MONITOR

Lean. Mean. Monitoring Machine.

Finally, dynamic monitoring that can keep up with your stack. AI features optimize your coverage and minimize noise, detecting issues before they arise.

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Customize to Your Heart’s Content

Sifflet offers both a robust library of out of the box monitors and customization capability. Your teams decide what needs monitoring and how to set it up.

Bye-Bye, Alert Fatigue

Data engineers don’t need more alerts, they need smarter alerts. Our AI learns adaptively as it goes to optimize coverage and minimize noise.

Hello, Data Reliability

Data reliability is reinforced with less manual work for technical teams, faster response times, and overall stronger performance.

IMPLEMENT

Ready-to-Go Monitors

Quick set up and implementation means quicker results.

See value instantly with pre-defined templates to check data at field and table levels
Help your business users and technical teams meet their quality and reliability objectives thanks to ready-to-go monitors

SUPERVISE

Lifecycle Monitoring

End-to-end coverage that never sleeps.

Detect anomalies continuously thanks to ML models
Give your business users ownership over monitors through LLM monitoring setup
Maintain control and accuracy with optional manual setup and user feedback

MAINTAIN

Scalability & Optimization

Monitoring that’s easy to maintain and coverage that’s just right.

Optimize monitoring coverage and minimize noise levels with AI-powered suggestions and supervision
Implement programmatic monitoring set up and maintenance with Data Quality as Code (DQaC)

TEAMS

Reinforced Reliability

Built for Everyone

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

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Data Users

Stop working with corrupt data. Sifflet embeds alerts in your dashboards, so you know exactly when there’s an incident or issue. And you can set up data monitors on your own.

Data Engineers

No more scaling monitors manually. Sifflet’s ML will optimize coverage for you, so you can be proactive instead of reactive in reducing downtimes.

Data Leaders

Give your teams the tools they need to reduce monitoring tasks by up to 50% thanks to Sifflet’s monitoring features.

Looking for more?

sifflet a guide to assessing your need for a data observability

A Guide to Assessing your Need for Data Observability

Every data-driven team dreams of creating "fireworks" with their insights. But without control, the "fire" of the modern data stack can spark chaos.
Discover if you're fighting fires or igniting fireworks:

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Data Quality Monitoring is Dead

In this blog, we’ll explore why data quality monitoring falls short and how observability is reshaping the future of data management. Ready to make the shift?

Let’s dive in!

Data monitoring vs observability solutions

Data monitoring and data observability are often used interchangeably, but they serve distinct purposes in managing modern data systems. Understanding the difference is key to choosing the right solution for your organization.

Ready to clarify?

Data Reliability, Reinforced

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

Speak with our Experts

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

Frequently asked questions

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.

What role did data observability play in Carrefour’s customer engagement strategy?

Data observability was crucial in maintaining high data quality for loyalty programs and marketing campaigns. With real-time metrics and anomaly detection in place, Carrefour was able to improve customer satisfaction and retention through more accurate and timely insights.

How can I monitor the health of my ETL or ELT pipelines?

Monitoring pipeline health is essential for maintaining data reliability. You can use tools that offer data pipeline monitoring features such as real-time metrics, ingestion latency tracking, and pipeline error alerting. Sifflet’s pipeline health dashboard gives you full visibility into your ETL and ELT processes, helping you catch issues early and keep your data flowing smoothly.

How does metadata management support data governance?

Strong metadata management allows organizations to capture details about data sources, schemas, and lineage, which is essential for enforcing data governance policies. It also supports compliance monitoring and improves overall data reliability by making data more transparent and trustworthy.

Why is data observability so important for AI-powered organizations in 2025?

Great question! As AI continues to evolve, the quality and reliability of the data feeding those models becomes even more critical. Data observability ensures that your AI systems are powered by clean, accurate, and up-to-date data. With platforms like Sifflet, organizations can detect issues like data drift, monitor real-time metrics, and maintain data governance, all of which help AI models stay accurate and trustworthy.

Is Sifflet available for VPC deployment on Google Cloud?

Yes it is! You can deploy Sifflet’s observability platform within your own private Google Cloud environment using VPC deployment, giving you full control over data governance and security.

How does Sifflet help scale dbt environments without compromising data quality?

Great question! Sifflet enhances your dbt environment by adding a robust data observability layer that enforces standards, monitors key metrics, and ensures data quality monitoring across thousands of models. With centralized metadata, automated monitors, and lineage tracking, Sifflet helps teams avoid the usual pitfalls of scaling like ownership ambiguity and technical debt.

Can I deploy Sifflet in my own environment for better control?

Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.

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