Solution by Use Case

Monitoring at Scale

Coverage without compromise.

Grow monitoring coverage intelligently as your stack scales and do more with less resources thanks to tooling that reduces maintenance burden, improves signal-to-noise, and helps you understand impact across interconnected systems.

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Don’t Let Scale Stop You

As your stack and data assets scale, so do monitors. Keeping rules updated becomes a full-time job, and tribal knowledge about monitors gets scattered, so teams struggle to sunset obsolete monitors while adding new ones. No more with Sifflet.

Optimize monitoring coverage and minimize noise levels with AI-powered suggestions and supervision that adapt dynamically
Implement programmatic monitoring set up and maintenance with Data Quality as Code (DQaC)
Automated monitor creation and updates based on data changes
Centralized monitor management reduces maintenance overhead

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Get Clear and Consistent

Maintaining consistent monitoring practices across tools, platforms, and internal teams that work across different parts of the stack isn’t easy. Sifflet makes it a breeze.

Set up consistent alerting and response workflows
Benefit from unified monitoring across your platforms and tools
Use automated dependency mapping to show system relationships and benefit from end-to-end visibility across the entire data pipeline

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Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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Frequently asked questions

What makes Sifflet a strong alternative to Monte Carlo for data observability?

Sifflet stands out as a modern data observability platform that combines AI-powered monitoring with business context. Unlike Monte Carlo, Sifflet offers no-code monitor creation, dynamic alerting with impact insights, and real-time data lineage tracking. It's designed for both technical and business users, making it easier for teams to collaborate and maintain data reliability across the organization.

Is this integration useful for teams focused on data governance and compliance?

Yes, it really is! With enhanced lineage and metadata tracking from source to destination, the Fivetran integration supports better data governance. It helps ensure transparency, traceability, and SLA compliance across your data ecosystem.

How does Sifflet help detect and prevent data drift in AI models?

Sifflet is designed to monitor subtle changes in data distributions, which is key for data drift detection. This helps teams catch shifts in data that could negatively impact AI model performance. By continuously analyzing incoming data and comparing it to historical patterns, Sifflet ensures your models stay aligned with the most relevant and reliable inputs.

Why is data freshness so important for data reliability?

Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.

What does Sifflet plan to do with the new $18M in funding?

We're excited to use this funding to accelerate product innovation, expand our North American presence, and grow our team. Our focus will be on enhancing AI-powered capabilities, improving data pipeline monitoring, and helping customers maintain data reliability at scale.

How does Sifflet support both technical and business teams?

Sifflet is designed to bridge the gap between data engineers and business users. It combines powerful features like automated anomaly detection, data lineage, and context-rich alerting with a no-code interface that’s accessible to non-technical teams. This means everyone—from analysts to execs—can get real-time metrics and insights about data reliability without needing to dig through logs or write SQL. It’s observability that works across the org, not just for the data team.

What makes business-aware data observability so important?

Business-aware observability bridges the gap between technical issues and real-world outcomes. It’s not just about detecting schema changes or data drift — it’s about understanding how those issues affect KPIs, dashboards, and decisions. At Sifflet, we bring together telemetry instrumentation, data profiling, and business context so teams can prioritize incidents based on impact, not just severity. This empowers everyone, from data engineers to product managers, to trust and act on data with confidence.

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

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