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Monte Carlo has raised $241.0M across 4 funding rounds.
Key people at Monte Carlo.
Monte Carlo has raised $241.0M in total across 4 funding rounds.
Monte Carlo offers a data and AI observability platform designed to ensure the reliability and health of an organization's data ecosystem. This comprehensive solution continuously monitors data pipelines and data quality, automatically identifying and alerting users to anomalies, errors, and data downtime across the entire data stack. Its core functionality enables enterprises to proactively discover, diagnose, and resolve data issues, thereby fostering trust in their data assets.
The company was established in 2019 by co-founders Barr Moses and Lior Gavish. Their entrepreneurial journey began with the crucial insight that pervasive data quality issues and "data downtime" significantly impeded businesses from fully realizing the value of their data. They set out to create a system that would address these systemic problems, allowing companies to build greater confidence in their data infrastructure.
Monte Carlo's platform serves enterprise organizations that rely heavily on data for their operations and are looking to integrate artificial intelligence more deeply. The company's overarching vision is to accelerate the world's adoption of trusted data and AI by equipping businesses with the tools necessary to eliminate data downtime. This empowers customers to make more informed decisions with reliable information, driving innovation and efficiency.
Key people at Monte Carlo.
Monte Carlo has raised $241.0M across 4 funding rounds. Most recently, it raised $140.0M Series D in May 2022.
Monte Carlo has raised $241.0M in total across 4 funding rounds.
Monte Carlo's investors include IVP, Accel, AirAngels, Andreessen Horowitz, Aya, B Capital Group, Cedar Capital Group, Cornerstone Venture Partners, Craft Ventures, CRV, Cyberstarts VC, Felicis Ventures.
Monte Carlo is a San Francisco-based technology company founded in 2019 that builds an end-to-end data and AI observability platform to ensure the reliability and accuracy of data pipelines.[1][2][4] It serves data teams at large enterprises like Block, Buzzfeed, Notion, Fox, PepsiCo, Amazon, and American Airlines by monitoring cloud warehouses, lakes, ETL tools, and BI systems for anomalies, downtime, and quality issues using metadata collection, data lineage reconstruction, and machine learning.[1][2][3][4] The platform solves data downtime—missing, inaccurate, or unreliable data—through plug-and-play integrations, automated incident detection, and resolution, enabling trusted data for AI and business decisions; it operates on a pay-as-you-go SaaS model based on monitored tables, with over 400 enterprise customers, 10M tables monitored, and 1,000 incidents resolved daily as of recent metrics.[1][4] Growth has been strong, with ARR nearing $15M in early 2024 (177% YoY), doubled Fortune 500 customers in Q3 2023, and 2025 recognitions including G2's #1 Data Observability Platform for eight quarters and Databricks Data Governance Partner of the Year.[1][2]
Monte Carlo was founded in 2019 by CEO Barr Moses, who coined the term "data observability" and identified a critical gap in monitoring data pipelines amid the rise of modern data stacks.[1][4] Moses and the team launched the company to address the lack of tools guaranteeing data trust, especially as businesses relied on data for digital products and decisions without easy visibility into pipeline health.[2][4] Early traction came from enterprises needing end-to-end coverage across fragmented tools; by 2023, it secured notable customers like Block and Notion, raised $236M from investors including Accel, ICONIQ Growth, GGV Capital, Redpoint, IVP, and Salesforce Ventures, and doubled Fortune 500 adoption in Q3 2023.[1][2] Pivotal moments include defining the data observability market in 2019 and expanding into AI observability by 2025 to tackle GenAI data challenges, evolving from data reliability to comprehensive AI pipeline trust.[1][4]
Monte Carlo stands out in the crowded data quality space through these key strengths:
Monte Carlo rides the explosive growth of data-driven AI and GenAI, where 100% of data leaders face pressure to build AI but only 68% feel ready due to pervasive data downtime plaguing even top enterprises.[4] Its timing aligns perfectly with modern data stacks' complexity—proliferating warehouses, lakes, and AI pipelines—creating market forces like heightened scrutiny post-migrations (e.g., JetBlue's 16-point "Data NPS" gain) and regulatory demands for governance.[2][4][6] By consolidating metadata signals into actionable insights, it accelerates data adoption, influences the ecosystem through 400+ enterprises standardizing observability (e.g., integrations with Databricks, Snowflake), and sets the category standard its CEO defined, enabling reliable GenAI at scale amid 2025's AI maturity push.[1][2][4]
Monte Carlo is primed to dominate as the go-to for enterprise data + AI observability, expanding beyond detection to full AI pipeline trust with faster scaling and deeper ML features.[3][4] Trends like GenAI proliferation, multi-cloud complexity, and compliance mandates will fuel growth, potentially pushing ARR past $50M+ by 2026 via wins in regulated sectors and new AI-specific modules.[1][2] Its influence will evolve from niche reliability fixer to ecosystem enabler, powering more trusted AI outcomes at Fortune 500 scale and cementing its role in making data downtime obsolete—just as it started by naming the problem.
