§ Pricing · Quality & testing

Monte Carlo pricing.

~$25k–50k / yr sales-led · estimated annual spend third-party spend data · Vendr

Monte Carlo does not publish pricing. Every tier is sales-led, on a usage based model. What is known: All tiers — Pay-as-you-go (up to 1,000 monitors, 10 users) and Pro/Enterprise are quoted; Vendr data suggests USD 25-50k/yr mid-market entry, more at enterprise scale. There is no free tier.

Sales-led

The numbers

What's verifiable.

Pricing model usage based

Charged per custom

Published ○ Contact sales required

Free tier ○ No

Free self-host ○ Not available

Sales-only tier All tiers — Pay-as-you-go (up to 1,000 monitors, 10 users) and Pro/Enterprise are quoted; Vendr data suggests USD 25-50k/yr mid-market entry, more at enterprise scale

The model

How usage based pricing works.

How it's charged

You pay for what runs — monitors, scans, or compute consumed. Starts cheap, scales with actual monitoring activity.

What to watch

Cost predictability. Usage meters make the first month cheap and the twelfth surprising; look for spend caps and alerts before committing.

Alternatives by price

Same cluster, ranked by entry cost.

dbt-expectations OSS · free Open-source dbt package adding 50+ Great Expectations-style assertions as native dbt tests that run in your own warehouse. pricing vs Monte Carlo

Elementary OSS · free The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project. pricing vs Monte Carlo

Great Expectations OSS · free Python-native data validation framework — the OSS standard, now in stewardship transition after the May 2026 acquisition. pricing vs Monte Carlo

Soda From $750 YAML-first data contracts and observability — SodaCL plus Soda Cloud, with anomaly detection and a self-hosted Kubernetes runner. pricing vs Monte Carlo

Datafold From $799 Pre-merge data diffing and column-level lineage — the tool that shifts data quality left into the pull request. pricing vs Monte Carlo

Metaplane Published, usage-based ML-powered, no-code data observability for the dbt and warehouse stack with automatic column-level lineage — now Metaplane by Datadog. pricing vs Monte Carlo

Acceldata Contact sales Enterprise data observability with ML data quality, reconciliation, and a built-in catalog — strong on hybrid and on-prem estates. pricing vs Monte Carlo

Anomalo Contact sales GUI-first ML anomaly detection at petabyte scale — pivoting in 2026 around agentic AI and unstructured-data monitoring. pricing vs Monte Carlo

Bigeye Contact sales Enterprise data observability with Autometrics ML thresholds — repositioning in 2026 as an AI Trust Platform with runtime governance. pricing vs Monte Carlo

Sifflet Contact sales EU-built full-stack data observability pairing ML-driven monitoring with an embedded catalog and field-level lineage. pricing vs Monte Carlo

Ranked by published entry price — sales-led tools sort last because their floor is unverifiable. For capability-ranked substitutes, see all Monte Carlo alternatives, scored.

Common questions

Asked about Monte Carlo pricing.

Does Monte Carlo publish pricing?

No. Monte Carlo is sales-led with no published rates. All tiers — Pay-as-you-go (up to 1,000 monitors, 10 users) and Pro/Enterprise are quoted; Vendr data suggests USD 25-50k/yr mid-market entry, more at enterprise scale.

Does Monte Carlo have a free tier?

No — there is no free tier.

Can you run Monte Carlo for free by self-hosting?

No. Monte Carlo is proprietary software with no free self-host path.

What's the cheapest alternative to Monte Carlo?

Among the 10 same-cluster alternatives indexed here, dbt-expectations has the lowest entry cost (oss · free). Ranking is by published entry price; sales-led tools may quote anywhere.

Where these numbers come from.

Every figure above traces to public sources — the vendor's own pricing page where one exists, marketplace listings and third-party spend data where it doesn't. Verified 2026·04·25. The full capability spec lives on the Monte Carlo tool page.

Pricing changes faster than any index. Notice something stale? Send a correction.

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