Product

Show Your %%Data Stack%% Who’s Boss.

Sifflet’s platform is powered by AI to tackle the sheer volume and complexity of the modern data stack.

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Augmented Assistance

Let AI help you speed things up. Sifflet is designed to reduce tedious tasks - like generating metadata descriptions or correcting SQL - with the click of a button.

No Coding Skills Required

Not an engineer? Not a problem. Describe what kind of monitors you’d like and Sifflet takes care of the rest.

Smart Alerts

Sifflet uses AI to optimize monitoring coverage and avoid alert fatigue by sending you the right alerts at the right time.

ADAPT

%%Dynamic%% Monitors

Monitors that get smarter as they go.

AI that creates monitors based on your prompts
Monitoring that learns from historical and on-going data
Detects anomalies in real time, adapts to trends, and sends meaningful alerts

ASSIST

Building Rich %%Metadata%%

Say goodbye to creating metadata manually.

AI-generated column and asset descriptions.
Automatic classification for the data in your fields.

%%Easy%% Monitor Creation

Create monitors, monitor names and descriptions effortlessly.

Monitor configuration, title and description suggestions.
SQL correction.
Regex suggestions
Monitor of Monitoring Accuracy (MoMA) suggestions

Tame Your Stack. %%Scale Your Smarts.%%

Sifflet’s AI-powered features help you show your stack who’s boss. Augment your team’s capabilities and make data observability everyone’s business.

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Data Users

Thanks to AI, there’s no need to wait for the data engineering team to adapt, create or fix a monitor. Your monitors can also adapt to changes in seasonal trends.

Data Engineers

Sifflet’s AI helps reduce manual work on tedious, repetitive tasks and gives your data users self-serve tools instead of requiring engineering time.

Data Leaders

AI features that make your data engineers more efficient and your data users better able to take ownership of their data.

Looking for more?

7 Reasons for Data Observability within AI-Powered Organizations

Sifflet AI Assistant - Automating your data observability at scale

AI and Data Management

Scale isn't so %%scary%%.

Sifflet’s AI-powered features help you wrangle your stack, even as it scales. Augment your team's capabilities today to make data observability everyone’s business.

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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

Still have a question in mind ?

Frequently asked questions

What role does MCP play in improving data quality monitoring?

MCP enables LLMs to access structured context like schema changes, validation rules, and logs, making it easier to detect and explain data quality issues. With tool calls and memory, agents can continuously monitor pipelines and proactively alert teams when data quality deteriorates. This supports better SLA compliance and more reliable data operations.

What are the key components of an end-to-end data platform?

An end-to-end data platform includes layers for ingestion, storage, transformation, orchestration, governance, observability, and analytics. Each part plays a role in making data reliable and actionable. For example, data lineage tracking and real-time metrics collection help ensure transparency and performance across the pipeline.

How does data observability fit into the modern data stack?

Data observability integrates across your existing data stack, from ingestion tools like Airflow and AWS Glue to storage solutions like Snowflake and Redshift. It acts as a monitoring layer that provides real-time insights and alerts across each stage, helping teams maintain pipeline health and ensure data freshness checks are always in place.

What is reverse ETL and why is it important in the modern data stack?

Reverse ETL is the process of moving data from your data warehouse into external systems like CRMs or marketing platforms. It plays a crucial role in the modern data stack by enabling operational analytics, allowing business teams to act on real-time metrics and make data-driven decisions directly within their everyday tools.

Why is data observability becoming such a priority for enterprises in 2025?

Great question! As more organizations rely on AI and analytics for decision-making, ensuring data quality, health, and reliability has become non-negotiable. Data observability platforms like Sifflet help teams detect issues early, reduce downtime, and maintain trust in their data pipelines.

What’s Sifflet’s vision for data observability in 2025?

Our 2025 vision is all about pushing the boundaries of cloud data observability. We're focusing on deeper automation, AI-driven insights, and expanding our observability platform to cover everything from real-time metrics to predictive analytics monitoring. It's about making data operations more resilient, transparent, and scalable.

What should I look for in a modern data discovery tool?

Look for features like self-service discovery, automated metadata collection, and end-to-end data lineage. Scalability is key too, especially as your data grows. Tools like Sifflet also integrate data observability, so you can monitor data quality and pipeline health while exploring your data assets.

What role does data lineage tracking play in observability?

Data lineage tracking is a key part of any robust data observability framework. It helps you understand where your data comes from, how it’s transformed, and where it flows. This visibility is essential for debugging issues, ensuring compliance, and building trust in your data pipelines. It's especially useful when paired with real-time data pipeline monitoring tools.

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