Monte Carlo

What it does

Monte Carlo is an observability platform for AI agents and the data they depend on. It ingests agent traces via OpenTelemetry, covering prompts, completions, latency, errors, and tool calls, and monitors the tables, pipelines, and models behind them. The result is alerting, evaluations, step-by-step tracing, and, through Bayes, grouped incident reports with a suggested fix.

Teams use it to watch agents across four layers: context, performance, behavior, and output. Because agent traces and data monitoring share one graph, a team can tell whether a wrong answer came from the agent or from its upstream data. The MCP Server and Agent Toolkit let agents and IDEs such as Claude, VS Code, and Cursor use Monte Carlo without leaving the development environment.

It suits enterprise data and AI teams running agents or data pipelines in production. Access is through a demo request or pricing inquiry, and plans are tiered and credit-based. Telemetry can be stored in the customer's own warehouse or lakehouse. Confirm costs, integrations, and tier-specific features with the vendor before committing.

What it can do

• Trace agent runs with telemetry across prompts, completions, latency, and errors
• Run customizable LLM-as-judge or deterministic evaluations on agent output
• Alert on LLM or tool failures, timeouts, and performance degradations
• Monitor data tables and pipelines that agents depend on, and trace data-caused issues
• Group related alerts into single incident reports with suggested fixes (Bayes)
• Access observability from IDEs through the MCP Server and Agent Toolkit

Works with

• Langchain
• Snowflake Intelligence
• Databricks Genie
• Databricks
• Snowflake
• Oracle
• SAP Hana
• Teradata
• Microsoft Fabric
• ServiceNow

Before you choose

• Pricing is tiered and credit-based; the page does not publish dollar amounts, so confirm costs with sales.
• Advanced security features (SSO, self-hosted storage, PII filtering) and additional integrations are reserved for higher tiers.
• Monte Carlo does not store customer data or train models on it, and humans remain in the loop for changes to data or systems.

Listed under

• Coding
• DevOps and observability
• Agent monitoring
• Agent observability
• AI engineers
• AI observability
• Data engineering
• Data governance

Get started

Start by requesting a demo or pricing through the site; the docs are linked separately.

1. Request a demo or pricing through the Monte Carlo site to choose a tier.
2. Connect your data warehouse and instrument agents with OpenTelemetry traces.
3. Install the MCP Server or Agent Toolkit in your IDE (Claude, VS Code, Cursor) to use Monte Carlo from your development environment.

Credits are consumed based on consumption rates; cost per credit depends on tier. Request pricing to confirm amounts. No free trial or free tier is confirmed on the page.

We couldn’t find published prices for Monte Carlo. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 30 Oct 2026. Each source supports part of the overview; availability and prices can change.

• AI Agent Observability: Tracing, Evals & Monitoring | Monte Carlo ↗montecarlo.ai
• MCP Observability Platform | 2x Your Data & AI Velocity ↗montecarlo.ai
• Monte Carlo Pricing | Agent & Data Observability Plans ↗montecarlo.ai
• Monte Carlo | The Agent Trust Platform For Data + AI Observability ↗montecarlo.ai
• Monte Carlo | The Agent Trust Platform For Data + AI Observability ↗montecarlo.ai

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montecarlo.ai: product, pricing and policy pages

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Last checked 30 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.