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Monte Carlo

Q: How much does Monte Carlo cost?

Monte Carlo uses usage based, credit consumption pricing with published rates on its order form pages: twenty five cents per credit on the Scale tier and forty five cents per credit on the Enterprise tier, where monitors consume credits at documented consumption rates. A Start tier covers one team with up to ten users, pay per monitor up to one thousand monitors, and ten thousand API calls per day; Scale adds unlimited users and fifty thousand API calls per day. Buyers can pay as they go or commit to usage for discounts, and all tiers include agent, ML, and data observability. Third party benchmark data puts typical annual contracts between twenty five and fifty thousand dollars for mid size deployments and one hundred twenty to two hundred fifty thousand dollars or more for large enterprise deployments.

Q: Who is Monte Carlo for?

Enterprise data and AI teams, Data engineering and platform teams, ML and analytics engineering teams, Enterprises scaling agents into production

Also known as: Monte Carlo Data, Agent Observability, Observability Agents

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Agent infrastructureindependentVerified 2026-07-07

Agent trust platform that unifies data and agent observability, so enterprise teams can monitor, troubleshoot, and improve production AI systems from the pipelines feeding agents to the outputs they produce.

Monte Carlo is a San Francisco company, founded in 2019, that created the data observability category and now positions itself as the agent trust platform, unifying data and agent observability in one system. The core platform monitors data warehouses, lakes, ETL, and business intelligence for freshness, volume, schema, and quality issues, using machine learning to learn each environment's normal behavior and alert the right people before problems propagate, with automatic field level lineage tying issues to their impact. It is rated number one in data observability on G2 and recognized by Gartner Peer Insights, GigaOm, and ISG, with enterprise customers including NASDAQ, Honeywell, Roche, Fox, JetBlue, and Pilot Flying J across more than five hundred deployments.

Agent Observability, generally available since early 2026, extends the platform across the AI stack. Built on an OpenTelemetry framework, it traces every agent run with telemetry across prompts, completions, user queries, latency, and errors from any model or orchestrator, then applies customizable LLM as judge and deterministic evaluations plus smart sampling and anomaly detection to catch low quality responses, drift, and silent regressions before they reach end users. Distinctively, telemetry is stored in the customer's own warehouse or lakehouse rather than a third party platform, supporting security, compliance, and auditability. The company also ships observability agents, a Monitoring Agent that recommends and deploys data quality monitors with a click, and a Troubleshooting Agent whose network of hundreds of subagents investigates root cause and returns verified explanations and next steps, with the company citing more than eighty percent faster incident resolution. These agents are read only and customer data is never stored by Monte Carlo nor used to train models.

Monte Carlo sits at the reliability and trust layer rather than building task agents, covering what it calls the full trust stack, context quality, performance, behavior, and outputs. It integrates with Snowflake, Databricks, BigQuery, Fivetran, dbt, Looker, Tableau, and Airflow, added native Salesforce CRM and Data Cloud monitoring, and routes alerts into Slack, Teams, email, ServiceNow, and Jira. It is a strong fit for enterprises scaling from dozens to hundreds of agents that want one trust loop across data and AI, and a weaker fit for teams wanting a lightweight point tool or a testing framework, which reviewers note it is not.

Vendor details

Canonical URL

https://montecarlo.ai

Subcategory

Data and AI observability

Funding status

Independent, headquartered in San Francisco, founded in 2019, and at Series D per Crunchbase. Monte Carlo created the data observability category and was named 2025 Databricks Data Governance Partner of the Year. It reports more than five hundred deployments across industries including pharma, financial services, retail, and CPG, with customers such as NASDAQ, Honeywell, Roche, Fox, JetBlue, Axios, and Pilot Flying J, and is available through the AWS Marketplace.

Company status

independent

Use cases & customers

Primary use cases

Data observability and quality monitoringAgent and AI observabilityIncident triage and automated root cause analysisData and agent lineage

Target customers

Enterprise data and AI teamsData engineering and platform teamsML and analytics engineering teamsEnterprises scaling agents into production

Deployment options

CloudCustomer hosted telemetry storage

Integrations

Monte Carlo integrates across the data and AI stack: warehouses and lakehouses including Snowflake, Databricks, and BigQuery, ETL and transformation tools including Fivetran and dbt, business intelligence tools including Looker and Tableau, orchestration including Airflow, and native Salesforce CRM and Data Cloud monitoring. Agent telemetry is consolidated from any model or orchestrator over an OpenTelemetry framework and stored in the customer's own warehouse or lakehouse. Alerts route to Slack, Teams, email, ServiceNow, and Jira, and monitors as code integrate with CI/CD workflows such as GitHub Actions.

In practice

A production agent degrades because an upstream table went stale. Monte Carlo detects the anomaly, ties it through lineage to the affected agent, and alerts the team in Slack before customers notice.

An enterprise scales from a handful of agents to hundreds and can no longer eyeball quality. Monte Carlo traces every run and applies LLM as judge evaluations with smart sampling to flag drift and low quality responses at scale.

An incident fires overnight. Monte Carlo's Troubleshooting Agent investigates with its network of subagents and hands the on call engineer a verified root cause explanation and next steps instead of a bare alert.

Sources & related URLs

Related / legacy domains

https://www.montecarlodata.com

Research sources

https://montecarlo.ai/ https://montecarlo.ai/platform/agent-observability/ https://www.montecarlodata.com/pricing/scale-order-form/ https://www.montecarlodata.com/pricing/enterprise-tier-order-form/ https://www.businesswire.com/news/home/20250909271741/en/Monte-Carlo-Launches-Agent-Observability-to-Help-Teams-Build-Reliable-AI https://www.vendr.com/marketplace/monte-carlo

Capability coverage

8.0 / 14 capabilities · 57%

Integrations & Tool CallingIntegrates across warehouses, ETL, BI, and orchestration including Snowflake, Databricks, BigQuery, Fivetran, dbt, Looker, Tableau, and Airflow, plus native Salesforce CRM and Data Cloud monitoring, montecarlo.ai and Vendr coverage retrieved 2026-07-07	Full
Workflow OrchestrationMonte Carlo observes and troubleshoots rather than orchestrating customer workflows, and its observability agents are explicitly read only and never act on customer systems, businesswire launch coverage retrieved 2026-07-07	Unable to verify
Knowledge Grounding & RAGThe platform monitors the context and data feeding agents, including unstructured documents, but does not itself provide knowledge grounding or retrieval, montecarlo.ai and siliconangle coverage retrieved 2026-07-07	Unable to verify
Human Oversight & GuardrailsSupports incident severity assignment, owners, and triage, positions itself across the spectrum from human guided oversight to autonomous operation, and its own agents are read only, G2 profile and businesswire coverage retrieved 2026-07-07	Partial
Security, Identity & GovernanceTelemetry is stored in the customer's own warehouse or lakehouse for security, compliance, and auditability, customer data is never stored by Monte Carlo nor used to train models, and access to samples is role and permission gated, montecarlo.ai agent observability page and businesswire retrieved 2026-07-07	Full
Observability & AuditabilityEnd to end data and AI observability is the core product, tracing every agent run across prompts, completions, latency, and errors and monitoring freshness, volume, schema, and quality with field level lineage, montecarlo.ai retrieved 2026-07-07	Full
Memory & State PersistenceThe AWS Marketplace listing describes a context layer that continuously improves through memory, feedback, and traces from every agent interaction, though not a general agent memory store, AWS Marketplace listing retrieved 2026-07-07	Partial
Deployment & Data ResidencyDelivered as a cloud service, with the Enterprise tier supporting customer hosted object storage and telemetry residing in the customer's own warehouse or lakehouse, enterprise order form and agent observability page retrieved 2026-07-07	Partial
Prebuilt Agents, Templates & PacksShips a fleet of observability agents including the Monitoring and Troubleshooting Agents, out of the box quality checks, and low code evaluation monitors deployable with a click, though not a marketplace of task agents, observability agents page retrieved 2026-07-07	Partial
Triggers & Channel CoverageReal time anomaly detection raises incidents and routes alerts across Slack, Teams, email, ServiceNow, and Jira, with monitors as code integrating into CI/CD, AWS Marketplace reviews and G2 retrieved 2026-07-07	Full
Model Flexibility & RoutingConsolidates telemetry from any model or orchestrator over an OpenTelemetry framework with customizable LLM as judge evaluations, model agnostic observation rather than routing, agent observability launch blog retrieved 2026-07-07	Partial
APIs, SDKs & MCP ExtensibilityProvides an API with documented daily call quotas per tier, easy to deploy OpenTelemetry instrumentation, and monitors as code, but no public SDK library or MCP surface is documented, order form pages and G2 retrieved 2026-07-07	Partial
Testing, Debugging & OptimizationCustomizable LLM as judge and deterministic evaluations with custom prompts detect low quality outputs and drift, and the Troubleshooting Agent automates root cause investigation with verified explanations, siliconangle and businesswire coverage retrieved 2026-07-07	Full
Browser & Computer UseNo browser or computer use capability is described, montecarlo.ai retrieved 2026-07-07	Unable to verify

Recent platform changes

No recent material changes tracked yet.

View all changes for Monte Carlo →

Pricing

Usage based credits, twenty five cents per credit on the Scale tier and forty five cents per credit on Enterprise, with total cost driven by how many monitors run and what they consume

credits consumed by monitors, with tiers gating users, monitor counts, and daily API calls

Public — partialHigh variable cost

Included quota

Start tier covers up to ten users, pay per monitor up to one thousand monitors, and ten thousand API calls per day; Scale adds unlimited users and fifty thousand API calls per day with one BI and one orchestration integration included

What is public

Credit rates are published on official order form pages, twenty five cents per credit on Scale and forty five cents on Enterprise, along with tier quotas for users, monitors, and API calls, and all tiers include agent, ML, and data observability. What is not public is the consumption rate per monitor category in dollar terms upfront, so total cost still requires scoping.

Billing mechanics

Buy credits and consume them as monitors run, at consumption rates documented by Monte Carlo. Pay as you go for flexibility or commit to usage for discounts and predictability. Monthly billing with credit card available on order forms, and the platform is purchasable through the AWS Marketplace. Enterprise supports full cloud or customer hosted object storage deployment.

Cost watchouts

Adding data sources and integrations raises cost, and sophisticated monitors consume credits faster than basic freshness checks, so estates with many custom monitors can outgrow initial estimates.

Variable cost rationale

Pricing is consumption based, with monitors drawing down credits at per category rates, so cost grows with tables monitored, monitor sophistication, and data sources connected. Third party benchmarks show wide contract ranges, from twenty five to fifty thousand dollars annually for mid size deployments to well over two hundred thousand for large estates, making scope the dominant cost factor.

Additional watchouts

Cost scales with tables, sources, and monitor categories, so clarify whether pricing counts active tables or all tables, and which monitor types consume credits fastest. Reviewers also note the distinction between Scale and Enterprise tiers is not always clear, so pin down what the forty five cent tier adds beyond customer hosted storage.

Overage / add-ons

Monitors consume credits at documented consumption rates published in Monte Carlo's docs, so cost scales with monitor category and volume rather than a hard overage fee

Sales call required

Mixed (some tiers require a call)

Free / trial

No public free tier; demo led with pay as you go available

Commercial notes

Third party benchmark data reports typical annual contracts of twenty five to fifty thousand dollars for deployments monitoring thirty to one hundred tables across two to three sources, and one hundred twenty to two hundred fifty thousand dollars or more for enterprise estates over three hundred tables, with ten to thirty percent discounts common on annual and multi year commitments.

Key ambiguities

Whether the published 2025 credit rates still apply and how consumption rates translate a monitor count into monthly credits.

Missing data

Per category consumption rates in dollars and the Start tier rate were not retrievable; the credit rates come from 2025 order form pages and should be confirmed current.

Verified 2026-07-07

Official pricing page Docs

Data confidence: high