COMPARISON

Sifflet is better for business

The best data leaders know that data quality isn’t just a technical issue. They want tooling that allows technical and business users to engage with data observability. That’s why teams are switching from Monte Carlo to Sifflet.

THE BIG PICTURE

Augmented data quality for analytics and AI

Monte Carlo focuses on technical data quality, looking at freshness, accuracy, nulls, voids and more. Sifflet does this too, but it goes one step further—pulling in documentation and business context with input from downstream users to understand business critical data products and processes.

The result is truly augmented data quality for analytics and AI, because technical teams know exactly what to prioritize when it comes to monitoring and triaging.

Don't Solve Half the Problem.

If you want to tackle data quality just from a technical perspective, Sifflet isn’t for you. But if you want to reach augmented data quality for analytics and AI that truly brings business value to downstream users, Sifflet is the right choice for today… and tomorrow.

Monte Carlo
Programmatic & Infrastructure as Code Capabilities
No Code Options/ Intuitive UI
Automated Machine Learning of Data Environments
Business Impact Prioritized Alerts
AI-Powered Monitoring Wizard for Monitor Setup
Programmatic Monitoring (SQL based Monitoring, Monitoring-as-Code)
Dynamic Context Engine for RCA
Downstream Impact Analysis
Proactive Data Quality Mitigation 

There's no one size fits all.

When it comes to data observability platforms, there's no one size fits all.
Chat with one of our experts today to learn more about Sifflet and if it's the right option for you.

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

How do modern storage platforms like Snowflake and S3 support observability tools?
Modern platforms like Snowflake and Amazon S3 expose rich metadata and access patterns that observability tools can monitor. For example, Sifflet integrates with Snowflake to track schema changes, data freshness, and query patterns, while S3 integration enables us to monitor ingestion latency and file structure changes. These capabilities are key for real-time metrics and data quality monitoring.
Can I trust the data I find in the Sifflet Data Catalog?
Absolutely! Thanks to Sifflet’s built-in data quality monitoring, you can view real-time metrics and health checks directly within the Data Catalog. This gives you confidence in the reliability of your data before making any decisions.
How does data observability support AI and machine learning initiatives?
AI models are only as good as the data they’re trained on. With data observability, you can ensure data quality, detect data drift, and enforce validation rules, all of which are critical for reliable AI outcomes. Sifflet helps you maintain trust in your data so you can confidently scale your ML and predictive analytics efforts.
How does Shippeo’s use of data pipeline monitoring enhance internal decision-making?
By enriching and aggregating operational data, Shippeo creates a reliable source of truth that supports product and operations teams. Their pipeline health dashboards and observability tools ensure that internal stakeholders can trust the data driving their decisions.
How does a unified data observability platform like Sifflet help reduce chaos in data management?
Great question! At Sifflet, we believe that bringing together data cataloging, data quality monitoring, and lineage tracking into a single observability platform helps reduce Data Entropy and streamline how teams manage and trust their data. By centralizing these capabilities, users can quickly discover assets, monitor their health, and troubleshoot issues without switching tools.
How does Sifflet help with compliance monitoring and audit logging?
Sifflet is ISO 27001 certified and SOC 2 compliant, and we use a separate secret manager to handle credentials securely. This setup ensures a strong audit trail and tight access control, making compliance monitoring and audit logging seamless for your data teams.
What benefits does end-to-end data lineage offer my team?
End-to-end data lineage helps your team perform accurate impact assessments and faster root cause analysis. By connecting declared and built-in assets, you get full visibility into upstream and downstream dependencies, which is key for data reliability and operational intelligence.
How do I ensure SLA compliance during a cloud migration?
Ensuring SLA compliance means keeping a close eye on metrics like throughput, resource utilization, and error rates. A robust observability platform can help you track these metrics in real time, so you stay within your service level objectives and keep stakeholders confident.
Still have questions?