Monte Carlo vs Anomalo vs Bigeye Compared on Price, Detection and Lineage
The three list prices, the meters behind them and what each platform actually catches, side by side. If the job turns out to be knowing what a change will break, connect a warehouse here and see your own lineage first.
Read-only connection. DataTrail never moves or mutates your data.
Monte Carlo, Anomalo and Bigeye are all enterprise data observability platforms, and only two of them print a real list price. On AWS Marketplace, Monte Carlo sells a $50,000 credit package for 12 months and Bigeye sells $45,000 for 100 monitored tables or $75,000 for 300. Anomalo's listing shows $1.00, a placeholder; buyers report paying $58,000 to $164,688 a year. Pick Monte Carlo for late and broken loads across a large estate, Anomalo for data that arrives on time but is wrong, and Bigeye for monitoring sold per table tier.
Three platforms, three different first questions
The fastest way to shortlist is to name the incident that keeps reaching your dashboards. Each of these vendors was designed around a different one, and their pricing models follow from that design.
Freshness, volume and schema monitoring across thousands of tables, cheap per table at scale, with lineage into BI tools to show who an incident hits. Sold in credits that each monitor draws daily.
Unsupervised models that learn the values in each table and flag distribution shifts, odd values and broken relationships without a rule. Also checks documents and text. Quote only, runs as SaaS or in your VPC.
Monitoring bought per table tier, with column-level lineage through a fixed number of connectors. Repositioned as an AI Trust Platform in June 2025, with observability as the foundation.
Monte Carlo vs Anomalo vs Bigeye pricing
None of the three prints a dollar figure on its own website. All three sell through AWS Marketplace, where a transactable listing has to carry an amount, so that is where the comparable numbers live. The listings are rendered in the browser, which is why most comparison pages quote no figures at all.
| Monte Carlo | Anomalo | Bigeye | |
|---|---|---|---|
| Public list price | $50,000.00 for 12 months, one "Monte Carlo Credit" package (AWS Marketplace) | $1.00 for 12 months per "unit", a placeholder (AWS Marketplace) | $45,000.00 for 100 tables, $75,000.00 for 300 tables, 12 months (AWS Marketplace) |
| What the meter counts | Credits, drawn daily by each monitor | Not published | Active monitored tables |
| Published unit rate | $0.18 per credit on Start, $0.28 on Scale (public order forms) | None | $450 per table a year at 100, $250 at 300 (our division) |
| Price on its own website | Four tiers (Start, Scale, Enterprise, Business Critical), no dollar figures | No pricing page, anomalo.com/pricing returns 404 | No pricing page, bigeye.com/pricing returns 404 |
| Overage | "Additional Usage Credit" line on the listing | Not published | None automatic; you move up a package |
| What buyers report paying | See Monte Carlo pricing | $58,000 to $164,688 a year, median $115,000 (Vendr) | See Bigeye pricing |
| Multi-year discount at list | 12-month term only | None: $1, $2, $3 for 12, 24, 36 months | 12-month term only |
| Free trial on the listing | None | None | None |
AWS Marketplace listings prodview-hikicsfohm3gg (Monte Carlo), prodview-sodj3n3wjs67s (Anomalo) and prodview-w4yis4dzpm4u2 (Bigeye), US list. Monte Carlo credit rates from its public order forms; Anomalo range from Vendr, read 1 October 2026. All three listings are 12-month SaaS contracts with no free trial, and Monte Carlo and Bigeye state no refunds.
Three listings, three different kinds of number
Bigeye sells something you can count: active monitored tables, 100 or 300 of them. Monte Carlo sells a credit and never says on the listing how many credits $50,000 buys; you have to go to its order forms and its consumption rate card to work that out. Anomalo sells a unit called "units", described as "units", for one dollar.
That is not a quality ranking. It tells you how each sales process will run. With Bigeye you can check a quote against a published tier. With Monte Carlo you can build an estimate from your table count before the first call. With Anomalo the first real number arrives in a private offer, so ask for it in writing, tied to scope, before the proof of concept starts.
What each costs at 100, 300 and 1,000 monitored tables
Table count is the number your procurement team will ask for first, so here is each vendor's list price at three estate sizes. Monte Carlo's figures cover Table Monitors only, at 1.75 credits a day per table; metric monitors, custom rules and agent runs draw more credits, so treat them as a floor.
| Monitored tables | Monte Carlo, per year | Anomalo, per year | Bigeye, per year |
|---|---|---|---|
| 100 tables | $11,498 Start, $17,885 Scale | Quote only | $45,000 |
| 300 tables | $34,493 Start, $53,655 Scale | Quote only | $75,000 |
| 1,000 tables | $114,975 Start, $178,850 Scale | Quote only | Quote only, above the published tiers |
Monte Carlo: tables x 1.75 credits x 365 days x $0.18 (Start) or $0.28 (Scale). Its AWS listing sells a $50,000 package, so a small estate will usually still sign at that floor. Bigeye: the two published packages. DataTrail, for comparison, is a flat $708 to $5,748 a year at any table count.
Tripling coverage from 100 to 300 tables costs about 1.67 times as much, so the rate per table falls from $450 to $250. At 150 tables you are stuck between the tiers, so ask what sits in between.
Both packages include two "Lineage Plus" connectors. At 300 tables you still have two, so a warehouse, a BI tool and an orchestrator already exceed what lineage can reach without an upgrade.
The listing charges $1, $2 and $3 for 12, 24 and 36 months, exactly linear. Any multi-year saving at any of these vendors is negotiated, not listed.
Monte Carlo vs Anomalo vs Bigeye features, detection and lineage
All three connect to Snowflake, BigQuery, Databricks and Redshift, so warehouse support will not separate them. What separates them is what they look at and how far their lineage reaches.
| Monte Carlo | Anomalo | Bigeye | |
|---|---|---|---|
| How it finds problems | Metadata signals first: freshness, volume and schema on every table, with metric monitors and custom SQL for values | Unsupervised machine learning on the values inside each table, plus rules and metric checks | Metadata and metric monitors with learned thresholds, chosen per table |
| Catches a late or missing load | Yes, its core strength | Yes, through table observability checks | Yes |
| Catches data that arrived on time but is wrong | Through metric monitors and SQL rules you set up | Its core strength, without writing a rule | Through metric monitors you choose |
| Column-level lineage | Yes, "Field Lineage", warehouse into BI, on every tier | Table-level lineage view; no column-level claim found | Yes, "Lineage Plus", with two such connectors in each published package |
| Unstructured data | Not its focus | Yes, documents and text for quality and PII | Not its focus |
| Where it runs | SaaS, SaaS with a customer-hosted data store, or a customer-hosted agent and data store | SaaS or fully in your VPC, plus a Snowflake Native App | SaaS, with an optional self-hosted agent that only connects outbound |
| Positioning in 2026 | Data and AI observability: Observability Agents (April 2025), Agent Observability (September 2025) | AI data quality, organized as nine task agents with the AIDA assistant | AI Trust Platform since June 2025, observability underneath |
Where each one honestly wins
Monte Carlo wins on breadth. It watches freshness, volume and schema across a very large estate, its marginal cost per table falls past 1,000 tables, and lineage on every tier turns an alert into a list of affected dashboards. If a data engineering team owns reliability for thousands of tables, it is the strongest of the three.
Anomalo wins inside the table. A currency column that started arriving in cents, a zero in a field that was never zero, a slow drift that skews a model: these pass every freshness and volume check. Anomalo is built to catch them without anyone writing the rule first, and it also checks unstructured content.
Bigeye wins on a countable contract. It is the only one of the three whose list price maps to a number you can verify on your side, monitored tables, and its per-table rate improves sharply at the second tier.
Which one fits your team
Monte Carlo. Model the bill from your table count with the published credit rates, not from the $50,000 listing. Detail on Monte Carlo pricing.
Anomalo. Get the quote in writing against a stated scope before the proof of concept. What drives it is on Anomalo pricing.
Bigeye, sized to the 100 or 300 tables that matter, with the connector limit in mind. The tier math is on Bigeye pricing.
None of the three is the cheapest fix. That is a lineage problem: see what reads a column before you change it with impact analysis.
Weighing just two of them? We have head-to-head pages for Anomalo vs Monte Carlo and Bigeye vs Monte Carlo, and the wider field, including Sifflet, Metaplane and Acceldata, is on our data observability tools comparison.
When you do not need any of the three
A lot of teams arrive at this comparison after a renamed column emptied a revenue dashboard. That incident is not an anomaly problem. It is a question of who reads the column, and it is answered by lineage. DataTrail is built for that job and nothing broader, and it is priced accordingly.
Snowflake, BigQuery, Redshift, Databricks or Postgres, with a read-only role. No agent to install and nothing written back.
Query history and your dbt project become column-level lineage from source tables to models and dashboards.
A late table or a renamed column alerts in Slack or email with the affected models and dashboards listed.
Open any column and see everything downstream before the pull request merges, at a flat price per workspace.
What DataTrail does not do: it does not run unsupervised models over your row values the way Anomalo does, and it is not built for monitoring tens of thousands of tables the way Monte Carlo is. If those are your problems, buy one of them. If change impact is the problem, plans are $59, $179 and $479 a month on the pricing page, or try the one-time demo first.
Monte Carlo, Anomalo and Bigeye questions buyers ask
Which is cheapest, Monte Carlo, Anomalo or Bigeye?
At published list rates, Monte Carlo is the cheapest for small estates: its order-form credit rates put 100 monitored tables at about $11,498 to $17,885 a year, against Bigeye's $45,000 package for 100 tables. Anomalo publishes no rate; buyers report paying $58,000 to $164,688 a year on Vendr. Monte Carlo's AWS listing still sells a $50,000 contract unit, so expect that as the practical floor.
What is the difference between Monte Carlo, Anomalo and Bigeye?
Monte Carlo is pipeline-first: it watches freshness, volume and schema across very large estates and uses lineage to show the blast radius of an alert. Anomalo is content-first: it uses unsupervised models on the values inside tables, and on unstructured documents, to catch data that arrived on time but is wrong. Bigeye sits between them and now sells itself as an AI Trust Platform.
Based on reviews, is Monte Carlo or Anomalo better?
Reviews will not settle it, because the two are built for different failures. Monte Carlo's own pricing page claims 13 straight quarters as a G2 category leader; Anomalo was named a Strong Performer in Gartner Peer Insights for augmented data quality in December 2025. Read reviews from teams with your stack and table count, then run both on the same tables.
Does Anomalo publish pricing?
No. Anomalo has no pricing page, and its AWS Marketplace listing prices one dimension named "units" at $1.00 for 12 months, which is a placeholder for a private offer, not a price. Buyer-reported contracts on Vendr run from $58,000 to $164,688 a year with a median of $115,000. Ask for a written quote tied to a table count before the proof of concept.
How much does Bigeye cost per table?
Bigeye's AWS Marketplace listing sells a Starter Package at $45,000 a year for 100 active monitored tables and an Enterprise Starter Package at $75,000 for 300. Divided out, that is $450 per table a year at the entry tier and $250 at the next one. Both packages include only two "Lineage Plus" connectors, so lineage reach does not grow with the table count.
Which of the three has the best data lineage?
Monte Carlo and Bigeye both build column-level lineage from the warehouse into BI tools, and Monte Carlo includes it on every tier. Anomalo's own docs describe its lineage as table-level and refreshed about once a day. If lineage and change impact are the main requirement rather than anomaly detection, a lineage-first tool may cover it without any of the three contracts.
Is there a cheaper alternative to Monte Carlo, Anomalo and Bigeye?
Yes, if your recurring problem is changes that break dashboards rather than subtle value drift. DataTrail connects read-only to Snowflake, BigQuery, Redshift, Databricks or Postgres, builds column-level lineage from query history and dbt, and alerts on freshness and schema changes with the downstream list attached, at a flat $59 to $479 a month with no per-table meter.
Where these numbers came from
The three AWS Marketplace prices were read from each listing's embedded rate card on 24 October 2026, the same method we use on every vendor page on this site. Monte Carlo's per-credit rates and its 1.75 credits per table a day come from its own public order forms and consumption rate card. The Anomalo range is buyer-reported on Vendr. The per-table and per-estate figures are our arithmetic on those published numbers, shown in the notes under each table.
List prices are where negotiation starts, and a large account will pay less. Feature rows come from each vendor's documentation and website; where a vendor's docs sit behind a login, we say what we could and could not verify rather than fill the gap.
Related pages
Per credit, per table and per year, from Monte Carlo's own order forms.
The $1.00 listing explained, and what buyers report paying.
$45,000 for 100 tables, $75,000 for 300, and the catches.
The full field, including Sifflet, Metaplane, Acceldata and Soda.
Competitors compared on fit, for teams replacing Monte Carlo.
Renamed and retyped columns, caught with the downstream list attached.
See what your changes break before you sign a five-figure contract
Connect your warehouse read-only and get column-level lineage, freshness and schema-change alerts with the downstream dashboards in every alert. Plans start at $59 a month billed yearly.