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Bigeye
Data and AI trust platform that combines observability, lineage, and policy controls so teams know how data feeds models and dashboards. Bigeye acquired the lineage vendor Data Advantage Group in June 2023.
Enterprise data teams that need lineage, quality monitoring, and tighter control over how AI systems use data.
Small teams that only want autonomous table monitoring with minimal setup.
Verdict
Bigeye is a data observability and AI trust platform. Data teams use it to monitor quality, follow lineage, and put policy around how AI systems use data. It matters when broken pipelines and unclear AI data use show up as business risk, not only as a dashboard glitch.
Score Breakdown
How Bigeye scores in the categories that matter to its buyers.
Pricing
Bigeye prices through sales-assisted packages. Model below reflects common mid-market packaging themes for 2026; confirm lineage and AI governance modules directly.
Sales-assisted data observability and AI trust platform. Expect annual packages sized by data sources and modules; ask what lineage and AI policy features are included.
The Field at a Glance
How Bigeye compares with other AI data quality & observability vendors we reviewed, by Overall Score and relative typical engagement cost.
Bigeye scores 7.3 in AI data quality & observability, under Anomalo (7.6) and Soda (7.4), and level with Sifflet (7.3). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Data observability | Strong | Monitors tables for freshness, volume and quality. |
| Lineage & impact | Strong | Lineage from the Data Advantage Group acquisition shows which assets a change affects. |
| AI data trust controls | Strong | Applies policies to the data AI systems read. |
| No-config autonomous DQ | Mixed | Anomalo sets up monitoring with less configuration. |
| Lightweight dbt tests only | Mixed | Teams with a handful of dbt tests can start with Soda Core. |
| Compliance automation | Poor | Bigeye does not cover compliance automation. |
Review Excerpts
Representative buyer views drawn from public information about Bigeye. Paid reviews and pay-for-play sites were excluded.
“Monitors on our key tables alert before the dashboards break, and lineage shows who is affected.”
Representative buyer view
“We trace a bad table to the dashboards and models that depend on it before we message stakeholders.”
Representative buyer view
“Pricing is by quote, and a team with a few dbt tests would pay for a broader platform than it needs.”
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: Bigeye’s website
Key Leadership
Kyle Kirwan
Co-founder
Methodology
This page is an independent evaluation of Bigeye for buyers comparing options in AI data quality & observability. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Bigeye 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)
- Data observability & lineage
- AI data trust / governance
- Issue resolution workflows
- Autonomous no-config monitoring
- 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
Bigeye is graded here as AI data quality & observability. Criteria scores can move as more review volume and product checks are added.