Monte Carlo

Monte Carlo is a data and AI observability platform that detects, triages and resolves broken data and misbehaving AI agents before they reach production users. It is aimed at data engineering and AI platform teams at large enterprises whose customer-facing agents and dashboards sit on top of sprawling cloud warehouse estates.

Monte Carlo is a San Francisco company that effectively created the data observability category when it launched its platform in December 2020, and has since extended it to cover AI agents, describing itself as an 'agent trust platform' unifying data and agent monitoring. The original product applies machine-learning anomaly detection and end-to-end lineage across cloud warehouses and pipelines — with confirmed coverage spanning Google BigQuery, AWS Athena, Apache Kafka streams and Pinecone vector indexes — to catch freshness, volume, schema and distribution failures before downstream consumers see them. On 9 September 2025 it launched Agent Observability, and on 12 March 2026 expanded it substantially around four pillars. Context validates the data and signals an agent retrieves, including LLM-based evaluation of AI-generated fields against warehouse data. Performance tracks latency, token usage, duration, error rates and trace-level cost across a whole workflow. Behavior uses Agent Trajectory Monitors to validate step sequencing, frequency and tool usage and to detect unintended loops or skipped tasks. Outputs combines pre-production evaluation against golden datasets with continuous production monitoring using LLM-as-judge or rule-based checks. The same release added a Monte Carlo-hosted OpenTelemetry deployment for AWS so teams need not run their own collectors, alongside packaged Monitoring, Troubleshooting and Operations agents and an MCP toolkit. The company reports more than 400 enterprise customers including Nasdaq, PepsiCo, Cisco, Comcast, Disney, Target, T. Rowe Price and Salesforce, and raised a $135 million Series D led by IVP in May 2022 at a $1.6 billion valuation, taking total funding to roughly $196 million. Pricing is a credits-based consumption model across four tiers and is not published.

Category

Data & Analytics

Pricing

Contact for pricing, Usage-based, Subscription

Target Market

Data Engineering Leaders, CDOs, AI Platform Teams, Analytics Engineers, CTOs

Deployment

Cloud-only, Multi-cloud

Headquarters

San Francisco, United States

Customers

400+ enterprises

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  Agent Trajectory Monitors

  Validate an agent's step sequencing, frequency and tool usage, flagging unintended loops or skipped tasks inside a live workflow.

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  Context validation

  Checks the data and signals an agent retrieves, evaluating AI-generated fields against warehouse data with custom prompt-based assessments.

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  Agent Metric Monitors

  Track latency, token usage, duration and error rates with trace-level cost monitoring across an entire agent workflow.

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  Output evaluation

  Pre-production tests against golden datasets plus continuous production checks using LLM-as-judge or deterministic rule-based monitors.

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  End-to-end lineage

  Maps a bad output back through retrieval to the upstream table, so remediation targets the cause instead of the visible symptom.

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  ML-driven data anomaly detection

  Automatically learns freshness, volume, schema and distribution baselines across the warehouse rather than requiring hand-written tests.

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  Hosted OpenTelemetry for AWS

  Monte Carlo runs the collector, so teams can onboard agent telemetry without operating their own OpenTelemetry infrastructure.

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  Platform agents and MCP toolkit

  Packaged Monitoring, Troubleshooting and Operations agents plus an MCP toolkit let teams query and act on observability data conversationally.

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  Preventing a broken pipeline reaching an executive dashboard

  Anomaly detection catches a freshness or volume break upstream and alerts the owning team before consumers ever see stale numbers.

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  Debugging a wrong agent answer

  Lineage traces the incorrect output back through the retrieval step to the specific upstream table that changed, cutting root-cause time.

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  Controlling agent spend

  Trace-level token and cost monitoring shows which workflow steps drive spend, so teams can cap or refactor the expensive path.

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  Gating an agent release

  Pre-production evaluation against golden datasets decides whether a new agent version is promoted, rather than shipping it and watching.

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  Catching runaway agent behaviour

  Trajectory monitors detect an agent looping or skipping required steps in production, which latency and error-rate alerts alone would never surface.

Monte Carlo publishes four tiers — Start, Scale, Enterprise and Business Critical — but no dollar figures on any of them; every tier reads 'Request pricing'. Billing is a credits model where customers buy credits and consume them against published consumption rates, with cost per credit varying by tier, so spend scales with monitored assets and API volume rather than seats (only Start caps users, at 10). There is no free tier and no self-serve trial, and independent commentary notes the platform reads as expensive to smaller organisations relative to dbt tests or open-source monitoring.

This page was written from 6 sources, 4 on domains other than montecarlo.ai.

1. 1.montecarlo.ai — montecarlo.aivendor
2. 2.montecarlo.ai — pricingvendor
3. 3.apmdigest.com — monte carlo introduces new agent observability capabilities
4. 4.techtarget.com — Monte Carlos Agent Observability targets reliability of AI
5. 5.news.crunchbase.com — monte carlo joins unicorn list 135m ivp
6. 6.techtarget.com — Monte Carlo unveils data observability for vector databases