Home
Pricing
%%Flexible pricing for %%every stage of data maturity
Build data trust at your own pace, from first monitors to enterprise-wide observability.
Got Snowflake credits sitting around? You can use them here.
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
Checklist
Access this (really) free checklist that helps you pick a data observability platform that pays off in speed, trust & measurable impact.
Let's make it a thing
One form, one message, one step closer to data you can actually trust.
Get in touch
Still have a question in mind ?
Contact Us
Frequently asked questions
Why should companies invest in data pipeline monitoring?
Data pipeline monitoring helps teams stay on top of ingestion latency, schema changes, and unexpected drops in data freshness. Without it, issues can go unnoticed and lead to broken dashboards or faulty decisions. With tools like Sifflet, you can set up real-time alerts and reduce downtime through proactive monitoring.
Why is field-level lineage important in data observability?
Field-level lineage gives you a detailed view into how individual data fields move and transform through your pipelines. This level of granularity is super helpful for root cause analysis and understanding the impact of changes. A platform with strong data lineage tracking helps teams troubleshoot faster and maintain high data quality.
How does Sifflet help close the observability gap for Airbyte pipelines?
Great question! Sifflet bridges the observability gap for Airbyte by using our Declarative Lineage API and a custom Python script. This allows you to capture complete data lineage from Airbyte and ingest it into Sifflet, giving you full visibility into your pipelines and enabling better root cause analysis and data quality monitoring.
How does the Sifflet AI Assistant improve data observability at scale?
The Sifflet AI Assistant enhances data observability by automatically fine-tuning your monitoring setup using machine learning and dynamic thresholds. It continuously adapts to changes in your data pipelines, reducing false positives and ensuring accurate anomaly detection, even as your data scales globally.
How does Sifflet support data quality monitoring for business metrics?
Sifflet uses ML-based data quality monitoring to detect anomalies in business metrics and alert users in real time. This enables both data and business teams to quickly investigate issues, perform root cause analysis, and maintain trust in their data.
How has the shift from ETL to ELT improved performance?
The move from ETL to ELT has been all about speed and flexibility. By loading raw data directly into cloud data warehouses before transforming it, teams can take advantage of powerful in-warehouse compute. This not only reduces ingestion latency but also supports more scalable and cost-effective analytics workflows. It’s a big win for modern data teams focused on performance and throughput metrics.
How do classification tags support real-time metrics and alerting?
Classification tags help define the structure and importance of your data, which in turn makes it easier to configure real-time metrics and alerts. For example, tagging a 'country' field as low cardinality allows teams to monitor sales data by region, enabling faster anomaly detection and more actionable real-time alerts.
Can I use Sifflet to detect bad-quality data in my Airflow pipelines?
Absolutely! With Sifflet’s data quality monitoring integrated into Airflow DAGs, you can detect and isolate bad-quality data before it impacts downstream processes. This helps maintain high data reliability and supports SLA compliance.
PRODUCT
Subscribe to our blog
The latest in data observability sent to your inbox.