Soda has acquired ML monitoring startup NannyML

Soda has acquired ML monitoring startup NannyML

Soda has acquired ML monitoring startup NannyML

Maarten Masschelein

Maarten Masschelein

Maarten Masschelein

CEO and Founder at Soda

CEO and Founder at Soda

Table of Contents

Today weโ€™re kicking off Soda Launch Week with a major announcement: Soda has acquired NannyML.

Together, weโ€™re building the most intelligent, context-aware data quality platform on the market. One that helps you prevent issues before they become business problems, detect anomalies that actually matter, and trace root causes across the entire stack, from data ingestion to automated decision-making.

This move brings together two teams with a shared goal: helping data and AI teams ship reliable, production-grade systems they can trust, whether those systems power dashboards, models, or autonomous agents.

Letโ€™s get into what this means, why weโ€™re doing it, and whatโ€™s coming next.

The Gap in Data Quality is Getting Worse

If youโ€™ve worked on data or AI infrastructure, youโ€™ve lived this:

  • A pipeline silently drops a column, no schema failure, but a downstream metric flatlines.

  • A dashboard suddenly shows revenue down 30%, and nobody knows why.

  • A model in production starts drifting due to subtle shifts in user behavior.

  • An agent retrains or reacts based on corrupted inputs, and no one catches it until decisions are made.

Most data quality tooling today canโ€™t handle this. It was built for a different era of batch jobs, static schemas, predictable data flows. It flags too much noise, misses critical context, and rarely shows downstream impact.

At the same time, the systems weโ€™re building today are more dynamic than ever:

  • Agents

  • LLM-powered decisioning

  • Real-time personalization

  • Hybrid batch-streaming pipelines

  • Continuous retraining loops

In this world, traditional checks and anomaly detection arenโ€™t enough. Data quality isnโ€™t just about correctness anymore, itโ€™s about consequence.

Why NannyML

NannyML tackled one of the hardest problems in modern AI systems:

How do you monitor model performance in production, when thereโ€™s no ground truth yet?

Their open-source library introduced estimation-based performance monitoring, robust drift detection, and alerting designed for real-world ML pipelines. It became the go-to toolkit for teams running models where labels are delayed, sparse, or unavailable.

But more importantly, they saw what was coming:

That models donโ€™t fail in isolation. They fail when data pipelines degrade, when user behavior shifts, when upstream assumptions break. And they believed the only way to solve this was to close the loop between data quality and AI behavior.

Weโ€™ve believed the same from day one.

By bringing our teams and platforms together, weโ€™re unifying those layers. Delivering a product that can monitor your entire system, not just pieces of it.

What Weโ€™re Building Together

With NannyMLโ€™s team and tech now integrated into Soda, hereโ€™s what this unlocks:

Smarter detection at the DQ layer

NannyMLโ€™s algorithms will power a more intelligent core in Sodaโ€™s checks and observability. Reducing noise, surfacing real issues faster, and adapting to change.

Context-aware alerting across the stack

Trace anomalies across systems: from a column drift in your warehouse, to a prediction shift in your model, to a behavior change in your agents.

End-to-end observability: from data to decision

Monitor the full lifecycle, not just tables or checks. See how upstream issues ripple into downstream systems. Know what changed, why it matters, and what to fix.

AI-native quality infrastructure

Whether youโ€™re running batch analytics, near-real-time features, or LLM orchestration, weโ€™re building foundational infrastructure that keeps data and behavior aligned.

And yes, NannyMLโ€™s open-source project will remain open, maintained, and fully supported. Weโ€™re not sunsetting it. Weโ€™re expanding it.

Why Now

Because the cost of bad data is rising, and fast.

The systems data powers today are higher-stakes, faster-moving, and harder to debug.

If your tooling doesnโ€™t understand impact, itโ€™s not helping. If it canโ€™t handle emergence and drift, itโ€™s irrelevant. And if itโ€™s not built for AI-native environments, itโ€™s already behind.

Weโ€™re not here to slap โ€œAIโ€ on legacy checks. Weโ€™re here to make data quality actually intelligent:

  • Impact-aware

  • Context-rich

  • Lifecycle-connected

  • And ready for systems that learn, adapt, and act

This acquisition accelerates that mission.

Whatโ€™s Coming This Week

This is Day 1 of Launch Week. All week long, weโ€™ll be announcing new capabilities and product drops that show what intelligent, AI-first data quality looks like in practice.

Hereโ€™s a preview of whatโ€™s coming:

  • The fastest and most accurate metrics observability

  • Collaborative data contracts

  • A free forever tier and transparent pricing

Weโ€™re just getting started, and weโ€™re building fast.

Where To Go Next

Watch the full announcement webinar

Hear directly from Maarten and Hakim about whatโ€™s changing, and whatโ€™s coming next.

Try Soda

See how our platform is evolving to support AI-native teams. No fluff, just the signals that matter.

This is the next chapter for data quality.

Smarter. Faster. AI-ready.

And built for teams like yours.

โ†’ Request a demo with our team

Trusted by the worldโ€™s leading enterprises

Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.

At the end of the day, we donโ€™t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe whatโ€™s happening, and thatโ€™s what Soda is enabling right now.

Sid Srivastava

Director of Data Governance, Quality and MLOps

Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.

Mario Konschake

Director of Product-Data Platform

Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.

Sutaraj Dutta

Data Engineering Manager

Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.

Gu Xie

Head of Data Engineering

4.4 of 5

Your data has problems.
Now they fix themselves.

Automated data quality, remediation, and management.

One platform, agents that do the work, you approve.

Trusted by

Trusted by the worldโ€™s leading enterprises

Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.

At the end of the day, we donโ€™t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe whatโ€™s happening, and thatโ€™s what Soda is enabling right now.

Sid Srivastava

Director of Data Governance, Quality and MLOps

Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.

Mario Konschake

Director of Product-Data Platform

Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.

Sutaraj Dutta

Data Engineering Manager

Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.

Gu Xie

Head of Data Engineering

4.4 of 5

Your data has problems.
Now they fix themselves.

Automated data quality, remediation, and management.

One platform, agents that do the work, you approve.

Trusted by

Trusted by the worldโ€™s leading enterprises

Real stories from companies using Soda to keep their data reliable, accurate, and ready for action.

At the end of the day, we donโ€™t want to be in there managing the checks, updating the checks, adding the checks. We just want to go and observe whatโ€™s happening, and thatโ€™s what Soda is enabling right now.

Sid Srivastava

Director of Data Governance, Quality and MLOps

Investing in data quality is key for cross-functional teams to make accurate, complete decisions with fewer risks and greater returns, using initiatives such as product thinking, data governance, and self-service platforms.

Mario Konschake

Director of Product-Data Platform

Soda has integrated seamlessly into our technology stack and given us the confidence to find, analyze, implement, and resolve data issues through a simple self-serve capability.

Sutaraj Dutta

Data Engineering Manager

Our goal was to deliver high-quality datasets in near real-time, ensuring dashboards reflect live data as it flows in. But beyond solving technical challenges, we wanted to spark a cultural shift - empowering the entire organization to make decisions grounded in accurate, timely data.

Gu Xie

Head of Data Engineering

4.4 of 5

Your data has problems.
Now they fix themselves.

Automated data quality, remediation, and management.

One platform, agents that do the work, you approve.

Trusted by