Modern data teams (100-3,000 employees) on Snowflake, BigQuery, or Databricks who want ML-driven anomaly detection without writing rules and value autotuning thresholds; teams that prefer a metric-first architecture.
Large regulated enterprises wanting maximum lineage and BI breadth (Monte Carlo broader), teams already committed to Datadog (Metaplane integrates), or buyers wanting fully transparent published pricing.
Is Bigeye a trustworthy vendor?
- 2021-03-09$17M Series A led by Sequoia
- 2022-08-24$45M Series B led by CoatueCoatue-led with Sequoia participation; round positioned the company for the 2024-2026 cycle but has not been refreshed since.
- 2023-11-08Bigeye Metrics architecture releaseMetric-first primitives positioned as the differentiator versus rule-driven peers.
- 2025-04-15Lineage expansion to BI tools (beta)Closing the BI-lineage gap versus Monte Carlo; production references still building.
What 95 reviews actually say
Synthesized from G2, Capterra, Reddit, Trustpilot. Patterns >15% prevalence shown.
Praise patterns
- ML-driven anomaly detection reduces rule-writing meaningfully87% →
- Metric-first architecture (Bigeye Metrics) is clean and reusable71% →
- Better pricing transparency than Monte Carlo at mid-market64% →
- Usable UI for non-engineers51% →
Complaint patterns
- Feature breadth trails Monte Carlo at enterprise tier78% →
- BI lineage less mature than Monte Carlo64% ↓
- Aug 2022 Series B has not been refreshed; valuation reset risk41% →
- Enterprise references thinner than Monte Carlo38% ↓
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“We picked Bigeye over Monte Carlo on price and the metric primitives. The detection quality is comparable on our stack; the lineage gap matters less than the sales pitch suggested.”
Staff Data Engineer, mid-market fintech· G2 · 2026-03-18
What buyers actually pay
62 anonymized deal disclosures · last updated 2026-05-01
| Company size | Median annual |
|---|---|
| 100-500 employees | $48,000 |
| 500-2,000 employees | $120,000 |
| 2,000+ employees | $280,000 |
Auto-verified certifications
Editorial: Strengths
- ML-driven anomaly detection with autotuning thresholds out-of-box
- Metric-first architecture (Bigeye Metrics) is clean and reusable
- Strong Snowflake, BigQuery, Redshift, Databricks coverage
- Usable UI for analysts and stewards (not just engineers)
- Slack and PagerDuty incident routing
- Founders shipped Uber Michelangelo data quality; credible technical pedigree
- Partial pricing transparency on website (better than Monte Carlo)
Editorial: Weaknesses
- Feature breadth trails Monte Carlo at enterprise tier
- BI lineage (Looker, Tableau, Power BI) less mature than Monte Carlo
- Aug 2022 Coatue Series B has not been refreshed; valuation reset risk
- Enterprise references thinner than Monte Carlo
- Pricing opaque at upper tiers despite partial public transparency
Key features & integrations
- +ML-driven anomaly detection with autotuning thresholds
- +Bigeye Metrics (metric-first primitives, reusable)
- +Freshness, volume, schema, distribution monitoring
- +Lineage across warehouse and dbt
- +Slack and PagerDuty incident routing
- +Custom SQL rules
- +Issue management with annotations
- +API and webhook integrations
Read our full ranking of Data Observability Software
Bigeye ranks #2 in our editorial review of 10 data observability software platforms. The deep-dive covers methodology, comparison tables, decision matrix, migration scoring, and FAQs.
Read the full rankingClosest alternatives in Data Observability Software
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