Monte Carlo and Databricks Partner to Deliver Data + AI Observability

Monte Carlo and Databricks double-down on their partnership, helping organizations build trusted AI applications by expanding visibility into the data pipelines that fuel the Databricks Data Intelligence Platform.

Announced today, Monte Carlo and Databricks are giving data + AI teams comprehensive visibility into the quality and reliability of AI systems in Databricks Data Intelligence Platform — helping organizations move beyond demos to dependable AI solutions. 

Beyond the hype: Building AI on reliable data

Groundbreaking comprehensive suite of tools like Databricks’ Data Intelligence Platform make it possible for all kinds of teams to build impactful AI-powered products. But successful implementations depend on one universal truth: your AI system can only be as good as the data flowing through it. 

Whether you use data to enrich model context in RAG or to fine tune and deploy custom models, when data quality falters, AI applications fail in costly ways, from outdated product recommendations leading to missed revenue to incomplete transaction data causing undetected fraud. Data + AI teams waste time troubleshooting, business users lose confidence in AI systems, and companies risk their reputation with every inaccurate output. 

That’s why Monte Carlo’s expanded partnership with Databricks is a game-changer: by providing end-to-end data + AI observability across structured and unstructured data pipelines within Data Intelligence Platform agent systems.

“We’re incredibly excited to see Monte Carlo expanding their data + AI observability capabilities to support unstructured data pipelines in Databricks’ Data Intelligence Platform,” said Adam Conway, SVP Products at Databricks. “This collaboration empowers our customers to gain deeper insights and trust in their AI-driven workloads, accelerating innovation with reliable, high-quality data.”

Read on to discover how we’re helping organizations ensure reliability across the entire data + AI lifecycle.

Visibility into every step of your data + AI journey

Imagine driving a car with no dashboard indicators. You wouldn’t know if you’re running low on gas, if the engine is overheating, or if your tire pressure is dangerously low—until something catastrophic happens.

For too long, data teams have been flying blind when it comes to AI systems. They’ve focused on monitoring model outputs while ignoring the health of the overall engine: data, system, code, and models. 

AI applications can break for countless reasons—scheduled data refreshes failing silently, customer behavior patterns shifting while training data remains static, or vector embeddings gradually losing relevance. Without proper visibility, these issues can go undetected until they cause significant damage.

The path to agentic AI is a dynamic one—from preparing your data to deploying and evaluating your agents—and data leaders need data + AI observability into every step in that workflow. That’s our vision at Monte Carlo.

Monte Carlo’s data + AI observability platform integrates with Databricks Data Intelligence Platform to illuminate the entire data journey, monitoring both structured and unstructured data pipelines as well as orchestration systems. This comprehensive visibility helps teams identify and resolve data issues before they cascade into AI failures. As we tighten our partnership with Databricks, we are excited to expand our joint solution to drive greater visibility across the entire lakehouse, including data, system, code, and models. With complete, end-to-end data + AI observability, mutual customers like Riot Games, American Airlines, and M&T Bank can ensure the reliability of their data and agentic AI products at scale.

Reliability at every layer of the data + AI stack

The organizations seeing the most success with AI ensure reliable data flows consistently into their applications. End-to-end data + AI observability isn’t a nice-to-have—it’s essential. As AI systems become more modular and complex, the old approach of simply checking if the final output looks right becomes untenable. Teams need visibility into every step of the data journey, from source systems to model serving.

Our partnership with Databricks extends Monte Carlo’s detection and resolution capabilities across data pipelines supporting Data Intelligence Platform, enabling teams to:

• Detect data quality issues across structured and unstructured sources before they affect AI outputs
• Track lineage to quickly identify impact and root causes
• Receive intelligent alerts tailored to critical data assets
• Quickly diagnose and resolve data pipeline problems

By ensuring data reliability, Monte Carlo and Databricks help organizations cut through the hype and deliver AI applications that consistently meet business requirements and user expectations. 

Want to learn more about the future of reliability for applications powered by Databricks? Give us a call.

Our promise: we will show you the product.