data lakes, ETL tools and BI systems, using metadata collection, automated field-level lineage reconstruction and machine learning to detect anomalies, schema and structural changes, freshness/quality failures and other forms of "data downtime," then alerting teams and helping them diagnose and resolve incidents.

The company positions its product as the data equivalent of application performance monitoring tools such as Datadog and New Relic: rather than monitoring running applications, it gives data leaders and business stakeholders end-to-end visibility into the health of data powering dashboards, decisions and data products in near real time. Integrations are described as plug-and-play API-based connectors covering warehouses such as Snowflake and BigQuery, along with lakes, transformation tooling and BI. Later descriptions extend the positioning to AI observability, framing the platform as a way to establish trust in data feeding AI systems.

Monte Carlo cites market context including roughly $39.2 billion spent on cloud databases in the prior year and a Gartner estimate that data downtime and poor data quality cost the average organization $12.9M per year, and its own research finding roughly one data incident per 15 tables in a typical environment.

Founding story

Barr Moses and Lior Gavish, a married couple, founded Monte Carlo in 2019 on the observation that pervasive data quality problems and "data downtime" prevented companies from trusting and fully using their data as modern data stacks proliferated. Moses is credited in one profile with coining the term "data observability." The company began selling its product in 2020.

Business model

B2B enterprise software sold as a SaaS platform. One aggregator profile describes a pay-as-you-go model priced on the number of monitored tables. Growth as of the 2022 Series D was said to come mostly from new customer additions rather than upsells, with net retention described by the CTO as trending positively but early.

Recurring software subscription revenue (ARR); pricing described in one profile as pay-as-you-go based on the number of tables monitored. The company disclosed only percentage growth figures (revenue more than doubling each quarter and up 800% year over year as of May 2022) rather than absolute revenue at that time.

Traction

At the May 2022 Series D the company reported hundreds of paying customers (Forbes cited 150 customers), 100% customer retention in 2021, revenue more than doubling every quarter since August 2021 and an 800% year-over-year revenue increase, and growth from 20 to 120 employees in 20 months. Fox reported that Monte Carlo accounts for roughly 10-15% of its tens of millions of dollars of data infrastructure spend and is used to ensure reliability of 4,000 data sources. A later third-party profile cites over 400 enterprise customers, 10M tables monitored and about 1,000 incidents resolved daily.

Latest developments

The most recent well-documented event is the May 2022 announcement of a $135M Series D led by IVP at a $1.6B valuation, closed in January 2022, making Monte Carlo the first data observability company to reach a billion-dollar valuation. The CEO said the round provided about five years of runway and planned to double or triple the 120-person team within a year, with product investment shifting from detection toward prevention and resolution. A later aggregator profile reports over 400 enterprise customers, 10 million tables monitored, roughly 1,000 incidents resolved daily, ARR nearing $15M in early 2024 (177% year-over-year), and expansion into AI observability by 2025.