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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Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Monte Carlo Credit | Monte Carlo's Data Observability Platform Credit | $50,000.00 |
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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
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
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Customer reviews
Praneetha Marini
Automated data monitors have reduced noise initially but have greatly boosted data trust
Reviewed on Jul 02, 2026
Review from a verified AWS customer
What is our primary use case?
Monte Carlo is a great tool and an excellent AI tool that helps in automated monitoring and lineage that quickly boosts data trust in my organization. The biggest value for us has been Monte Carlo 's automated monitors. Instead of handwriting freshness and volume checks for hundreds of Snowflake tables, the ML-based detectors learn normal patterns and alert us on anomalies automatically. This caught a stalled pipeline load hours before our business stakeholders would have and saved us from reporting on stale numbers. The dbt and Snowflake integrations were quick to connect and are a core part of our daily workflow. End-to-end lineage is the feature I rely on most. When an alert fires, I can trace it from the downstream table back through the dbt models to the exact upstream source in a couple of clicks, which has cut our root cause investigation time from hours to minutes.
Monte Carlo has helped us solve the critical problem of data downtime by replacing manual, tedious data quality tests with automated machine learning monitoring and end-to-end data lineage mapping.
What is most valuable?
I love the end-to-end lineage, which I rely on most because when an alert fires, I can trace it from the downstream table back through the dbt models to the exact upstream source in a couple of clicks, which has helped cut our root cause investigation time from hours to minutes. I also love the automated monitors which help us instead of handwriting freshness and volume checks for hundreds of Snowflake tables, the machine learning-based detectors learn normal patterns and alert us on anomalies automatically.
On the user interface and user experience, the incident view and Slack alerting keep the whole data team in the loop without anyone having to log in and dig around. The user interface is very good, which Monte Carlo is always known for. Integrations are good, at least for the options we use in our organization. Performance is good. The pricing is a little expensive compared to other alternatives like DataDog, but it is manageable for a product-based company like us. Support has always been proactive and very responsive. Auto intelligence helps detect the right frequency for data refresh. Overall, the customer support is very responsive and helpful 24/7.
What needs improvement?
The automated monitors can also be noisy at first. During the initial learning period, we saw a fair number of false positive alerts, which meant manual tuning and some effort to set sensible thresholds before the signal-to-noise ratio improved.
The user interface also has a bit of a learning curve for newer team members, especially those who do not use it every day.
For how long have I used the solution?
Monte Carlo has been around my organization for the past six years.
What do I think about the stability of the solution?
Monte Carlo is stable because I have not experienced any downtime or lagging issues.
What do I think about the scalability of the solution?
Monte Carlo has handled our growth volume without any issues. It is a very scalable tool for my organization's use.
How are customer service and support?
Support has always been proactive and very responsive. The support team has been very proactive and solution-oriented.
Which solution did I use previously and why did I switch?
I was using SODA, DataDog, and Anomalo before Monte Carlo.
How was the initial setup?
We did not face any hiccups. It was very easy and time-saving. We never hit any snags. It was straightforward, easy, and smooth.
What about the implementation team?
The implementation proceeded seamlessly and smoothly according to my experience.
What was our ROI?
In terms of return on investment, the time we save on building and maintaining custom data quality checks and on faster incident resolution has easily justified the cost. Onboarding and support were smooth. The team helped us get our key tables monitored quickly. An unexpected benefit has been how the lineage and monitoring have improved data trust across our organization so that stakeholders rely on the data more and we field fewer questions about whether the numbers are correct.
What's my experience with pricing, setup cost, and licensing?
The process was very straightforward and simple. Everything was kept simple and easy to understand. We did not have any challenges purchasing Monte Carlo through AWS .
Which other solutions did I evaluate?
Anomalo and DataDog were alternatives I considered.
What other advice do I have?
Overall, Monte Carlo has helped us solve a real data quality and observability gap. With Monte Carlo's automated monitoring, we now catch many of these issues proactively, often before they reach downstream consumers. The support team has been very solid. Monte Carlo is a great tool if you are looking for a quicker solution and quicker turnaround to spot the sources of issues and fix data mismatches. I rate this product a 5 out of 5.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Hemanth Rama Kumar Garre
Automated monitoring has reduced manual checks and flags data incidents with precise alerts
Reviewed on Jul 01, 2026
Review provided by PeerSpot
What is our primary use case?
Monte Carlo is a data observability tool that can help track data volume changes and flag incidents if there is any unusual activity on a table, data models, or any jobs. For instance, if something unusual happens on an ETL job, it will raise an incident and send alerts via integrated platforms like Teams application and emails.
Monte Carlo also helps in monitoring applications like ServiceNow , Jira , and Snowflake by establishing connectivity with them. The solution is beneficial for various scenarios, such as when sudden deviations from normal data patterns occur. For instance, if a cloud data warehouse like Snowflake experiences an unexpected change, Monte Carlo flags incidents immediately. Its AI agent enhances troubleshooting by providing analytical insights. Besides, integrations with other applications, such as ServiceNow and Jira , ensure an end-to-end alerting mechanism.
On an account level, it builds monitoring capabilities across vast data sources. It also supports triggering alerts via ServiceNow by using webhooks, allowing associates to take immediate action. Recently, Monte Carlo introduced an AI agent to aid in troubleshooting, ensuring only main production layers are analyzed. This backend troubleshooting does not grant complete access to all layers but remains highly effective in problem-solving.
What is most valuable?
The most valuable aspect of Monte Carlo's observability feature is its automation of the monitoring processes, which eliminates the need for an individual to manually monitor numerous models or tables. It flags issues with precision and ensures proactive resolutions only on the affected components, thereby enhancing efficiency vastly.
Monte Carlo's scalable nature further bolsters its value proposition. Once integrations are established, future model updates are automatically captured without additional setup costs or actions. Given that the data platform's needs perpetually grow, Monte Carlo provides seamless adaptability.
The software manages data auditing and monitoring across platforms like Snowflake with its robust algorithms. By analyzing metadata over an extended period, Monte Carlo's flagging system, based on deviations from historical averages, ensures precise incident identification. Its ability to utilize custom monitors further extends its value, as users can implement logic-based rules and receive targeted alerts.
The introduction of a performance tab greatly aids optimization, visually displaying runtime graphs to identify model issues quickly. Monte Carlo's near perfection in accuracy ensures every flag corresponds to a genuine issue, attested by its consistent performance over time.
Monte Carlo's AI troubleshooting agent, which mimics human oversight through tiered analysis, provides ample support in incident resolution. This ensures incidents are well-documented, analyzed, and tackled despite limited access to all data layers.
What needs improvement?
While Monte Carlo frequently updates its UI platform, the changes might pose adaptation challenges for long-time users, as the continual evolution is not always intuitive. Additionally, occasional latency hampers efficient access during critical incidents, leading to potential misses of high-priority alerts.
In terms of data accessibility, granting read-level access to non-sensitive data layers would enhance insights for users significantly. Users currently experience limitations in data layer access, such as bronze and silver data layers containing essential business logics.
Sharing metadata with clients could bolster Monte Carlo's analytical capacity, allowing clients to draw deeper insights from shared data.
For how long have I used the solution?
I have been using this solution for four years.
What do I think about the stability of the solution?
I have never noticed something which Monte Carlo flagged that was not relevant to the issue. The accuracy is 100% from what I have noticed. I have never noticed any issues where something is actually happening on a data warehouse, but Monte Carlo still flags it as an issue. I have not seen these kinds of scenarios with Monte Carlo while working. The accuracy is consistently 100%.
What do I think about the scalability of the solution?
I can say Monte Carlo's scalability is at approximately 90%. Once we establish the integration in the future, we can enable an option to capture the future models. We do not need to work again and again. Once we establish the connectivity, we do not need to work on adding new tables. It should be a one-time effort. This approach is more scalable. Data platforms will not stop growing from their beginning state. They will always expand. Monte Carlo demonstrates scalability in adopting new models automatically, which should serve organizations well.
How are customer service and support?
We have connected to Monte Carlo regarding a few things. When we are establishing any new connection and face any issues, we reach out to them for technical support.
The technical support is fine. They will respond within two to three hours, but the solution may take some time, ranging from 24 to 48 hours. Technical support is satisfactory from them. Even though the product application team is not that much larger, they are still giving better support.
How was the initial setup?
When onboarding Monte Carlo, you can review the documentation for whatever source you want to connect from Monte Carlo. There you can find more use cases.
What other advice do I have?
When I started working with Monte Carlo, I did not see as many features as currently exist. Previously, the product did not have troubleshooting agents. Also, when I started working, it did not have a performance tab. The performance tab shows performance in a graphical way, allowing me to easily review the model and check the average run time. If there is any unexpected spike that happens for a specific day, I can see that.
For technical support, I would give it eight out of ten. Currently, for my account, we are not giving all layers access to Monte Carlo. We are only giving access to the main golden layer. We are not giving access to the bronze layer and silver layer because they contain business logics.
I am not certain about billing information because that is at an account level, and clients would be aware of billing information rather than myself.
My overall rating for this product is eight out of ten.
Mukesh S.
Drastically reduced our data downtime and pipeline issues
Reviewed on Jun 30, 2026
Review provided by G2
What do you like best about the product?
What I like best is how seamlessly Monte Carlo integrates with our modern data stack (Snowflake and dbt) to provide instant data observability. The automated, ML-driven lineage is incredibly accurate, and getting proactive alerts in Slack allows our engineering team to catch data downtime and broken pipelines before our business stakeholders notice them.
What do you dislike about the product?
Sometimes the initial setup can lead to a bit of alert fatigue. If thresholds aren't finely tuned, we get too many Slack notifications for minor schema changes or expected data volume fluctuations, which takes some time to clean up.
What problems is the product solving and how is that benefiting you?
We used to struggle with unexpected schema changes and broken data pipelines that went unnoticed until business stakeholders reported them. Since implementing Monte Carlo, the automated data observability and Slack alerts catch these anomalies instantly. This has drastically reduced our data downtime and restored confidence in our downstream dashboards.
Vaishnavi K.
Monitoring That Outperforms Manual Checks
Reviewed on Jun 30, 2026
Review provided by G2
What do you like best about the product?
monitoring is the better than manual ones.
What do you dislike about the product?
sometimes the page doesn't load properly
What problems is the product solving and how is that benefiting you?
we are using it for dq
Ashokkumar T.
Auto Intelligence Nails the Right Data Refresh Cadence
Reviewed on Jun 29, 2026
Review provided by G2
What do you like best about the product?
UI is far good. Which Montecarlo is always known for.
Integrations are good atleast for the options what we use in our org.
Performance is good.
Little Expensive for small sized Org. Manageble for a product based company like us.
Support we have not used much. Onboarding was pretty straight.
Auto Intelligence helps detect the right frequency for data refresh. When manual refresh settings aren’t accurate and end up creating noise, Monte Carlo suggests the right refresh cadence with its in-built intelligence.
Note: Formatted by AI, but not generated by AI
Integrations are good atleast for the options what we use in our org.
Performance is good.
Little Expensive for small sized Org. Manageble for a product based company like us.
Support we have not used much. Onboarding was pretty straight.
Auto Intelligence helps detect the right frequency for data refresh. When manual refresh settings aren’t accurate and end up creating noise, Monte Carlo suggests the right refresh cadence with its in-built intelligence.
Note: Formatted by AI, but not generated by AI
What do you dislike about the product?
Complete Monitor as Service. We would need option to host on the companies hosted version.
What problems is the product solving and how is that benefiting you?
Quicker turnaround to spot the source of issue and fixing a data mismatch