Monte Carlo
Data observability extended to monitoring AI agents in production
Overview
Monte Carlo is a data and AI observability platform that monitors warehouses, lakes, BI and ETL tools for freshness, volume, schema and quality problems and traces incidents through lineage. Since 2026 it also monitors AI agents' context, behaviour, performance and output. It is sold in credits across four tiers, with prices on request.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 14 Oct 2026 and may be out of date or incomplete.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.
- Observability for agents, ML, data and performance
- Incident triage, root-cause analysis and lineage
- Self-guided onboarding, 24-hour support SLA
- SSO, SCIM, self-hosted storage, PII filtering, audit logging
- Data exports and webhooks
- Lakes and Databricks, MySQL, Postgres, SQL Server integrations
- Multi-workspace support
- Enterprise cost attribution (chargebacks)
- Oracle, SAP HANA, Teradata, Microsoft Fabric and ServiceNow integrations
- Everything in Enterprise
- Dedicated instance
- Disaster recovery rollover to a different region
Use cases
What you can produce with Monte Carlo
- Automated freshness, volume, schema and quality monitoring across warehouses, lakes, BI and ETL tools
- Incident triage and root-cause analysis with upstream and downstream lineage
- Agent observability that traces agent decisions and alerts on LLM or tool failures and timeouts
- LLM-as-judge evaluations of agent output for relevancy and prompt adherence
- An MCP server and open-source Agent Toolkit for Claude Code, VS Code and Cursor
- SSO, SCIM, self-hosted storage and audit logging on Scale and above
- A dedicated instance with cross-region disaster recovery on Business Critical
Monte Carlo remains one of the most complete data observability platforms, with automated monitors across the warehouse, lake, BI and orchestration stack and lineage-based root-cause analysis. Its 2026 move into agent observability is a logical extension rather than a bolt-on: the data feeding an agent is half of what makes it reliable, and Monte Carlo already watches that data.
There is no free or self-serve tier. All four tiers are bought in credits priced on request, and the Start tier is capped at 10 users and 1,000 monitors. Best for enterprises where bad data or a misbehaving agent has a measurable cost.
What we found
Monte Carlo started as a data observability company: software that watches pipelines, warehouses, lakes and BI tools for stale, missing or malformed data and traces each incident through lineage. In 2026 it repositioned itself as an agent trust platform, moved to the montecarlo.ai domain and now contracts as Monte Carlo AI, Inc., a Delaware corporation. The argument is that an AI agent can only be trusted if the data it retrieves is sound, so the same platform now monitors agents' context, performance, behaviour and output, with tracing, LLM-as-judge evaluations and alerts on model or tool failures, built on OpenTelemetry.
Funding has been quiet since May 2022, when IVP led a $135 million Series D at a reported $1.6 billion valuation, bringing the total to about $236 million; we found no later round. The company now claims more than 400 enterprise customers and 10 million monitored tables, and says G2 has ranked it the leader in data observability for 13 consecutive quarters. Those are Monte Carlo's own figures; G2 itself was unreadable on our research day, so no rating is quoted here.
Earlier versions of this page described a free tier with 1,000 monitors and a $25-a-month Pro plan. Neither appears on the pricing page. Every tier is bought in credits consumed at published rates, with the cost per credit quoted on request. Start allows up to 10 users, up to 1,000 monitors and 10,000 API calls a day. Scale adds SSO, SCIM, self-hosted storage and webhooks with unlimited users, Enterprise adds multiple workspaces and chargebacks, and Business Critical adds a dedicated instance with cross-region recovery. Contract values quoted elsewhere could not be traced to the company and have been removed.
For developers, an MCP server and an open-source Agent Toolkit of skills and Claude Code plugins bring Monte Carlo into Claude, VS Code and Cursor. It suits data teams with a mature warehouse stack that now run agents on top of it.
Compiled by RECATOOLS from public sources: vendor documentation and pricing, code repositories and published reporting.
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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