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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Deployed on AWS
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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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Monte Carlo Credit | Monte Carlo's Data Observability Platform Credit | $50,000.00 |
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All fees are non-cancellable and non-refundable except as required by law.
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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
Positive reviews
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Negative reviews
AI generated from product descriptions
Data Quality Monitoring
Machine learning-based monitoring and alerting for data quality issues across data warehouses, data lakes, ETL pipelines, business intelligence, and AI tools
Root Cause Analysis
Automatic root cause identification and impact assessment with end-to-end field-level lineage for data issues
Proactive Issue Detection
Proactive identification of data issues across the data stack before stakeholder notification
Data Lineage and Cataloging
Automatic field-level lineage tracking and centralized data cataloging for data asset accessibility, location, health, and ownership
Multi-Stack Integration
End-to-end observability platform supporting data warehouses, data lakes, ETL systems, business intelligence tools, and AI applications
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 AWS data estate including Redshift, S3, Glue, Athena, Lake Formation, and SageMaker to generate business context with certified definitions, lineage, ownership, and quality scores in two weeks.
Context Development Lifecycle Management
Provides Build, Test, Review, Approve, Deploy, and Learn stages where AI bootstraps context and simulates tests while domain experts resolve ambiguity and approve before deployment.
Multi-Agent Context Delivery Protocol
Delivers unified context through MCP Servers to multiple AI agents including Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, Cursor, and Gemini via a single open protocol.
Native AWS Data Platform Integrations
Natively integrates with Amazon Redshift, S3, Glue, Athena, Lake Formation, and SageMaker Unified Studio, plus Snowflake, Databricks, dbt, Airflow, and leading BI platforms.
Compounding Learning Loop
Continuously improves context quality through memory, feedback, and traces from every agent interaction, enabling the context layer to become smarter with each query.
Standard contract
No
No
Customer reviews
Anonymous
Reliable Anomaly Detection with Learning Curve
Reviewed on Jul 23, 2026
Review provided by G2
What do you like best about the product?
I like how Monte Carlo is self-sufficient and can learn from itself to improve by creating new rules and alerts. It's smart enough to look at data, identify alerts, and create new ones if necessary, which saves a lot of time and work because we don't have to dig into the data ourselves or make our own tools.
What do you dislike about the product?
I think the UI might be a little bit intimidating. There's a lot going on. It's has a lot of information packed, which isn't a bad thing, but to a beginner, it might look a little intimidating.
What problems is the product solving and how is that benefiting you?
Monte Carlo prevents our data from going missing and alerts from going unnoticed. We trust it to detect anomalies, send alerts, and eliminate the manual need to check data, saving us time and effort.
Retail
Monte Carlo Makes Data Quality Monitoring and Troubleshooting Easy
Reviewed on Jul 21, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo is easy to set up and use. Its automated monitoring, troubleshooting agent, data lineage, and alerting make it easy to detect and troubleshoot data quality issues quickly, helping teams maintain confidence in their data.
What do you dislike about the product?
Monte Carlo can be expensive, especially as usage scales. One of the downsides of the tool is it cannot validate data after it is offloaded from Snowflake, which limits end-to-end data quality monitoring across the full pipeline.
What problems is the product solving and how is that benefiting you?
The anomaly detection feature is very effective at identifying unexpected data issues, and the troubleshooting agent helps Tier 1 support quickly triage alerts, reducing investigation time and improving operational efficiency.
Internet
Seamless Data Monitoring and Alerting with Monte Carlo
Reviewed on Jul 21, 2026
Review provided by G2
What do you like best about the product?
I like Monte Carlo's approach and its ability to configure and see a full lineage within my BI stack. It's pretty neat that I can configure one post that will be automatic or set up monitors that are fully configurable by me and my team. I also appreciate how it seamlessly connects with my other orchestration stores. The initial setup was pretty easy and straightforward, which is a big plus.
What do you dislike about the product?
pricing is a bit high
What problems is the product solving and how is that benefiting you?
I use Monte Carlo to track table behaviors and ensure data behaves normally, receiving alerts for anomalies. It helps manage multiple databases by monitoring them actively, so I'm aware of any issues or breaks.
Manufacturing
Continuously Evolving AI Features That Keep Getting Better
Reviewed on Jul 17, 2026
Review provided by G2
What do you like best about the product?
That is a product that is integrating quiet a few AI functionalities in the tool and is continuesly evolving
What do you dislike about the product?
I think there is a possibility of improvement in the way the credits are controlled, dashboard for it so client can have visibility of what is being spent.
What problems is the product solving and how is that benefiting you?
Detecting anomaly detection in near real time way.
Anonymous
Effortless Setup, Useful Alerting, But Alert Noise Needs Refinement
Reviewed on Jul 01, 2026
Review provided by G2
What do you like best about the product?
I like that with Monte Carlo, we don't have to set thresholds manually for anomalies. Instead, we can rely on Monte Carlo to decide what is noteworthy for an alert. I also appreciate that it is pretty much plug and play, working out of the box with very little setup. The setup process is extremely easy, which is one of my favorite parts about the Monte Carlo platform.
What do you dislike about the product?
Noisiness is the biggest challenge. Tuning old alerts so we only get alerted about things that are truly noteworthy and need our attention. It's the biggest downside and thing we fight with.
What problems is the product solving and how is that benefiting you?
Monte Carlo handles data warehouse and DVT jobs, providing observability without the need to set thresholds, as it alerts based on what’s noteworthy, out of the box with minimal setup.