"Data observability" is the data-engineering version of an alarm system: instead of finding out a dashboard is wrong when your CEO asks about it in a meeting, these tools watch your pipelines around the clock and flag the broken table, the missing rows, or the schema change before anyone downstream notices. Monte Carlo and Bigeye are two of the best-known players in that space, and each lists the other as a direct alternative. Here's the actual difference.
Monte Carlo
Monte Carlo is often called the pioneer of "data downtime" monitoring — the company that popularized the category. It watches your pipelines, and increasingly your AI agents in production, and tells you when something breaks before your users or your boss notice.
For who: mid-size to large companies running data pipelines and AI agents in production who need broad coverage across both.
Price: quote-only. Consumption-based (credits), spread across four tiers, with no published pricing.
Strengths: the most established name in the category, with the credibility that comes with it; covers both traditional data pipelines and AI agents under one roof; wide range of integrations with the modern data stack.
Limits: no free tier at all; consumption-based pricing is less predictable than a flat subscription; you can't get a number without talking to sales.
Bigeye
Bigeye covers similar ground — lineage, anomaly detection, sensitive-data discovery — but positions itself specifically as an "AI trust platform": its pitch is helping enterprises verify that their data is safe and reliable enough to actually feed into AI systems.
For who: enterprises with critical data and active AI initiatives who want lineage, anomaly detection and sensitive-data discovery bundled into one product.
Price: also quote-only, enterprise SaaS. There's a free trial, but it's conditional on your stack including Snowflake or Claude Code.
Strengths: combines lineage, anomaly detection and sensitive-data discovery in a single tool; supports real-time policy enforcement, not just alerting after the fact; the Snowflake/Claude Code trial gives a concrete, if narrow, way to try it for free.
Limits: also no public pricing; the free trial only applies if you're already on the specific stack it targets; narrower in scope than a full data governance suite.
Side by side
| Monte Carlo | Bigeye | |
|---|---|---|
| Core focus | Data + AI agent observability, broad coverage | Lineage, anomaly detection, sensitive-data discovery — "AI trust" |
| Pricing model | Consumption-based credits, 4 tiers, quote-only | Enterprise SaaS, quote-only |
| Free trial | None | Yes, but conditional on Snowflake/Claude Code |
| Standout feature | Broadest coverage, covers AI agents in production too | Real-time policy enforcement on top of detection |
| Best known for | Being the category's pioneer | Data trust specifically for AI use cases |
Verdict
Pick Monte Carlo if you want the most established, broadest-coverage tool in the category, and you're monitoring both classic data pipelines and AI agents running in production — and you're fine with a sales conversation to get pricing.
Pick Bigeye if your priority is narrower and sharper: proving your data is trustworthy enough to feed into AI systems, with lineage and sensitive-data discovery baked in — and especially if you're already on Snowflake or Claude Code and want to actually try it before buying.
Neither publishes pricing, so in practice the decision usually comes down to a proof-of-concept with your own pipelines rather than a spec sheet. Both are quote-only enterprise tools — if your team or budget is smaller, it's worth checking Bigeye's alternatives Anomalo and Collibra, or Monte Carlo's alternative Anomalo, before committing to either.