Optimize or Pay the Price: Cut Costs & Fix Slowdowns with Monte Carlo Performance Products

Nobody ever got fired for saving money and optimizing pipelines. 

In today’s AI-powered world, data volumes are exploding and pipelines are more complex than ever. That makes the cost of inefficient jobs and queries high stakes—delayed pipelines, wasted compute, escalating warehouse spend, broken product experiences…you get the idea.

To keep up, data teams need more than dashboards or dbt tests. They need end-to-end observability that goes beyond table-level monitoring to proactively surface and resolve performance issues across their stack. That’s where Monte Carlo comes in.

The Cost of Poor Job and Query Hygiene

A slow-running query isn’t just a nuisance. It’s a drain on your budget, your team, and your customer trust. 

Issues like suboptimal dbt models, inefficient joins, and silent job failures cause cascading problems for engineers and consumers alike—but traditional solutions (like fragmented monitoring and Airflow SLAs) leave teams on the back-foot, chasing down root causes only after an issue has impacted stakeholders downstream.

Without a centralized, end-to-end view of performance, slow or failing jobs can quietly accumulate technical debt, spiking compute costs, delaying dashboards, and stalling the critical workflows they’re supposed to support. 

Introducing: Monte Carlo’s New Job Performance Dashboard

Monte Carlo’s new Job Performance Dashboard is the latest upgrade to our Performance capabilities.

Built to give data teams a birds-eye view of job health across Airflow DAGs, dbt jobs, Databricks Workflows, and Azure Data Factory, the Job Performance Dashboard centralizes ETL performance data to help you discover and resolve bottlenecks faster—and reduce costs in the process.

The best part?  Monte Carlo doesn’t just tell you what’s slow—it shows you why too. With detailed Gantt views, metadata overlays, and lineage insights, you can drill into any job, query, or task to help you pinpoint the root cause, optimize for performance, and move on.

Real-World Results: SurveyMonkey Cuts Costs by 73%

SurveyMonkey’s data team faced growing performance and governance challenges as their data environment scaled to 25+ sources, 600 daily dbt models, and over 2,000 tests. Business stakeholders expected fresh, trustworthy data by 8 a.m., but pipeline failures, unmonitored model performance, and inefficient queries frequently caused delays, re-runs, and mounting Snowflake costs.

With Monte Carlo, SurveyMonkey created a powerful observability layer that combined anomaly detection with proactive testing. Monte Carlo’s Performance products empowered the team to identify and fix inefficient queries, clean up unused tables, and optimize models—slashing Snowflake credit usage by 73% per 10,000 jobs.

Real-World Results: Tenable Reduces Pipeline Latency by 50%

Tenable, a leading cybersecurity company, powers its Tenable One Exposure Management Platform with globally distributed Snowflake environments and real-time data streams from sensors across dozens of services. With over 16 Snowflake accounts worldwide and a nine-person engineering team, ensuring fast, fresh, and accurate data is no small feat. 

Monte Carlo’s data + AI observability platform is a core component of Tenable’s infrastructure, automatically detecting data quality issues, freshness gaps, and schema changes with minimal manual effort. By adopting Performance products and custom monitors, Tenable has also been proactively identifying slow queries and failing jobs, boosting reliability and controlling costs without added complexity. The result? A 50% reduction in overall pipeline latency and a shift from reactive firefighting to proactive optimization at scale. 

3 Ways to Get the Most from Performance

Monte Carlo helps teams move from reactive firefighting to proactive optimization:

1. Pinpoint Cost Drivers: Use the dashboard to investigate spikes in warehouse costs by filtering for high-cost or long-running queries. Drill into queue time, partitions scanned, and execution metadata to optimize inefficient workloads and reduce unnecessary compute spend.
2. Optimize dbt Models and Job Performance: Identify which dbt models are slowing down pipeline execution using query-to-job traceability and Gantt-style job visualizations. Understand dependencies and bottlenecks to prioritize model optimization and streamline development cycles.
3. Improve End-User Experience and Pipeline Reliability: Monitor query latency for tools like Looker to catch slow dashboards before users do. Analyze Airflow, Databricks, or ADF job trends to find degrading or frequently failing DAGs—then drill into root causes to prevent repeat incidents.

Performance Monitoring for the Whole Stack

Whether you’re debugging a slow Airflow DAG, auditing warehouse costs, or optimizing user-facing dashboards, Monte Carlo gives you the tools to spot performance bottlenecks—and fix them fast. With performance views for queries and jobs side-by-side with data quality monitors and lineage, you get a complete picture of reliability and efficiency across your data stack.

Ready to reduce costs, move faster, and keep data consumers happy? Schedule a demo to see how Monte Carlo can help you operationalize performance and trust at scale.

Our promise: we will show you the product.