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Pricing
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Build data trust at your own pace, from first monitors to enterprise-wide observability.
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Let’s chat about how it works.
Entry
Growth
Enterprise
Number of Assets Monitored
Up to 500
Up to 1,000
1,000+ (scales flexibly)
Great for...
Small but mighty data teams
Cross-functional data teams
Large, regulated or complex organizations
Procurement Process
Self-Serve/Marketplaces
Sales-Assisted/Marketplaces
Direct Enterprise Sales or Channel
What you'll get
Core Data Observability & Catalog
(Fundamental metrics: freshness, schema, volume, custom metrics...)
Business-Aware Lineage & Impact Analysis
Automated Root-Cause Analysis
AI-Powered Incident Management
Advanced Governance
(RBAC, Audit logs...)
Data Observability Agent
SSO
Snowflake/BigQuery/S3 Data Sharing
Early Access to Upcoming Data Observability Agents
Pipeline Monitoring
Deployment
Deployment Type
SaaS
SaaS
SaaS/Hybrid/Self-hosted
SLA & Support
Standard
Priority
Enterprise (24/7, white-glove)
Onboarding & Success Program
Guided
Dedicated
Enterprise (including executive sponsorship)
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.
Looking for more?
Customer Story
Automating Data Quality at Scale: Inside Penguin Random House’s Sifflet Implementation
Blogpost
Data Observability, Five Years In: Why the Old Playbook Doesn’t Work Anymore
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Checklist
Access this (really) free checklist that helps you pick a data observability platform that pays off in speed, trust & measurable impact.
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One form, one message, one step closer to data you can actually trust.
Get in touch
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Frequently asked questions
What impact did Sifflet have on fostering a data-driven culture at Meero?
Sifflet’s intuitive UI and real-time data observability dashboards empowered even non-technical users at Meero to understand data health. This transparency helped build trust in data and promoted a stronger data-driven culture across the organization.
Why is data quality monitoring so important for data-driven decision-making, especially in uncertain times?
Great question! Data quality monitoring helps ensure that the data you're relying on is accurate, timely and complete. In high-stress or uncertain situations, poor data can lead to poor decisions. By implementing scalable data quality monitoring, including anomaly detection and data freshness checks, you can avoid the 'garbage in, garbage out' problem and make confident, informed decisions.
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 is data observability, and why is it important for companies like Hypebeast?
Data observability is the ability to understand the health, reliability, and quality of data across your ecosystem. For a data-driven company like Hypebeast, it helps ensure that insights are accurate and trustworthy, enabling better decision-making across teams.
What is a data observability platform and why does it matter?
A data observability platform is a system that continuously monitors the health and reliability of your data pipelines. It helps you detect issues like schema changes, volume drops, or stale data before they impact business decisions. By combining technical telemetry with business context, platforms like Sifflet ensure data trust across the entire organization.
What is dbt Impact Analysis and how does it help with data observability?
dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.
How does data observability complement a data catalog?
While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.
What should I look for in a data lineage tool?
When choosing a data lineage tool, look for easy integration with your data stack, a user-friendly interface for both technical and non-technical users, and complete visibility from data sources to storage. These features ensure effective data observability and support your broader data governance efforts.
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