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Sifflet
Data observability platform that combines monitoring, field-level lineage, a data catalog, and AI agents for root cause and incident triage. Sifflet raised an $18 million Series B in June 2025 from EQT Ventures, Mangrove Capital Partners and Capmont Technology.
Data teams on Snowflake, BigQuery, or Databricks who need lineage that shows business impact, not just failed tests.
Teams that only need a few dbt tests, or very small stacks where a catalog and lineage graph will sit unused.
Verdict
Sifflet is a data observability platform founded in Paris in 2021; the name is French for whistle. Data teams use it to watch tables for freshness, volume, and schema changes, trace a broken number back through lineage, and tell the people who use a dashboard when it is wrong. It stands out because it puts monitoring, lineage, and a catalog in one place, so data engineers and business users look at the same picture when data breaks.
Score Breakdown
How Sifflet scores in the categories that matter to its buyers.
Pricing
Sifflet prices on the number of data assets monitored, with three tiers. Prices are given on request; Snowflake credits can be used to pay.
| Plan / SKU | Meter | Price (USD) | What stands out |
|---|---|---|---|
| Entry | assets monitored | Up to 500 | Self-serve or cloud marketplace |
| Growth | assets monitored | Up to 1,000 | Sales-assisted; adds governance and SSO |
| Enterprise | assets monitored | 1,000+ | SaaS, hybrid, or self-hosted; 24/7 support |
The Field at a Glance
How Sifflet compares with other AI data quality & observability vendors we reviewed, by Overall Score and relative typical engagement cost.
Sifflet scores 7.3 in AI data quality & observability, under Anomalo (7.6) and Soda (7.4), and level with Bigeye (7.3). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Freshness, volume, schema monitors | Strong | Core Sifflet motion. |
| Field-level lineage | Strong | Upstream cause and downstream impact. |
| Catalog with health status | Strong | Same tool as monitoring. |
| Pipeline checks in Airflow or dbt | Mixed | Flow Stopper helps; setup needed. |
| Plug-and-play for tiny teams | Mixed | Takes configuration. |
| Compliance automation | Poor | Sifflet does not cover compliance automation. |
Review Excerpts
Excerpts from company materials and public coverage of Sifflet, plus representative buyer views drawn from public information. Paid reviews and pay-for-play sites were excluded.
“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.”
“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.”
“It takes configuration to wire monitors into Airflow and dbt, so a tiny team may find a plug-and-play tool faster to start.”
Representative buyer view
Where customer reviews couldn't be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums, and product documentation.
Based on: Sifflet’s website
Company Facts
- Founded
- 2021 (CB Insights)
- Headquarters
- Paris, France (CB Insights)
- Total funding raised
- $31.32M (CB Insights)
- Key investors
- Mangrove Capital Partners, EQT Ventures, Capmont Private Equity, Bessemer Venture Partners, Agoranov (CB Insights)
- Website
- siffletdata.com
Key Leadership
Salma Bakouk
Co-founder and CEO
Methodology
This page is an independent evaluation of Sifflet for buyers comparing options in AI data quality & observability. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Sifflet did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria sit under it in two groups.
- Buyer outcomes (for AI data quality & observability)
- Monitoring & anomaly detection
- Lineage & impact analysis
- Catalog & business context
- Lightweight setup
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
Sifflet is graded here as AI data quality & observability. Criteria scores can move as more review volume and product checks are added.