Bigeye is the data observability platform built by Kyle Kirwan and Egor Gryaznov, two of the engineers who built Uber's internal data quality system. Where Monte Carlo wins on brand and category-creator status, Bigeye competes on a more technical positioning: a deeper metric library, finer-grained SLAs, and a vibe that appeals to data engineers who want to see what is being measured rather than trust a black box.

If Monte Carlo is the Datadog of data observability (broad, marketing-led, easy-button), Bigeye is closer to Honeycomb (technical, metric-first, built by people who lived through the problem at scale).

The Origin Story

Kyle Kirwan was the first product manager on Uber's data platform team. Egor Gryaznov was a staff engineer on the same team. Together they were responsible for Databook, Uber's internal data catalog, and a related tool that monitored the health of Uber's thousands of Hive tables. Uber, like every hyperscaler before it, had to invent its own data quality tooling because nothing on the market in 2017-2018 worked at that scale.

When they left Uber to start Bigeye in 2019 (the company was originally called Toro), they were essentially rebuilding what they had learned at Uber, this time as a commercial product everyone else could buy. The Uber pedigree gave them instant technical credibility, and an early Sequoia term sheet.

The timing was interesting: Bigeye and Monte Carlo were founded within months of each other in 2019. They have been racing each other ever since. Monte Carlo got the brand. Bigeye got the engineering respect. Neither has decisively won.

What the Product Does

The core abstraction in Bigeye is the metric. A metric is a SQL expression evaluated against a column at a regular interval — count_null(email), avg(order_total), pct_unique(user_id). Bigeye ships with a library of about 70 pre-built metrics (the company calls these "autometrics") and lets you write custom ones.

Each metric gets a learned baseline, an SLA, and an alerting policy. When a metric drifts outside its expected range, Bigeye fires an alert. This is roughly the same shape as Monte Carlo, but the metric-centric framing gives Bigeye a slightly different feel: instead of "incidents" you get "broken metrics," and instead of "monitors" you get "SLAs." The vocabulary is borrowed from SRE, deliberately.

Bigeye also pioneered some features that competitors copied:

• Deltas / data diffs. Comparing two tables (or two versions of the same table) to find where they differ. Useful for migrations and refactors.
• Issues and lineage. Standard observability fare, mostly on par with Monte Carlo.
• Open API for custom metrics. A bigger emphasis than Monte Carlo on letting engineers script their own checks.

The company has more recently leaned into AI-assisted monitoring, like most of the category, with features for automatically suggesting which tables to monitor and which metrics matter most.

The Opinionated Take

Bigeye is the data engineer's data observability tool. It is more technical, more transparent, and has historically been more responsive to feedback from hands-on customers. If you have an opinionated data platform team that wants to see SQL definitions for every check, Bigeye feels more natural than Monte Carlo.

The downside is brand and sales motion. Monte Carlo has dramatically more presence in the analyst reports (Gartner, Forrester) and at conferences. When a CDO reads a McKinsey report on data quality, the report mentions Monte Carlo. Bigeye has had to grind its way into deals that Monte Carlo gets handed by analysts. This is a structural disadvantage in enterprise sales.

The funding picture also favors Monte Carlo: Bigeye has raised about $66M to Monte Carlo's $236M. In a category where enterprise sales is expensive and slow, that funding gap matters a lot.

The prediction: Bigeye will probably end up either being acquired (a likely buyer is one of the cloud warehouses, or a broader data platform like Informatica or Collibra) or finding a niche as the technical/engineer-led alternative to Monte Carlo. The category cannot sustain three or four large independents, so consolidation is coming.

Where Bigeye Fits in the Stack

Like all data observability tools, Bigeye is horizontal. It connects to:

• Snowflake, Databricks, BigQuery, Redshift, and on-prem databases (Postgres, SQL Server)
• dbt for model lineage
• Slack, PagerDuty, Jira, Opsgenie for alerting
• Airflow and other orchestrators for run-status correlation

A typical Bigeye buyer is a data platform team at a 500-5000 person company that has already invested in a serious data platform and wants quality monitoring as a first-class engineering concern, not a marketing checkbox.