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
Near-Zero Setup Redshift Monitoring with Proactive Slack/PagerDuty Alerts
Reviewed on Nov 05, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo monitors our Redshift warehouse with almost no setup. The automatic table monitors catch freshness, volume, and schema changes across our Kafka and ODS tables, so the Data Engineering team can hear about a broken pipeline before the analysts do. Alerts route to Slack and PagerDuty, which fits cleanly into our on-call rotation. Other teams (Search, Ads, Tracking) can also build their own metric monitors and custom SQL monitors on the same platform, so data quality checks aren’t all on our team. I also like the MCP server: from Claude Code, I can triage an alert, check lineage, and review the monitors without leaving the terminal.
What do you dislike about the product?
Some monitors can get noisy, and tuning them still takes a lot of manual effort. For example, one custom SQL monitor on our tracking events triggered 36 incidents in 30 days, and the team has to keep adjusting thresholds by hand to reduce alert fatigue. Metric monitors on our larger Redshift tables also sometimes time out or fail with errors, and it isn’t always obvious from the UI what caused the failure. It’s also easy to end up with monitors that effectively check nothing. A few of ours still point to tables that no longer exist and show “0 tables,” but there’s no warning to the owner. On top of that, many monitors don’t have a notification channel configured, so an anomaly can sit unnoticed. Better default settings for routing and some kind of cleanup or guardrails would help a lot.
What problems is the product solving and how is that benefiting you?
Monte Carlo has solved our problem of discovering bad data too late. In the past, a stale Kafka load or a schema change in a source table could make it all the way to dashboards and models before anyone on the Data Engineering team noticed. Now we have freshness, volume, and schema monitors on our Redshift tables that alert us in Slack and PagerDuty, which lets us fix the pipeline before analysts and product teams rely on the data. It has also helped spread data quality ownership across the company. The Search, Ads, and Tracking teams monitor their own business metrics—such as ranker scores, ad loss rate, and iOS tracking gaps—so they can catch issues in their own domain without coming to us for custom checks. The end result is less time spent on incident investigation and more trust in the numbers people use to make decisions.
Retail
Slack Integration and AI Agent Make Monitoring and Troubleshooting Easy
Reviewed on Oct 30, 2026
Review provided by G2
What do you like best about the product?
the integration with Slack is very useful in monitoring our services, likewise the addition of AI agent is very helpful for problem solving and troubleshooting issues.
What do you dislike about the product?
the threshold setting options are somehow limited and do not always allow us to capture our use cases
What problems is the product solving and how is that benefiting you?
it helps us to monitor the quality of our data in Snowflake
Pharmaceuticals
User-Friendly Data Observability with Fast, Knowledgeable Support
Reviewed on Oct 30, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo Delivers, user friendly solution for the observability. I tac be used both from UI and MaC, which is big advantage. The UI is sometimes slow. AI agents takes pretty much time when there is many monitors in the domain. Technical support is fast, direct support from Laura is very satisfying. She has deep technical knowledge with quick rising up and stepping into issue resolution.
What do you dislike about the product?
Loading time, UI performance, the timing for AI agents when domain contains many monitors.
What problems is the product solving and how is that benefiting you?
Monitoring the data in the data pipelines.
Media Production
Powerful Trend Analysis and Time Series Quality Checks
Reviewed on Oct 28, 2026
Review provided by G2
What do you like best about the product?
Trend analysis and time series Quality checks
What do you dislike about the product?
UI is not that useful. But with coding agents I am able to create any QC monitors easily
What problems is the product solving and how is that benefiting you?
We can find trend outliers so easily
Computer Software
Intuitive AI Monitoring That Keeps Our Schema Changes Under Control
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
It is very helpful to control schema changes or even large additions or deletions. Monitors with AI are so intelligent and provide confidence on "if something happens, I'll be notified". If you configure it properly, you can sleep well :)
The UI is super intuitive and easy to set. You can also link it with pager duty to trigger alarms so it is a good quality advisor.
The UI is super intuitive and easy to set. You can also link it with pager duty to trigger alarms so it is a good quality advisor.
What do you dislike about the product?
The only thing that I can dislike a little bit is how verbose Monte Carlo is if you configure too much things. If you have a big DWH with hundreds of tables, and you configure hundreds of monitors... the alert channel in slack could be a mess. But it depends more on how good you are setting the correct and needed monitors.
What problems is the product solving and how is that benefiting you?
QA. Basically being sure that we don't have massive insertions without knowing, schema changes.. or even X days without data. It is super helpful in this way.