Digna
A data quality and observability tool that runs its checks directly inside your database instead of copying data out to a separate system, with pricing based on the number of tables you actually monitor rather than a flat platform fee.
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Description
A lot of data observability tools work by pulling a copy of your data into their own system to analyze it, which raises questions about where sensitive data ends up and adds latency. Digna takes a different approach: it runs its statistical and machine learning anomaly detection in-database, directly where your data already lives, which matters for organizations in finance, healthcare, or the public sector where data residency and access control are non-negotiable.
The platform is split into five modules — anomaly detection, analytics, timeliness, validation, and schema tracking — and can deploy in a private cloud or fully on-premise for organizations that can't use a public SaaS product for compliance reasons. Pricing follows a base-fee-plus-per-table model: you pay to unlock the platform, then a per-table rate as you activate monitoring on more of your database, with Digna explicitly advertising no hidden platform or API fees layered on top.
💬 Our review
The short version: Digna's in-database, on-premise-capable approach is the whole reason to consider it — if your organization can't legally or contractually let data leave its own environment for a SaaS quality tool, most competitors are simply off the table and Digna becomes one of the few real options.
Against SaaS-first competitors like Anomalo, Bigeye, or Monte Carlo, which typically process data in their own cloud infrastructure, Digna's in-database and on-premise deployment directly addresses data residency requirements common in finance, healthcare, and government. The modular, per-table pricing is also more transparent than the fully quote-based enterprise pricing typical of this category, though the base fee plus per-table structure still requires modeling out your actual table count to estimate real cost. As a newer, less widely known name than Monte Carlo or Anomalo, it's worth doing extra diligence on production references, especially for a first deployment in a regulated environment. <!-- ai-generated -->
💰 Pricing
📊 Global score
🤖 AI-enriched data
Base fee + prix par table activée, sans frais cachés
Pros
Traitement in-database, données ne quittent jamais l'environnement
Déploiement on-premise possible
Pricing modulaire transparent
Cons
Marque moins établie
Prix exact nécessite un devis
Écosystème plus petit