Data observability · Sifflet

Sifflet

End-to-end data observability platform combining anomaly detection, lineage and governance controls for data and AI assets.

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Sifflet monitors data pipelines end to end, applying machine-learning anomaly detection alongside manually authored rules to catch freshness, volume, schema and distribution problems. It builds lineage that extends beyond warehouse tables into BI dashboards and, increasingly, AI/ML assets, so an incident can be traced to its source and its downstream impact. AI-assisted incident management helps suggest likely root causes, and data contracts let teams codify expectations between producers and consumers of a dataset. A governance layer adds access controls and cost monitoring on top of the observability features. Sifflet is offered as cloud SaaS with sales-assisted and self-serve procurement paths, and as hybrid/dedicated deployments for enterprise customers, priced across three named tiers based on the number of monitored assets.

At a glance

Vendor Sifflet
Pricing model Quote only
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Teams wanting observability paired with governance controls in one platform.

Pricing

Three named tiers scaled by monitored assets; no public dollar prices, self-serve or sales-assisted procurement.

Plan Price Notes
Entry Custom up to 500 monitored assets; self-serve/marketplace procurement
Growth Custom up to 1,000 monitored assets; sales-assisted/marketplace procurement
Enterprise Custom 1,000+ monitored assets, scales flexibly; direct enterprise sales or channel

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • ML-based anomaly detection
  • Automated and manual data quality rules
  • End-to-end lineage including BI and AI assets
  • AI-assisted incident management and root cause analysis
  • Data contracts
  • Governance and access controls
  • Cost monitoring

Integrations

Profile last reviewed September 21, 2026

Alternatives

Monte Carlo Data observability platform using ML-based anomaly detection to catch broken pipelines before they reach dashboards. Bigeye Data observability platform that auto-generates quality metrics and anomaly alerts across warehouses and lakes. Acceldata Enterprise data observability platform covering data reliability, pipeline performance and cloud data cost optimization.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Diagnostic analytics Analytics focused on explaining why something happened by drilling into patterns, correlations, and root causes behind results. Root cause analysis (RCA) A structured process for tracing a problem back through its contributing factors to the underlying cause, not just its symptoms.

Related guides

How to choose a data observability tool Pick a data observability tool by how it generates coverage, where it needs to run relative to your data, and whether you're already living inside dbt.