Bigeye vs Monte Carlo: Pricing, Cost per Monitored Table and Which to Buy

Bigeye and Monte Carlo both publish a real US list price on AWS Marketplace and neither publishes one on its own website. Read from the live listings on 9 September 2026, Bigeye sells a Starter Package at $45,000.00 for 12 months covering 100 Active Monitored Tables, and an Enterprise Starter Package at $75,000.00 covering 300. Monte Carlo sells one dimension called a Monte Carlo Credit at $50,000.00 for 12 months and never says what a Credit entitles you to. Bigeye is therefore the only vendor in this category where you can compute a cost per monitored table: $450 a year at the entry tier and $250 at the larger one.

That difference matters more than the ten percent gap between the two entry prices. One vendor sells you a countable thing. The other sells you a unit it declines to define. Everything below comes from the two live listings and from each vendor's own pricing page, read the same day.

These Marketplace pages are client-side rendered, so the prices are not visible to an ordinary fetch and have to be read out of the page's embedded data. That is the main reason the comparison articles ranking for this question contain no dollar figures at all.

Bigeye pricing vs Monte Carlo pricing, side by side

Both vendors offer a 12-month term only. Neither exposes a 24 or 36 month price, which is worth knowing if your procurement team assumes a longer commitment earns an automatic discount. Across every vendor in this category that does publish multi-year terms, the pattern we keep measuring is that they are exactly linear. Collibra lists $170,000, $340,000 and $510,000 for 12, 24 and 36 months. At list, a longer term buys nothing, and any discount is a negotiated concession rather than a published one.

Cost per monitored table, the number neither vendor computes

Divide each Bigeye package by its stated entitlement and the pricing model becomes legible in a way that no other vendor in this category allows.

Tripling the number of monitored tables costs about 1.67 times as much, so the effective rate per table falls by roughly 44 percent between the two published tiers. That is a genuine volume break, and it is unusually generous compared with the flat rates we find elsewhere. Datafold, for example, lists $15,000 for five developers and $30,000 for ten, which divides to exactly $3,000 per developer with no volume break at all.

The practical consequence is that the entry package is the expensive way to buy Bigeye per unit of coverage. If your warehouse has 150 tables worth monitoring, you are in an awkward spot: the Starter Package does not cover them and the Enterprise Starter Package charges you for 150 you will not use. Ask what sits between the two published tiers before you assume the rate card is the whole menu.

You cannot run this calculation from the Marketplace listing alone, because it never says how many credits $50,000 buys. It can be run from Monte Carlo's own documents, though. Two public order forms on montecarlo.ai print the credit rate, $0.18 on Start and $0.28 on Scale, and the docs publish the consumption rate card: a Table Monitor is 1.75 credits a day for each of the first 1,000 tables. That works out to $114.97 or $178.85 per table per year, which is well under Bigeye's $450 at the 100-table size. The full arithmetic, bracket by bracket, is on our Monte Carlo pricing page.

What $50,000 actually buys at Monte Carlo

Monte Carlo's own pricing page names four tiers, Start, Scale, Enterprise and Business Critical, and explains the model in one sentence: "Buy credits and consume them based on our Consumption Rates. Cost per credit depends on the tier that makes sense for you."

Read that against the Marketplace listing and a gap opens up. The listing publishes a single price of $50,000.00 for one Credit and does not say which of the four tiers that price belongs to. So the one public number Monte Carlo has cannot be located on the vendor's own published tier structure. You know the contract unit costs $50,000, you do not know which tier it belongs to, and the listing does not say how many credits it contains. The credit rate itself is published elsewhere on Monte Carlo's site, on the Start and Scale order forms, at $0.18 and $0.28 per credit.

This is a shape we keep finding among enterprise data vendors: a transactable listing is obliged to carry a real dollar amount, but nothing obliges it to say what the amount buys. Alation sells its Data Catalog in units and never defines a unit. Collibra exposes one platform dimension with no stated entitlement. Monte Carlo sells a Credit. In each case the published price is real and the quantity is not.

One housekeeping note if you are checking this yourself: Monte Carlo's site now lives at montecarlo.ai, and the old montecarlodata.com URLs return a 301 redirect. Older comparison pages still cite the previous domain.

The connector ceiling that does not scale

The most useful detail in Bigeye's rate card is easy to read past. Both packages include "Two Core Lineage Plus Connectors". Not two per hundred tables. Two, at 100 tables, and still two at 300.

So the entitlement that triples between the tiers is table coverage, and the entitlement that governs how much of your stack the lineage graph can actually reach stays fixed. A team paying $75,000 to monitor 300 tables has the same connector budget as a team paying $45,000 to monitor 100. If your warehouse, your BI tool and your orchestrator are three separate systems, two connectors is a real constraint on how far lineage travels, and it is the question to put to a sales engineer before the number of tables.

Neither the vendor comparison pages nor the aggregator sites mention this, because reading it requires opening the rate card rather than the feature matrix.

Bigeye is an AI Trust Platform now, and the meter did not change

Bigeye announced its AI Trust Platform on 4 June 2025 and the company now leads with it: governance policies that define how AI agents access data, observability over the quality and sensitivity of the data feeding AI systems, and enforcement that can alert on, block or steer agent activity. The data observability product is still there, positioned as the foundation underneath.

The interesting part for a buyer is that the repositioning has not reached the rate card. In September 2026 the Marketplace listing still sells Active Monitored Tables and lineage connectors, the same meters as before. That is reassuring rather than damning: you are buying the observability product you think you are buying, at a meter you can count. But if you are evaluating Bigeye for the agent governance story specifically, note that none of it appears in the published packaging, so its cost is a conversation rather than a rate.

It is also worth separating the two problems, because they are usually solved in different places. Knowing whether the data feeding a model is fresh and correct is a data observability question. Constraining what an autonomous agent is permitted to read, call and send is closer to runtime security for AI agents, and teams that need both rarely find one vendor doing both well yet.

Bigeye vs Monte Carlo: the head-to-head

Who should buy which

Buy Bigeye if you need to model the cost before you commit. It is the only vendor here whose published packaging lets a finance team build a defensible number without a sales cycle, and the per-table rate at the larger tier is competitive. It suits a team with a well-scoped warehouse that knows roughly how many tables genuinely matter, and it suits organizations where the AI governance roadmap is a live topic. Push hard on the two-connector limit and on what happens when a table stops being "active".

Buy Monte Carlo if breadth of automated coverage matters more than predictable packaging. It built the category, it is the incumbent you most often find already installed, and its anomaly detection covers a lot of ground with very little rule writing. Expect to negotiate rather than model. Go into the first call with your table count, your warehouse platforms and your incident history, because those are the inputs that will set your credit consumption.

Buy neither if what you actually needed was lineage. This is the most common expensive detour in this category, and it is worth naming plainly. A team gets burned by a schema change, wants to know which downstream tables, models and dashboards a column feeds, and ends up in a five or six figure observability evaluation that answers a different question. Anomaly detection tells you something broke. Lineage tells you what a change will break, before you ship it.

Where Datatrail fits, and where it does not

We build Datatrail, so weigh this accordingly. We are not trying to match Monte Carlo's breadth of automated anomaly detection at enterprise scale, and if that is genuinely your requirement you should buy Monte Carlo.

What we do is the lineage-first version of this job. Connect a read-only role to Snowflake, BigQuery, Redshift or Databricks and Datatrail parses query history and dbt artifacts into column-level lineage that stays current by itself, because it reads what actually ran rather than only what dbt declared. The same graph drives impact analysis: before you drop or rename a column, you see the downstream models, tables and dashboards that depend on it, by name. Freshness and schema-change monitoring sit on that graph, so an alert arrives already attached to its blast radius instead of pointing at one table.

Frequently asked questions

How much does Bigeye cost?

Bigeye's published US list prices are $45,000.00 for 12 months for the Starter Package, covering 100 Active Monitored Tables, and $75,000.00 for the Enterprise Starter Package, covering 300. Both were read from its AWS Marketplace listing on 9 September 2026 and both include two core lineage connectors and a browser extension. That works out to $450 and $250 per monitored table per year.

How much does Monte Carlo data observability cost?

Monte Carlo's published US list price is $50,000.00 for 12 months for one Monte Carlo Credit, read from its AWS Marketplace listing on 9 September 2026. The listing does not define what a Credit entitles you to. Monte Carlo's pricing page says cost per credit varies by tier, and its Start and Scale order forms print the rate at $0.18 and $0.28 per credit, with a Table Monitor consuming 1.75 credits a day. On those rates 100 monitored tables cost about $11,498 to $17,885 a year and 1,000 tables about $114,975 to $178,850. Treat $50,000 as a verifiable reference point for one Marketplace unit, not as the cost of a deployment.

Is Bigeye cheaper than Monte Carlo?

At list, Bigeye's entry package is $45,000 against Monte Carlo's $50,000, so it is about ten percent cheaper to start. Beyond that the comparison cannot be completed from public data, because Bigeye states its entitlement and Monte Carlo does not. Bigeye's 300-table package at $75,000 is 1.5 times Monte Carlo's single Credit, but nobody outside Monte Carlo can say how many tables a Credit covers.

What is a Monte Carlo credit?

A Credit is Monte Carlo's consumption unit. Its pricing page states that you buy credits and consume them at published Consumption Rates, with the cost per credit depending on your tier. The Consumption Rates are public in Monte Carlo's docs (Version 2.1: 1.75 credits per table per day in the first bracket, 1.0 per metric, 20 per Query Performance Monitor), and the per-credit price is printed on two public order forms, $0.18 on Start and $0.28 on Scale. Enterprise and Business Critical rates are not published. The AWS Marketplace listing prices one contract unit at $50,000.00 for 12 months without naming a tier or a credit count.

Does Bigeye do data lineage?

Yes, and it is central to how the product works: Bigeye uses lineage to connect an alert to what sits downstream of it, and its packaging explicitly meters lineage connectors. The detail worth probing is the two-connector limit that applies at both published tiers, and how deep the lineage goes. Ask to see column-level lineage on a query with a CTE and a window function, not on a simple select.

Do Bigeye and Monte Carlo publish pricing?

Not on their own websites, where both route you to a demo request. Both publish on AWS Marketplace, because a transactable listing has to carry a real dollar amount. That is where every figure on this page came from. It is worth checking Marketplace for any quote-only vendor before concluding that a price is private, and worth re-checking periodically, because rate cards change without announcement.

What are the alternatives to Bigeye and Monte Carlo?

The shortlist usually adds Sifflet at $48,000 a year and Acceldata, which lists Data Reliability at $5,000 and Spend Intelligence at $100,000, all published on AWS Marketplace. Datafold sits nearby at a flat $3,000 per developer per year. Anomalo and Ataccama both appear on Marketplace with $1.00 placeholder prices that are transactable but meaningless, so neither can be priced from public data. If the requirement leans toward catalog and governance instead, the comparison shifts to Collibra, Alation and Atlan.

For the wider field, including every vendor in this category that publishes a verifiable price anywhere, see our guide to data observability tools, the full Bigeye comparison, the Bigeye pricing breakdown and the Monte Carlo comparison. If Acceldata is the other name on your shortlist we put both rate cards side by side in Acceldata vs Monte Carlo, and there is more on how these listings are structured in data observability pricing.