# Great Expectations vs Monte Carlo Comparison
---
## AI Generated Summary
- **G2 reviewers report** that Monte Carlo excels in user experience, particularly highlighting its **intuitive setup** and **automated monitoring**. Users appreciate how quickly insights into data begin to flow, thanks to the helpful guidance from the Monte Carlo team.
- **Users say** that Great Expectations shines in its **flexibility** as a Python library, allowing data professionals to define their own expectations for data validation. This feature helps users focus more on data analysis rather than worrying about data quality.
- **Reviewers mention** that Monte Carlo's ability to **self-learn** and create new rules and alerts is a significant time-saver. This capability is particularly beneficial for teams looking to maintain confidence in their data quality without constant manual oversight.
- **According to verified reviews** , Great Expectations is praised for its **ease of use** , with users noting that it simplifies data quality management. This user-friendly interface allows teams to efficiently validate and verify data, bridging gaps between different teams.
- **G2 reviewers highlight** that while Monte Carlo has a strong overall satisfaction score, it faces challenges in specific areas like **real-time analytics** , where Great Expectations outperforms it significantly. Users appreciate Great Expectations' robust analytics capabilities, which enhance their data quality management processes.
- **Users report** that Monte Carlo provides excellent **support quality** , with a score that reflects its commitment to helping users troubleshoot data quality issues quickly. In contrast, Great Expectations, while still effective, has received slightly lower marks in this area, indicating room for improvement in user support.



---
## Frequently Asked Questions

### What is the difference between Monte Carlo vs Great Expectations?

Great Expectations stands out for higher reviewer satisfaction on setup and requirements, while Monte Carlo is favored for its integrations and alerting system.

| [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) | [Great Expectations](https://www.g2.com/products/great-expectations/reviews) |
| --- | --- |
| 4.3/5 (536 reviews) | 4.5/5 (11 reviews) |
| — | — |
| 8.2 | 9.2 |
| 9.0 | 8.5 |
| Alerts (98 all-time mentions) | Ease of Setup (recent reviews) |



### How do the pricing models of Monte Carlo and Great Expectations compare?

Great Expectations is rated higher for price and value satisfaction, while Monte Carlo reviewers frequently cite high cost as a trade-off.

- **Monte Carlo:** In recent reviews, many cite Monte Carlo as expensive, especially as usage scales, and note the need to monitor costs and credit consumption.
- **Great Expectations:** Reviewers highlight that Great Expectations is open source and available at no cost, making it accessible for organizations seeking value.
- **Switching reasons:** Reviewers mention switching to Great Expectations for its open-source model and lower cost of ownership.



### What are the best alternatives to Monte Carlo and Great Expectations?

Soda, Acceldata, and Anomalo are the top three alternatives to Monte Carlo and Great Expectations.

| Product | G2 Rating (reviews) | Pricing Insight | Top Reviewer-Cited Strength |
| --- | --- | --- | --- |
| [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) | 4.3/5 (536 reviews) | Reviewers cite high cost, especially at scale | Alerts (98 all-time mentions) |
| [Great Expectations](https://www.g2.com/products/great-expectations/reviews) | 4.5/5 (11 reviews) | Open source, no cost to use | Ease of Setup (recent reviews) |
| [Soda](https://www.g2.com/products/soda/reviews) | 4.4/5 (55 reviews) | — | Data quality monitoring (recent reviews) |
| [Acceldata](https://www.g2.com/products/acceldata/reviews) | 4.4/5 (56 reviews) | — | Enterprise scalability (recent reviews) |
| [Anomalo](https://www.g2.com/products/anomalo/reviews) | 4.4/5 (44 reviews) | — | Anomaly detection (recent reviews) |



### Which AI Agent Observability features should I prioritize when comparing Monte Carlo and Great Expectations?

Buyers should prioritize integrations, alerting and monitoring, ease of setup, data lineage, and automation when comparing Monte Carlo and Great Expectations.

- **Integrations:** Monte Carlo has 45 all-time mentions for integrations, with 99 reviews citing Slack, 33 citing Snowflake, and 11 citing PagerDuty; Great Expectations supports Snowflake integration in recent reviews.
- **Alerting and Monitoring:** Monte Carlo is cited for alerts (98 all-time mentions) and monitoring (92 all-time mentions); Great Expectations is noted for automated profiling and validation in recent reviews.
- **Ease of Setup:** Great Expectations scores 9.2, Monte Carlo 8.2.
- **Data Lineage:** Monte Carlo has 46 all-time mentions for data lineage; Great Expectations is cited for data documentation and profiling in recent reviews.
- **Automation:** Monte Carlo has 34 all-time mentions for automation; Great Expectations is noted for automating data quality checks in recent reviews.



### What are the pros and cons of Monte Carlo vs Great Expectations?

Monte Carlo's headline strength is its alerting and monitoring system, while Great Expectations is praised for ease of setup and open-source accessibility.

- **Monte Carlo strengths:** Alerts (98 all-time mentions), monitoring (92), integrations (45), data lineage (46), automation (34), ease of use (104), and easy setup (44).
- **Monte Carlo trade-offs:** Alert overload (57 all-time mentions), alert management complexity (58), high cost, and a learning curve for new users.
- **Great Expectations strengths:** Ease of setup (9.2 score), open-source availability, automated profiling, and strong documentation in recent reviews.
- **Great Expectations trade-offs:** Requires technical knowledge for setup, limited support for some data sources, and ongoing maintenance needs cited in recent reviews.



### Is Monte Carlo or Great Expectations better for small businesses?

Great Expectations is a better fit for small businesses due to its open-source model and ease of setup.

- **Monte Carlo:** No largest segment data available; reviewers note high cost as a barrier for small organizations.
- **Great Expectations:** No largest segment data available; reviewers highlight open-source accessibility and ease of setup as advantages for small teams.



### Which AI Agent Observability platform has better integrations?

Monte Carlo is favored for integrations, with extensive reviewer-cited support for Slack, Snowflake, and PagerDuty.

- **Monte Carlo:** Slack integration (99 all-time mentions), Snowflake (33), PagerDuty (11); reviewers consistently highlight integration breadth and ease.
- **Great Expectations:** Snowflake integration is supported in recent reviews; reviewers note Python-based extensibility for pipeline integration.



### How do Monte Carlo and Great Expectations compare on customer support?

Monte Carlo and Great Expectations are rated within 0.5 points on Quality of Support, with Monte Carlo slightly ahead at 9.0 versus 8.5.

- **Monte Carlo:** Reviewers frequently cite responsive onboarding and helpful support, with 9.0 Quality of Support score.
- **Great Expectations:** Reviewers describe support as helpful and documentation as strong, with an 8.5 Quality of Support score.



### Which is easier to implement, Monte Carlo or Great Expectations?

Great Expectations leads on Ease of Setup with a 9.2 score versus Monte Carlo's 8.2.

- **Monte Carlo:** Reviewers describe setup as straightforward but note a learning curve for advanced features and initial alert tuning.
- **Great Expectations:** Reviewers highlight easy setup, especially for Python users, and strong documentation supporting onboarding.



### Which product has better Integrations?

Monte Carlo is the stronger choice for integrations, with higher reviewer-cited mention counts and broader third-party tool support.

- **Monte Carlo:** Integrations (45 all-time mentions), Slack (99), Snowflake (33), PagerDuty (11); reviewers highlight integration breadth and ease.
- **Great Expectations:** Snowflake integration is supported in recent reviews; reviewers note Python-based extensibility for pipeline integration.



| | Great Expectations | Monte Carlo | 
|---|---|---|
| **Star Rating** | 4.5 out of 5 | 4.3 out of 5 | 
| **Total Reviews** | 11 | 549 | 

---
## Top Pros & Cons

### Great Expectations

**Not enough data**

### Monte Carlo

Pros:
- Ease of Use (104 reviews)
- Alerts (98 reviews)

Cons:
- Alert Management (58 reviews)
- Alert Overload (57 reviews)

---
## Ratings Comparison
| Rating | Great Expectations | Monte Carlo | 
|---|---|---|
  | **Meets Requirements** | 9.2 (11 reviews) | 8.4 (490 reviews) | 
  | **Ease of Use** | 9.1 (9 reviews) | 8.3 (500 reviews) | 
  | **Ease of Setup** | 9.2 (6 reviews) | 8.2 (360 reviews) | 
  | **Ease of Admin** | 8.3 (6 reviews) | 8.5 (167 reviews) | 
  | **Quality of Support** | 8.5 (10 reviews) | 9.0 (442 reviews) | 
  | **Has the product been a good partner in doing business?** | 8.6 (6 reviews) | 9.3 (171 reviews) | 
  | **Product Direction (% positive)** | 10.0 (10 reviews) | 8.8 (473 reviews) | 

---
## Pricing

### Great Expectations

#### Entry-Level Pricing

No pricing available

#### Free Trial

No information available

### Monte Carlo

#### Entry-Level Pricing

No pricing available

#### Free Trial

No information available

---
## Features Comparison By Category

### Database Monitoring

| Product | Score | Reviews |
|---|---|---|
| **Great Expectations** | N/A | N/A |
| **Monte Carlo** | 7.6/10 | 269 |

#### Functionality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Monitoring** | Not enough data | 9.0 (265 reviews) | 
| **Alerting** | Not enough data | 8.8 (266 reviews) | 
| **Logging** | Not enough data | 7.8 (238 reviews) | 
| **Response Time** | Not enough data | 8.3 (246 reviews) | 
| **Reporting** | Not enough data | 7.7 (243 reviews) | 
| **Data Visualization** | Not enough data | 7.4 (243 reviews) | 
| **Performance Monitoring** | Not enough data | Not enough data | 
| **Real-Time Monitoring** | Not enough data | Not enough data | 
| **Server Monitoring** | Not enough data | Not enough data | 
| **Real-Time Reporting** | Not enough data | Not enough data | 
| **Uptime Reporting** | Not enough data | Not enough data | 
| **Transaction Monitoring** | Not enough data | Not enough data | 
| **Real-Time Data** | Not enough data | Not enough data | 

#### Agentic AI - Database Monitoring

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Autonomous Task Execution** | Not enough data | 7.1 (13 reviews) | 
| **Multi-step Planning** | Not enough data | 6.9 (13 reviews) | 
| **Cross-system Integration** | Not enough data | 6.8 (13 reviews) | 
| **Adaptive Learning** | Not enough data | 7.3 (14 reviews) | 
| **Natural Language Interaction** | Not enough data | 6.8 (12 reviews) | 
| **Proactive Assistance** | Not enough data | 6.5 (13 reviews) | 
| **Decision Making** | Not enough data | 7.1 (13 reviews) | 
| **Third-Party Integrations** | Not enough data | Not enough data | 
| **Capacity Planning** | Not enough data | Not enough data | 

#### Additional Functionality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Resource Management** | Not enough data | Not enough data | 
| **Anomaly Detection** | Not enough data | Not enough data | 
| **Visual Analytics** | Not enough data | Not enough data | 
| **Remote Monitoring & Management** | Not enough data | Not enough data | 
| **Secure Data Storage** | Not enough data | Not enough data | 
| **Dashboard** | Not enough data | Not enough data | 
| **Generative AI** | Not enough data | Not enough data | 
| **Configuration Management** | Not enough data | Not enough data | 
| **API** | Not enough data | Not enough data | 
| **User Management** | Not enough data | Not enough data | 
| **Capacity Management** | Not enough data | Not enough data | 
| **Diagnostic Tools** | Not enough data | Not enough data | 
| **Dependency Tracking** | Not enough data | Not enough data | 
| **Troubleshooting** | Not enough data | Not enough data | 
| **Reporting & Statistics** | Not enough data | Not enough data | 
| **Reporting/Analytics** | Not enough data | Not enough data | 
| **Predictive Analytics** | Not enough data | Not enough data | 
| **Audit Management** | Not enough data | Not enough data | 
| **Real-Time Notifications** | Not enough data | Not enough data | 
| **Application Management** | Not enough data | Not enough data | 
| **Data Storage Management** | Not enough data | Not enough data | 
| **Application-Level Analysis** | Not enough data | Not enough data | 
| **Multitenancy** | Not enough data | Not enough data | 
| **Query Analysis** | Not enough data | Not enough data | 
| **Performance Management** | Not enough data | Not enough data | 
| **Access Controls/Permissions** | Not enough data | Not enough data | 
| **Compliance Management** | Not enough data | Not enough data | 
| **Historical Trend Analysis** | Not enough data | Not enough data | 
| **Alerts/Notifications** | Not enough data | Not enough data | 
| **Summary Reports** | Not enough data | Not enough data | 
| **AI Copilot** | Not enough data | Not enough data | 
| **Event Logs** | Not enough data | Not enough data | 
| **Dashboard Creation** | Not enough data | Not enough data | 
| **Automated Discovery** | Not enough data | Not enough data | 
| **Prioritization** | Not enough data | Not enough data | 
| **Issue Tracking** | Not enough data | Not enough data | 
| **Activity Dashboard** | Not enough data | Not enough data | 
| **Performance Metrics** | Not enough data | Not enough data | 
| **Resource Optimization** | Not enough data | Not enough data | 
| **Real-Time Analytics** | Not enough data | Not enough data | 
| **Status Tracking** | Not enough data | Not enough data | 

### DataOps Platforms

| Product | Score | Reviews |
|---|---|---|
| **Great Expectations** | N/A | N/A |
| **Monte Carlo** | 7.6/10 | 59 |

#### Data Management

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Data Integration** | Not enough data | 8.6 (53 reviews) | 
| **Metadata** | Not enough data | 8.4 (48 reviews) | 
| **Self-service** | Not enough data | 8.6 (53 reviews) | 
| **Automated workflows** | Not enough data | 8.0 (51 reviews) | 

#### Agentic AI - DataOps Platforms

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Autonomous Task Execution** | Not enough data | 7.6 (7 reviews) | 
| **Multi-step Planning** | Not enough data | 6.7 (6 reviews) | 
| **Cross-system Integration** | Not enough data | 6.9 (6 reviews) | 
| **Adaptive Learning** | Not enough data | 7.4 (7 reviews) | 
| **Decision Making** | Not enough data | 6.9 (6 reviews) | 

#### Analytics

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Analytics capabilities** | Not enough data | 7.9 (51 reviews) | 
| **Dasboard visualizations** | Not enough data | 7.7 (48 reviews) | 

#### Monitoring and Management

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Data Observability** | Not enough data | 9.2 (58 reviews) | 
| **Testing capabilities** | Not enough data | 7.7 (49 reviews) | 

#### Cloud Deployment

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Hybrid cloud support** | Not enough data | 7.5 (44 reviews) | 
| **Cloud migration capabilities** | Not enough data | 7.1 (42 reviews) | 

#### Generative AI

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **AI Text Generation** | Not enough data | 6.3 (35 reviews) | 
| **AI Text Summarization** | Not enough data | 6.2 (35 reviews) | 

### Data Observability

| Product | Score | Reviews |
|---|---|---|
| **Great Expectations** | 8.8/10 | 11 |
| **Monte Carlo** | 7.6/10 | 382 |

#### Functionality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Real-time Analytics** | 9.4 (8 reviews) | 7.4 (293 reviews) | 
| **Data quality monitoring** | 8.8 (11 reviews) | 8.8 (342 reviews) | 
| **Automation** | 8.8 (11 reviews) | 8.2 (306 reviews) | 
| **End to End visiblity** | 8.9 (9 reviews) | 8.1 (312 reviews) | 

#### Management

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Anomaly identification** | 8.3 (10 reviews) | 8.8 (345 reviews) | 
| **Single pane view** | 8.9 (9 reviews) | 7.8 (292 reviews) | 
| **Real-time alerts** | 8.5 (8 reviews) | 8.3 (332 reviews) | 
| **Data lineage** | 8.7 (9 reviews) | 8.0 (315 reviews) | 
| **Integrations** | 8.6 (11 reviews) | 8.2 (326 reviews) | 

#### Generative AI

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **AI Text Generation** | Not enough data | 5.8 (232 reviews) | 

#### Agentic AI - Data Observability

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Autonomous Task Execution** | Not enough data | 6.9 (33 reviews) | 
| **Multi-step Planning** | Not enough data | 6.5 (28 reviews) | 
| **Cross-system Integration** | Not enough data | 7.0 (30 reviews) | 
| **Natural Language Interaction** | Not enough data | 6.7 (27 reviews) | 
| **Proactive Assistance** | Not enough data | 7.1 (32 reviews) | 

### AI Agent Observability

| Product | Score | Reviews |
|---|---|---|
| **Great Expectations** | N/A | N/A |
| **Monte Carlo** | 9.3/10 | 14 |

#### Tracing & Debugging

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Agent Debugging** | Not enough data | Not enough data | 
| **Trace Visualization** | Not enough data | Not enough data | 
| **End-to-End Agent Tracing** | Not enough data | Not enough data | 

#### Evaluation & Quality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Regression Testing** | Not enough data | Not enough data | 
| **Hallucination Detection** | Not enough data | Not enough data | 
| **Automated Output Evaluation** | Not enough data | Not enough data | 

#### Production Monitoring

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Alerts & Notifications** | Not enough data | 9.3 (9 reviews) | 
| **Latency Monitoring** | Not enough data | Not enough data | 
| **Token Usage & Cost Tracking** | Not enough data | Not enough data | 

#### Agent Discovery & Governance

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Audit Logging** | Not enough data | Not enough data | 
| **Agent Discovery** | Not enough data | Not enough data | 
| **Policy Compliance Monitoring** | Not enough data | Not enough data | 

### Data Quality

| Product | Score | Reviews |
|---|---|---|
| **Great Expectations** | N/A | N/A |
| **Monte Carlo** | 7.0/10 | 203 |

#### Functionality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Identification** | Not enough data | 8.1 (193 reviews) | 
| **Correction** | Not enough data | 6.5 (176 reviews) | 
| **Normalization** | Not enough data | 6.7 (170 reviews) | 
| **Preventative Cleaning** | Not enough data | 6.1 (165 reviews) | 
| **Data Matching** | Not enough data | 6.5 (166 reviews) | 
| **Real-Time Data** | Not enough data | Not enough data | 

#### Management

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Reporting** | Not enough data | 7.2 (169 reviews) | 
| **Automation** | Not enough data | 7.5 (170 reviews) | 
| **Quality Audits** | Not enough data | 8.0 (170 reviews) | 
| **Dashboard** | Not enough data | 7.4 (179 reviews) | 
| **Governance** | Not enough data | 7.6 (170 reviews) | 

#### Generative AI

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **AI Text Generation** | Not enough data | 5.2 (145 reviews) | 
| **AI Text Summarization** | Not enough data | 5.3 (145 reviews) | 
| **Generative AI** | Not enough data | Not enough data | 

#### Additional Functionality

| Feature | Great Expectations | Monte Carlo | 
|---|---|---|
| **Metadata Management** | Not enough data | Not enough data | 
| **Collaboration Tools** | Not enough data | Not enough data | 
| **Search/Filter** | Not enough data | Not enough data | 
| **Workflow Management** | Not enough data | Not enough data | 
| **AI Copilot** | Not enough data | Not enough data | 
| **Third-Party Integrations** | Not enough data | Not enough data | 
| **Data Synchronization** | Not enough data | Not enough data | 
| **Data Import/Export** | Not enough data | Not enough data | 
| **Customizable Rules** | Not enough data | Not enough data | 
| **Master Data Management** | Not enough data | Not enough data | 
| **Monitoring** | Not enough data | Not enough data | 
| **Data Transformation** | Not enough data | Not enough data | 
| **Multiple Data Sources** | Not enough data | Not enough data | 
| **Self Service Portal** | Not enough data | Not enough data | 
| **Customer Database** | Not enough data | Not enough data | 
| **Data Verification** | Not enough data | Not enough data | 
| **Data Migration** | Not enough data | Not enough data | 
| **Multi-Language** | Not enough data | Not enough data | 
| **Single Sign On** | Not enough data | Not enough data | 
| **Duplicate Detection** | Not enough data | Not enough data | 
| **Email Address Extraction** | Not enough data | Not enough data | 
| **Reporting/Analytics** | Not enough data | Not enough data | 
| **Data Profiling** | Not enough data | Not enough data | 
| **Data Extraction** | Not enough data | Not enough data | 
| **Data Mapping** | Not enough data | Not enough data | 
| **Address Validation** | Not enough data | Not enough data | 
| **Match & Merge** | Not enough data | Not enough data | 
| **Performance Metrics** | Not enough data | Not enough data | 
| **Visual Analytics** | Not enough data | Not enough data | 
| **Version Control** | Not enough data | Not enough data | 
| **API** | Not enough data | Not enough data | 
| **Data Capture and Transfer** | Not enough data | Not enough data | 
| **Access Controls/Permissions** | Not enough data | Not enough data | 
| **Compliance Management** | Not enough data | Not enough data | 
| **Data Discovery** | Not enough data | Not enough data | 

---
## Categories
**Shared Categories (2):** [Data Quality Tools](https://www.g2.com/categories/data-quality), [Data Observability Software](https://www.g2.com/categories/data-observability)


**Unique to Monte Carlo (3):** [AI Agent Observability Software](https://www.g2.com/categories/ai-agent-observability), [Database Monitoring Tools](https://www.g2.com/categories/database-monitoring), [DataOps Platforms](https://www.g2.com/categories/dataops-platforms)


---
## Reviewer Demographics

### By Company Size

| Segment | Great Expectations | Monte Carlo | 
|---|---|---|
| **Small-Business** | 36.4% | 4.7% | 
| **Mid-Market** | 45.5% | 43.1% | 
| **Enterprise** | 18.2% | 52.2% | 

### By Industry

#### Great Expectations

- **Accounting:** 36.4%
- **Information Technology and Services:** 18.2%
- **Telecommunications:** 9.1%
- **Security and Investigations:** 9.1%
- **Online Media:** 9.1%
- **Computer Software:** 9.1%
- **Banking:** 9.1%

#### Monte Carlo

- **Financial Services:** 13.0%
- **Computer Software:** 11.9%
- **Information Technology and Services:** 11.9%
- **Manufacturing:** 3.9%
- **Marketing and Advertising:** 3.5%
- **Retail:** 3.1%
- **Pharmaceuticals:** 3.1%
- **Entertainment:** 2.9%
- **Oil & Energy:** 2.9%
- **Online Media:** 2.7%
- **Other:** 41.1%

---
## Alternatives

### Alternatives to Great Expectations

- [Soda](https://www.g2.com/products/soda/reviews) — 4.4/5 stars (55 reviews)
- [Demandbase One](https://www.g2.com/products/demandbase-one/reviews) — 4.4/5 stars (2003 reviews)
- [GTM Studio - Powered by ZoomInfo](https://www.g2.com/products/gtm-studio-powered-by-zoominfo/reviews) — 4.5/5 stars (3512 reviews)
- [Validity Engage](https://www.g2.com/products/validity-engage/reviews) — 4.4/5 stars (705 reviews)
- [Planhat](https://www.g2.com/products/planhat/reviews) — 4.5/5 stars (961 reviews)
- [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) — 4.3/5 stars (821 reviews)
- [HubSpot Data Hub](https://www.g2.com/products/hubspot-data-hub/reviews) — 4.5/5 stars (644 reviews)
- [dbt](https://www.g2.com/products/dbt/reviews) — 4.7/5 stars (209 reviews)
- [Mezmo](https://www.g2.com/products/mezmo/reviews) — 4.6/5 stars (224 reviews)
- [Integrate.io](https://www.g2.com/products/integrate-io/reviews) — 4.4/5 stars (213 reviews)

### Alternatives to Monte Carlo

- [Acceldata](https://www.g2.com/products/acceldata/reviews) — 4.4/5 stars (55 reviews)
- [Anomalo](https://www.g2.com/products/anomalo/reviews) — 4.4/5 stars (44 reviews)
- [Datadog](https://www.g2.com/products/datadog/reviews) — 4.4/5 stars (728 reviews)
- [Soda](https://www.g2.com/products/soda/reviews) — 4.4/5 stars (55 reviews)
- [Metaplane](https://www.g2.com/products/metaplane/reviews) — 4.8/5 stars (116 reviews)
- [Sifflet](https://www.g2.com/products/sifflet/reviews) — 4.3/5 stars (55 reviews)
- [Dynatrace](https://www.g2.com/products/dynatrace/reviews) — 4.5/5 stars (1371 reviews)
- [Databricks](https://www.g2.com/products/databricks/reviews) — 4.6/5 stars (1373 reviews)
- [Hightouch](https://www.g2.com/products/hightouch/reviews) — 4.6/5 stars (414 reviews)
- [IBM Instana](https://www.g2.com/products/ibm-instana/reviews) — 4.4/5 stars (480 reviews)

---
## Top Discussions

### Great Expectations

No discussions available for this product.

### Monte Carlo

- Title: [What is Monte Carlo software?](https://www.g2.com/discussions/what-is-monte-carlo-software) — 1 comment
  > **Top comment:** "Monte Carlo is a fully automated, end-to-end data observability platform that helps data engineering teams reduce time to detection and resolution for data..."

---
**Source:** [G2.com](https://www.g2.com) | [Comparison Page](https://www.g2.com/compare/great-expectations-vs-monte-carlo)