Make Data Observability Everyone’s Business

Sifflet is an AI-native data observability platform built for data teams with business users in mind. 

Make Data Observability Everyone’s Business
Trusted by Leading Companies

Show Your Stack Who’s Boss

Unified data observability that packs a three-in-one punch. From data discovery to integrated monitoring and troubleshooting capabilities, you’ll be the one in charge.

Seamlessly connect with all your favorite data tools to centralize insights and unlock the full potential of your data ecosystem.
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Join the ranks of happy customers who’ve made Sifflet a G2 leader, trusted for its innovation and impact
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Stay ahead of issues with real-time alerts that keep you informed and in control of your data health
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Organize, discover, and leverage your data assets effortlessly with a smart, searchable catalog built for modern teams.
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Harness the power of AI-driven suggestions to improve efficiency, accuracy, and decision-making across your workflows.
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Empower your team with tailored access, enabling secure collaboration that drives smarter decisions.
What Our Customers Say

See Sifflet in action!

Curious about how Sifflet can transform the way your team works with data?
Join our 30-min biweekly demo to see how data leaders, engineers, and platform teams use Sifflet to detect, resolve, and prevent issues—before they impact the business.

Register for our biweekly demo now!
ACHIEVE

See Data Breakthroughs

Sifflet helps you remove the obstacles that stand in the way of superior insights, value, and products from data. 

Supercharge Productivity 

Data engineers spend up 50% of their time on mundane reliability tasks and data analysts between 40 to 80% of their time vetting data quality. Sifflet augments your team’s capabilities and supercharges their productivity. 

Uplevel Data Reliability
and Quality 

See next-level improvements to data reliability and quality thanks to tools that make it easier and faster than ever to find and fix your data. 

Empower Ownership, Enable Self-Serve 

Sifflet ensures that your colleagues always know the health status of data, can give input to monitors, and take ownership of their data assets. Collaboration with data teams improves and it’s easier to enable data-mesh and self-serve.

TEAMS

Built for

Built for Everyone

Everyone

Data Leaders

Data Engineers

Data Users

Everyone

Sifflet allows everyone to own and build confidence in data thanks to features that make it easy to access, understand and contribute to data reliability and quality.

Data Leaders

Drive innovation and enable AI. With Sifflet, you can transform your data strategy, governance, and team productivity while ensuring efficient and scalable data infrastructure.

Data Engineers

Boost your productivity. Sifflet gives you end-to-end visibility into your architecture, assets, and pipelines. Advanced monitoring ensures you get the right alerts and lineage helps you get to resolution faster.

Data Users

No more data discrepancies. Sifflet ensures the highest levels of data quality. Your teams can make the best possible decisions for your company, unlocking new levels of performance that help you compete in the age of AI.

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 does Sifflet’s Freshness Monitor scale across large data environments?
Sifflet’s Freshness Monitor is designed to scale effortlessly. Thanks to our dynamic monitoring mode and continuous scan feature, you can monitor thousands of data assets without manually setting schedules. It’s a smart way to implement data pipeline monitoring across distributed systems and ensure SLA compliance at scale.
How is data freshness different from latency or timeliness?
Great question! While these terms are often used interchangeably, they each mean something different. Data freshness is about how up-to-date your data is. Latency measures the delay from data generation to availability, and timeliness refers to whether that data arrives within expected time windows. Understanding these differences is key to effective data pipeline monitoring and SLA compliance.
How does MCP improve root cause analysis in modern data systems?
MCP empowers LLMs to use structured inputs like logs and pipeline metadata, making it easier to trace issues across multiple steps. This structured interaction helps streamline root cause analysis, especially in complex environments where traditional observability tools might fall short. At Sifflet, we’re integrating MCP to enhance how our platform surfaces and explains data incidents.
How is Sifflet rethinking root cause analysis in data observability?
Root cause analysis is a critical part of data reliability, and we’re making it smarter. Instead of manually sifting through logs or lineage graphs, Sifflet uses AI and metadata to automate root cause detection and suggest next steps. Our observability tools analyze query logs, pipeline dependencies, and usage patterns to surface the 'why' behind incidents — not just the 'what.' That means faster triage, quicker resolution, and fewer surprises downstream.
Why is data observability becoming essential for modern data teams?
As data pipelines grow more complex, data observability provides the visibility needed to monitor and troubleshoot issues across the full stack. By adopting a robust observability platform, teams can detect anomalies, ensure SLA compliance, and maintain data reliability without relying on manual checks or reactive fixes.
Why are containers such a big deal in modern data infrastructure?
Containers have become essential in modern data infrastructure because they offer portability, faster deployments, and easier scalability. They simplify the way we manage distributed systems and are a key component in cloud data observability by enabling consistent environments across development, testing, and production.
How do the four pillars of data observability help improve data quality?
The four pillars—metrics, metadata, data lineage, and logs—work together to give teams full visibility into their data systems. Metrics help with data profiling and freshness checks, metadata enhances data governance, lineage enables root cause analysis, and logs provide insights into data interactions. Together, they support proactive data quality monitoring.
How does MCP support data quality monitoring in modern observability platforms?
MCP helps LLMs become active participants in data quality monitoring by giving them access to structured resources like schema definitions, data validation rules, and profiling metrics. At Sifflet, we use this to detect anomalies, enforce data contracts, and ensure SLA compliance more effectively.
Still have questions?

More Data. Less Chaos.

If you want a smoother running stack,
let’s talk about what Sifflet can do for you.