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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
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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.
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Frequently asked questions
What role does data lineage tracking play in root cause analysis?
Data lineage tracking is essential for root cause analysis because it shows exactly how data flows through your pipeline. With tools like Sifflet, teams can trace issues back to their origin in seconds instead of days. This visibility helps engineers quickly identify and fix the 'first wrong turn' in complex environments, like Adaptavist did during their monorepo-to-polyrepo migration.
How can I monitor the health of my pipelines in a decentralized data architecture?
With decentralized architectures, data pipeline monitoring becomes essential. Tools like Sifflet offer centralized visibility across domain-owned pipelines, helping teams stay aligned, detect anomalies, and ensure SLA compliance without slowing down local innovation.
What role does Sifflet play in Etam’s data governance efforts?
Sifflet supports Etam by embedding data governance into their workflows through automated monitoring, anomaly detection, and data lineage tracking. This gives the team better visibility into their data pipelines and helps them troubleshoot issues quickly without slowing down innovation.
How does data quality monitoring help improve data reliability?
Data quality monitoring is essential for maintaining trust in your data. A strong observability platform should offer features like anomaly detection, data profiling, and data validation rules. These tools help identify issues early, so you can fix them before they impact downstream analytics. It’s all about making sure your data is accurate, timely, and reliable.
What is data observability and why is it important?
Data observability is the ability to monitor, understand, and troubleshoot data systems using real-time metrics and contextual insights. It's important because it helps teams detect and resolve issues quickly, ensuring data reliability and reducing the risk of bad data impacting business decisions.
How does the improved test connection process for Snowflake observability help teams?
The revamped 'Test Connection' process for Snowflake observability now provides detailed feedback on missing permissions or policy issues. This makes setup and troubleshooting much easier, especially during onboarding. It helps ensure smooth data pipeline monitoring and reduces the risk of refresh failures down the line.
How do Subdomains improve data observability at scale?
Subdomains help scale data observability by letting you organize your domains into a hierarchy that mirrors your org chart. This means teams can manage their own data pipeline monitoring while the platform team maintains strategic oversight. It’s a great way to improve clarity, security, and speed across your observability platform.
Why should data teams care about data lineage tracking?
Data lineage tracking is a game-changer for data teams. It helps you understand how data flows through your systems and what downstream processes depend on it. When something breaks, lineage reveals the blast radius—so instead of just knowing a table is late, you’ll know it affects marketing campaigns or executive reports. It’s a critical part of any observability platform that wants to move from reactive to proactive.
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