Monte Carlo
Data observability that's expanding into AI agent monitoring
Overview
Monte Carlo watches data pipelines across warehouses, lakes, BI and orchestration tools for freshness, volume, schema and quality anomalies, and in 2026 extended the same monitoring to AI agents. G2's top-rated data observability platform for seven straight quarters.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- 1 user, 1,000 monitors
- Data, ML & agent observability
- 10,000 API calls/day
- Self-guided onboarding
- Up to 10 users
- SSO & SCIM
- Self-hosted storage
- PII filtering
- Advanced security & audit logging
- Unlimited scale
- Dedicated support
Use cases
What you can produce with Monte Carlo
- Automated freshness, volume, schema & quality monitors
- Data lineage tracking
- LLM-generated incident summaries
- AI agent observability (behavior, performance, output monitoring)
- GitHub integration for breaking-change detection
- Free tier: 1,000 monitors, 1 user
- SSO/SCIM and self-hosted storage on Pro
ASEAN Perspective
Monte Carlo in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Seven consecutive quarters as G2's #1 data observability platform isn't a fluke — Monte Carlo built this category and still covers warehouse, lake, BI and orchestration data more broadly than most competitors, backed by ML-generated monitors that spare teams from writing tests by hand. The 2026 push into agent observability, tracking AI agent behavior and outputs alongside the data feeding them, is a sensible extension rather than a bolt-on.
What changed for the better: there's now a real free tier (1,000 monitors, one user) and a $25/month Pro plan, so smaller teams can actually try it without a sales call — new for a company that used to be enterprise-quote-only. Full deployments still land in the $25K–$50K+ ACV range once you're covering real table volume, and reviewers flag noisy alerting that needs deliberate tuning. Best fit: teams with a mature modern data stack where bad data has a measurable downstream cost.
What people say
Monte Carlo has raised $236M across four rounds, including a $135M Series D, from investors that read like a data-infra who's who. Headcount sat at 549 as of May 2026. The company has been G2's #1-ranked data observability platform for seven consecutive quarters and picked up a spot in G2's 2026 Best Software Awards, in the top 50 for IT infrastructure and management.
Pricing changed shape in 2026: alongside enterprise contracts (typically $25,000–$50,000 a year for 30–100 tables across a couple of data sources, sometimes $50K–$200K+ for the largest deployments), Monte Carlo now publishes a Free tier — one user, 1,000 monitors, 10,000 API calls a day — and a $25/month Pro tier with SSO, SCIM and self-hosted storage. That's a real shift from the quote-only model the company used for most of its history.
G2 reviewers put Monte Carlo at 4.3/5 across roughly 500 reviews. The praise is consistent: automated anomaly detection catches problems before they reach dashboards, and the UI is described as intuitive for what's a genuinely complex monitoring surface. The most common complaint is alert noise — several reviewers say the system needs deliberate tuning to stop over-alerting, the same complaint leveled at most ML-based anomaly tools in this category. Monte Carlo also holds the #1 spot in G2's Enterprise Relationship Index for the category, which tracks renewal and support satisfaction rather than raw feature checklists — a signal that the enterprise deployments, at least, are sticking.
Feature-wise, Monte Carlo covers freshness, volume, schema and quality monitoring, lineage tracking, and now LLM-generated incident summaries that explain what broke and why. The newer agent observability layer monitors AI agent behavior, performance and output quality in production, positioning the company for teams running agents on top of the same data it already watches. A GitHub integration flags breaking schema changes before they ship, and the official GitHub org ships mc-agent-toolkit (89 stars), packaging Monte Carlo skills for Claude Code, Cursor and other coding agents.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Monte Carlo's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Monte Carlo unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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