Sold by: Monte Carlo Data
Deployed on AWS
Data breaks. We ensure your team is the first to know and the first to solve with end-to-end data observability.
4.3
Overview
As businesses increasingly rely on data and AI to power digital products and drive better decision making, it's mission-critical that this data is accurate and reliable. Monte Carlo's Data + AI Observability Platform is an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, business intelligence, and AI tools. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster. Monte Carlo also provides automatic, field-level lineage and centralized data cataloging that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements.
Highlights
- Detect: Detect data quality issues before your stakeholders at each stage of the pipeline
- Resolve: Resolve data issues with out-of-the-box root cause and impact analysis, including end-to-end field-level lineage
- Prevent: Prevent data downtime proactively across your stack
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
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Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Monte Carlo Credit | Monte Carlo's Data Observability Platform Credit | $50,000.00 |
Dimensions summary
This listing uses a single pricing dimension: the Monte Carlo Credit. You buy credits under a contract, and your total cost scales with the number of credits you commit to. Credits fund your use of the Data + AI Observability Platform. As your monitoring needs grow, you add more credits. Because there is one unit type, pricing stays simple: you scale usage up or down by adjusting how many credits you purchase, rather than choosing between separate tiers or plans.
Top-of-mind questions for buyers
A credit is the consumption unit for the Data + AI Observability Platform. You draw down credits as you use platform features and monitors. The vendor documents how credit consumption and chargebacks work, so credits track actual usage rather than a fixed count of users or hosts.
Cost scales with the credits you commit to under contract. As you monitor more data sources or run more monitors, you draw down credits faster. To cover added usage, you purchase more credits. There are no separate tiers to move between, so scaling means adjusting credit quantity.
Credits fund your use of platform features, including data warehouse integrations, monitors, and agent operations. Consumption depends on the volume and type of monitoring you run. The vendor documents credit consumption and chargebacks, letting you attribute usage across teams. Contact the vendor for how specific features meter against your credit balance.
docs.getmontecarlo.com
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Updated weekly
By Monte Carlo Data
By Collibra
By Atlan
Top
10
In Data Governance
Top
10
In Data Catalogs, Data Governance
Top
10
In Data Catalogs, Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
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Negative reviews
AI generated from product descriptions
Machine Learning-Based Anomaly Detection
Uses machine learning algorithms to infer and learn data patterns, proactively identifying data quality issues across data warehouses, data lakes, ETL pipelines, business intelligence, and AI tools.
Root Cause and Impact Analysis
Provides automated root cause identification and impact assessment for data issues with end-to-end field-level lineage tracing.
Field-Level Data Lineage
Delivers automatic field-level lineage mapping that tracks data flow and dependencies across the entire data stack.
Centralized Data Cataloging
Maintains centralized data catalog with visibility into data asset accessibility, location, health status, and ownership information.
Multi-Stack Integration
Monitors and alerts across heterogeneous data environments including data warehouses, data lakes, ETL systems, business intelligence platforms, and AI tools.
AI Governance Framework
Active metadata-based governance with rules, processes and responsibilities to ensure ethical AI practices, mitigate risk, adhere to legal requirements, and protect privacy
Automated Data Lineage
End-to-end lineage tracking providing transparency into data transformation and flow across systems, including both summary-level business lineage and detailed technical lineage
Unified Data Catalog
Multi-cloud and hybrid environment data discovery with business context including data origin, ownership, usage patterns, and access to reports, AI models and data products
Data Quality Automation
Automated monitoring and rule management system for enterprise-wide data quality management replacing manual processes
Privacy and Compliance Workflow
Centralized automation of privacy workflows to operationalize privacy requirements and address global regulatory compliance
Automated Data Discovery and Context Generation
Automatically ingests from entire AWS data estate including Redshift, S3, Glue, Athena, Lake Formation, and SageMaker, generating business context with certified definitions, lineage, ownership, and quality scores in two weeks.
Context Development Lifecycle Management
Runs full development lifecycle including Build, Test, Review, Approve, Deploy, and Learn phases where AI bootstraps context and simulates tests while domain experts resolve ambiguity and approve before deployment.
Multi-Agent Context Delivery Protocol
Delivers unified context to multiple AI agents through MCP Servers using open protocol standards, supporting Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, Cursor, Gemini, and other MCP-compatible tools.
Native AWS Data Platform Integrations
Provides native integrations with Amazon Redshift, S3, Glue, Athena, Lake Formation, and SageMaker Unified Studio, plus support for Snowflake, Databricks, dbt, Airflow, and leading BI platforms.
Continuous Learning Loop with Feedback
Implements compounding learning mechanism where memory, feedback, and traces from every agent interaction improve context quality and accuracy over time.
Standard contract
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Customer reviews
Information Technology and Services
Centralized data reliability that builds confidence
Reviewed on Sep 03, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo gives us confidence in the reliability of our data by making incidents easier to detect, investigate, and understand. I especially like how it centralizes freshness, quality, lineage, and alerting in one place, so teams can quickly see what broke, what was impacted, and where to focus. It helps reduce the time spent manually chasing data issues and makes data reliability feel much more operational and measurable.
What do you dislike about the product?
Monte Carlo is powerful, but it can sometimes feel noisy or hard to tune, especially when monitors generate alerts that are technically correct but not always actionable. The investigation workflows are useful, though they can require context from outside Monte Carlo to fully understand root cause. I’d also like clearer guidance on monitor configuration and prioritization so teams can focus more easily on the highest-impact data reliability issues.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps solve the problem of finding and understanding data quality issues before they become bigger downstream problems. It gives visibility into freshness, volume, schema changes, lineage, and anomalies, which helps us catch broken pipelines or unexpected data changes faster. The main benefit is reduced time spent manually investigating issues. It helps teams understand impact, prioritize the right fixes, and build more trust in the data used for reporting, analytics, and decision-making.
Information Technology and Services
Easy Monitoring Setup with Powerful Troubleshooting and Integrations
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
The Ease of Use and Operations agents, along with the Monitor agents, make monitor setup much easier and more straightforward. The Troubleshoot agent is especially valuable for root cause analysis (RCA) when issues come up. I also like the integration with multiple connectors like Data360 and AI.
What do you dislike about the product?
For retrieving failure records for that particular monitor currently users should be dependent on UI. metadata such as pass/fail status, including these details in data exports would enable the creation of more custom dashboards. better integration with data360 new objects types + informatica/mulesoft connectors
What problems is the product solving and how is that benefiting you?
observability & data quality
Reliable Data Observability
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo has transformed how we manage data reliability & Observability. Before adopting to it , we spent hours chasing broken pipelines and missing records. Now, issues are flagged in real time, with clear incident detection, triage workflows, and root cause analysis the helps us resolve them faster. The data lineage view makes it easy to see downstream impact, and the integrations with our existing stack were smooth. The dashboards give leadership confidence in the accuracy of our reporting, and the freshness and volume monitoring ensure we don't miss silent data issues. It's a platform that saves us time, reduces risk, and build trust in our analytics.
What do you dislike about the product?
Initial setup takes some effort, alerts can be noisy at first, some advanced features like lineage and triage require extra training to fully leverage.
What problems is the product solving and how is that benefiting you?
Monte Carlo solves centralized data quality by giving us proactive alerts and easy to use DQ dimensions., which saves effort and helps us act before issues impact business.
Leisure, Travel & Tourism
Straightforward and Easy to Use
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
I really like how straightforward it is to use. I also like the table where it includes everything, from the old and new primary locations which has really been helpful plus the direct link to the tour, also that we get reports on time.
What do you dislike about the product?
For example, in the menu where you can see the progression of the work (like fixed and in progress etc), I feel like there are too many options, which makes it confusing. Also, regarding the graphs, I think it would be easier if we just kept the table.
What problems is the product solving and how is that benefiting you?
It’s been helping us as a locations team by keeping our eyes on edge cases and letting us identify and solve them.
Venkata R.
Rich, Mature Data Observability That’s Easy to Use and Integrate
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
Rich functionality and maturity in data observability space. Ease of use and easy to integrate with any data sources. MC helps to check our data assets can be trusted. By integrating various tools / pipelines, MC provides single window to monitor our data assets. Its rich UI and functionality ensures that tool can be easily used by developers or end-users. Highly recommended and much needed tool if trustworthy data is essential in an organization.
What do you dislike about the product?
Access model can be improved. For now, only developers access MC. Secondly data quality option can be improved with some additional options e.g. duplicate checks etc.,
What problems is the product solving and how is that benefiting you?
For data products, we have SLAs such as freshness, volume analysis etc., With MC, we dynamically check whether the table contains recent data.