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The Offer (valid through Jan 31st, 2026) $30,000 / 12 months (normally $85K - a 65% discount)

Purchase via Snowflake Marketplace or AWS PPA (no PO, no onboarding delays).
Includes:
- Up to 500 assets + unlimited users
- Enterprise Edition features at Entry price, including: SSO, Snowflake Data Sharing, Pipeline Monitoring, AI Agents
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Pre-configured 12-month SKU for 1-click private offer acceptance.
Unlockable via unused Snowflake credits.
First 20 customers only.

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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

"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

"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

"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 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

"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

How do AI agents like Sentinel and Sage improve data reliability?

Sentinel and Sage, two of Sifflet’s AI agents, continuously monitor data lineage, usage patterns, and operational metrics to detect issues early. By bundling related alerts, identifying root causes, and suggesting fixes, they reduce downtime and improve overall data reliability. This kind of automated data quality monitoring helps teams stay ahead of incidents and maintain SLA compliance.

What’s the role of an observability platform in scaling data trust?

An observability platform helps scale data trust by providing real-time metrics, automated anomaly detection, and data lineage tracking. It gives teams visibility into every layer of the data pipeline, so issues can be caught before they impact business decisions. When observability is baked into your stack, trust becomes a natural part of the system.

What kinds of data does Shippeo monitor to support real-time metrics?

Shippeo tracks critical operational data like order volume, GPS positions, and platform activity. With Sifflet, they monitor ingestion latency and data freshness to ensure that metrics powering dashboards and customer reports are always up to date.

How can I track the success of my data team?

Define clear success KPIs that support ROI, such as improvements in SLA compliance, reduction in ingestion latency, or increased data reliability. Using data observability dashboards and pipeline health metrics can help you monitor progress and communicate value to stakeholders. It's also important to set expectations early and maintain strong internal communication.

How does reverse ETL fit into the modern data stack?

Reverse ETL is a game-changer for operational analytics. It moves data from your warehouse back into business tools like CRMs or marketing platforms. This enables teams across the organization to act on insights directly from the data warehouse. It’s a perfect example of how data integration has evolved to support autonomy and real-time metrics in decision-making.

How does data observability support compliance with regulations like GDPR?

Data observability plays a key role in data governance by helping teams maintain accurate documentation, monitor data flows, and quickly detect anomalies. This proactive monitoring ensures that your data stays compliant with regulations like GDPR and HIPAA, reducing the risk of costly fines and audits.

What role does passive metadata play in Sifflet’s observability platform?

Passive metadata is the backbone of Sifflet's observability platform. It fuels the data catalog, supports anomaly detection, and enables tools like Sentinel and Sage to monitor data quality, trace issues, and automate responses. Without passive metadata, real-time metrics and lineage insights wouldn’t be possible.

Why is data quality so critical for businesses today?

Great question! Data quality is essential because it directly influences decision-making, customer satisfaction, and operational efficiency. Poor data quality can lead to faulty insights, wasted resources, and even reputational damage. That's why many teams are turning to data observability platforms to ensure their data is accurate, complete, and trustworthy across the entire pipeline.

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