Every data team eventually hits the same wall: a dashboard shows a number that's obviously wrong, and nobody can say why until someone spends half a day tracing it back through five pipelines to find a column that's been silently null for two weeks. That's "data downtime" — and it's gotten more expensive now that broken data doesn't just mislead a dashboard, it feeds straight into an AI agent that confidently hallucinates a wrong answer from it. The tools below exist to catch that before it reaches anyone: they watch your tables and pipelines continuously, flag anomalies, and in some cases fix or block the problem automatically instead of waiting for someone to notice.
The short version: if you want something free to start with today, Great Expectations is open-source and genuinely free at its core. If you want AI that learns what "normal" looks like without you writing rules by hand, Anomalo and Monte Carlo are the category's most established anomaly-detection platforms. If your data can't leave your own database for compliance reasons, Digna runs its checks in-place. And if governance — a certified, company-wide definition of what your data actually means — matters more than raw anomaly detection, Collibra is built for that.
What "data quality" and "data observability" actually cover
Two overlapping ideas. Data quality means checking that data matches explicit rules — this column shouldn't be null, this value should be between 0 and 100, this ID should be unique. Data observability is the broader, often AI-driven version: watching pipelines and tables for anything that looks abnormal compared to their own history, even if nobody wrote a rule for it. Most tools below do a mix of both, with different amounts of "write it yourself" versus "let the AI figure out what normal looks like."
1. Monte Carlo — the pioneer of data downtime monitoring
Monte Carlo is often credited with coining "data downtime" as a category, and it's expanded from watching data pipelines to also watching AI agents in production — useful now that broken data and broken agents are frequently the same incident.
Price: consumption-based (credits), four tiers, no public pricing — a quote is required.
Forces: the most established brand in data observability specifically; covers both data pipelines and AI agents rather than just tables; broad integration coverage across the modern data stack.
Limites: no free tier to test before committing; consumption-based pricing is harder to forecast than a flat subscription; you'll need a sales conversation to get a real number.
Pick Monte Carlo if you want the most established, broadest-coverage platform and you're already past the "do we need this" question.
2. Anomalo — AI that learns "normal" without manual rules
Anomalo uses AI to automatically learn what typical data looks like in your tables and flags deviations, so nobody has to write a validation rule for every single column across thousands of tables.
Price: enterprise model, custom pricing through a consultative sales process.
Forces: anomaly detection with effectively zero manual configuration; scales to thousands of tables without linear effort; multiple specialized AI agents rather than one generic model.
Limites: no public pricing; less precise control than hand-written rules if you need to catch something very specific; genuinely overkill for a small team with a handful of tables.
Pick Anomalo if you have too many tables to realistically write manual rules for each one and want AI to cover the gap.
3. Bigeye — lineage, anomalies and sensitive data in one platform
Bigeye positions itself as an "AI trust platform" — combining lineage tracking, anomaly detection and sensitive-data discovery so enterprises can verify data is safe and reliable enough to actually feed into AI systems, not just a dashboard.
Price: enterprise SaaS, quote-based, with a conditional free trial tied to Snowflake or Claude Code usage.
Forces: bundles lineage, anomaly detection and sensitive-data discovery instead of three separate tools; real-time policy enforcement; a free trial path exists if you're already on Snowflake or Claude Code.
Limites: no public pricing outside the trial; the free trial is conditional, not universally available; narrower in scope than a full governance suite if that's what you actually need.
Pick Bigeye if you specifically need lineage, anomaly detection and sensitive-data discovery bundled together, especially if you're feeding data into AI systems.
4. Great Expectations — free, open-source, code-first
Great Expectations is the most widely used open-source tool in this space: you define what your data should look like as Python code — an "expectation" — and it validates every pipeline run against that automatically.
Price: GX Core is free (Apache 2.0); GX Cloud Developer tier is free; Team and Enterprise are quote-based.
Forces: genuinely free and open-source at the core, not a trial; a large, established community; native integrations with Airflow, Databricks and Snowflake.
Limites: requires writing Python code, so it's not a point-and-click tool for non-engineers; no automatic AI anomaly detection out of the box; GX Cloud's paid tiers don't publish pricing.
Pick Great Expectations if your team is comfortable in Python and you want a genuinely free, open-source foundation instead of a vendor relationship.
5. Soda — data contracts with AI-assisted fixes
Soda is built around "data contracts" — explicit, collaborative agreements between data producers and consumers about what a dataset should look like — with AI that detects, explains, and can help fix anomalies when a contract breaks.
Price: free tier with limited SPUs (Soda Processing Units); Team is $750/month; Enterprise is quote-based.
Forces: a real free tier to actually start with, not just a trial; published Team pricing ($750/month), unusual in this category; the data-contracts framing makes ownership and accountability explicit between teams.
Limites: the jump from free to $750/month is a significant step with no obvious middle tier; Enterprise pricing is still quote-only; relying on AI to auto-fix anomalies requires a level of trust some teams aren't ready to extend yet.
Pick Soda if you want a real free tier to start today and data contracts as a framework for who's accountable when something breaks.
6. Digna — runs in your database, never copies your data out
Digna runs its checks directly inside your database instead of extracting data to a separate system, and prices by the number of tables you actually monitor rather than a flat platform fee.
Price: base fee plus a per-table price for whichever tables you enable — no hidden fees, per the vendor.
Forces: in-database processing means your data never leaves your own environment, which matters a lot for finance, healthcare and public-sector residency requirements; on-premise deployment is an option; pricing is modular and scales with what you actually monitor instead of a flat enterprise fee.
Limites: a much smaller, less-established brand than Monte Carlo or Anomalo; the exact price still requires a quote; a smaller ecosystem of integrations and community resources.
Pick Digna if data residency or on-premise deployment is a hard requirement and your data genuinely cannot leave your own database.
7. Collibra — governance and a certified catalog, not just quality checks
Collibra takes a different angle from the rest of this list: instead of just flagging anomalies, it builds a formal, certified catalog of what your business data actually means, then feeds that context to AI models so they answer questions correctly instead of guessing.
Price: enterprise model, pricing via demo and sales conversation.
Forces: an established data catalog and governance platform, not a newer entrant; a certified ontology genuinely reduces AI hallucinations grounded in ambiguous data definitions; strong footing in regulated industries that need formal governance, not just quality alerts.
Limites: no public pricing; requires real organizational buy-in and process change to get value, not just a tool install; overlaps in scope with dedicated governance platforms like Ataccama, so it's worth comparing both.
Pick Collibra if you're a large, regulated organization that needs a certified, company-wide definition of your data — governance, not just anomaly alerts.
How they compare
| Tool | Best for | Starting price | Approach |
|---|---|---|---|
| Monte Carlo | Broadest data + AI agent observability | Custom (consumption-based) | Anomaly detection |
| Anomalo | Zero-config AI anomaly detection at scale | Custom (enterprise) | AI anomaly detection |
| Bigeye | Lineage + anomalies + sensitive-data discovery | Custom, conditional free trial | Observability + governance |
| Great Expectations | Free, code-first quality checks | Free (Apache 2.0) | Rule-based, code |
| Soda | Real free tier + data contracts | Free, then $750/mo | Data contracts + AI |
| Digna | In-database, data residency requirements | Base + per-table (custom) | In-database checks |
| Collibra | Governance + certified catalog | Custom (enterprise) | Governance + catalog |
None of these are strictly mutually exclusive — it's common to run Great Expectations checks in a pipeline for free and layer a paid observability platform like Monte Carlo or Anomalo on top once the manual-rules approach stops scaling. Start with whichever gap is actually costing you time right now: if nobody's validating your data at all, Great Expectations gets you real coverage for free this week; if validation exists but broken data still reaches production undetected, that's when the AI-driven observability platforms start earning their price tag.