Agentic Index
Datafold vs Monte Carlo (2026)
Datafold last verified ·Monte Carlo last verified
Datafold and Monte Carlo both make data work reliable for teams running agents, from different sides. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Datafold automates data engineering. Its Migration Agent translates pipelines and validates them to full parity, and its Data Knowledge Graph and MCP tools give coding agents reliable context, with migrations priced as a fixed fee and parity guaranteed by contract. Monte Carlo is an agent trust platform that traces and evaluates AI agents alongside the data they depend on, runs a fleet of read only observability agents, keeps telemetry in the customer's own warehouse and exposes an MCP server, priced by usage credits at $0.25 each on Scale and $0.45 on Enterprise. Datafold lets admins register their own LLM provider and posts code reviews with suggested changes on pull requests, while Monte Carlo runs its own agents on AWS Bedrock with no model choice. Choose Datafold to migrate and change pipelines safely. Choose Monte Carlo to watch agents and data in production.
On the Agentic Index agent infrastructure ranking, Datafold clears the bar and Monte Carlo does not. Datafold documents all five production contract capabilities in full; Monte Carlo does not document model flexibility and routing in full. 33 of the 186 vendors in the lane clear it. See the agent infrastructure ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Datafold and Monte Carlo are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Datafold if
- You are migrating pipelines and want parity with the source proven.
- Coding agents need reliable context about your data.
- You want to choose the model the agent runs on.
Choose Monte Carlo if
- Agents in production should be traced and evaluated alongside the data they use.
- Telemetry must stay in your own warehouse.
- Usage based credits at public rates fit how you want to pay.
| Feature | D Datafold |
M Monte Carlo |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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DatafoldIntegrations & Tool Calling Datafold's AI Code Reviews post a summary comment and inline suggested changes on pull requests through its GitHub, GitLab and Azure DevOps apps, and the Migration Agent works across legacy and modern warehouses, ETL tools and the customer's code repository to translate pipelines. Sourcedocs.datafold.com/deployment-testing/ai-code-reviews.mdread 2026-09-28 |
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Monte CarloIntegrations & Tool Calling Agent traces arrive through OpenTelemetry from any model, framework or orchestrator. Monte Carlo reads the tables, pipelines and models that agents depend on, and its PR Agent reviews pull requests through a GitHub integration. Its agents never manipulate data or systems, so there are no connectors that let an agent take authenticated actions in outside systems. SourceMonte Carlo, montecarlo.ai/platform/agents, platform/agent-observability and mcp-and-toolkitread 2026-09-21 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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DatafoldWorkflow Orchestration The Migration Agent runs a fixed loop of translating, compiling, diffing and self correcting until parity, and code reviews pass through a review supervisor. The LLM provider settings refer to migration goals and per role model overrides, but the roles are not described, and customers cannot compose their own branching or flows. Sourcedocs.datafold.com/data-migration-automation/datafold-migration-agent.mdread 2026-09-28 |
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Monte CarloWorkflow Orchestration Agents that run elsewhere are what Monte Carlo observes, evaluates and troubleshoots. Customers cannot build workflows in it that sequence, branch or retry an agent's steps. SourceMonte Carlo, montecarlo.ai/platform/agentsread 2026-09-21 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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DatafoldTriggers & Channel Coverage AI Code Reviews run automatically whenever a pull request is created or updated, monitors run on schedules and alert on data diffs, metrics, tests and schema changes, and people reach the assistant by mentioning it in Slack or through MCP clients. Sourcedocs.datafold.com/deployment-testing/ai-code-reviews.mdread 2026-09-28 |
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Monte CarloTriggers & Channel Coverage Monitors and evaluations run continuously on production traces and data. The Triage Agent scores every new alert for likelihood and blast radius as it arrives, and the PR Agent assesses the production impact of each pull request. These agents start from incoming events, and no person has to start each run. SourceMonte Carlo, montecarlo.ai/platform/agents and platform/agent-observabilityread 2026-09-21 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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DatafoldKnowledge Grounding & RAG The Data Knowledge Graph serves the customer's lineage, business logic, usage, ontology and organizational knowledge to Datafold's agents and, over MCP, to the customer's own coding agents. Sourcedatafold.comread 2026-09-28 |
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Monte CarloKnowledge Grounding & RAG The context an agent retrieves is monitored for accuracy, freshness and completeness, but Monte Carlo does not provide a retrieval structure over the customer's knowledge that grounds an agent's answers. SourceMonte Carlo, montecarlo.ai/platform/agent-observabilityread 2026-09-21 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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DatafoldMemory & State Persistence The Slack assistant rereads the latest messages of a thread on every turn, about fifteen messages, so context lasts within a thread. The agents keep no memory across sessions with a set scope and lifetime, and the Data Knowledge Graph holds the customer's knowledge, not the agents' own memory. Sourcedocs.datafold.com/integrations/agents/slack-bot.mdread 2026-09-28 |
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Monte CarloMemory & State Persistence The agents learn and improve from previous resolutions, Monte Carlo says, and that learning goes into the product. There is no session, conversation or long term memory that an agent reads back. SourceMonte Carlo, montecarlo.ai/platform/agentsread 2026-09-21 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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DatafoldHuman Oversight & Guardrails Admins limit which MCP tools an agent's service account can call through group permissions, hiding the rest, and code review suggestions wait for people to merge them in their own Git host. Datafold's engineers oversee migrations as part of its service, which the customer does not operate, and there is no approval step before an agent action commits. Sourcedocs.datafold.com/security/mcp-tool-permissions.mdread 2026-09-28 |
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Monte CarloHuman Oversight & Guardrails People decide on what Monte Carlo's agents recommend. The Monitoring Agent proposes monitors that are set up when accepted, the Tuning Agent recommends threshold and filter changes, and the Cost and Performance Agent offers cleanup recommendations. With the MCP Server and Agent Toolkit, humans stay in the loop as well. Customers cannot insert approval steps into their own agents' workflows, and there are no autonomy modes by risk. SourceMonte Carlo, montecarlo.ai/platform/agents and mcp-and-toolkitread 2026-09-21 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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DatafoldSecurity, Identity & Governance The SafeBase trust center lists SOC 2, HIPAA and GDPR with reports available on request, and Datafold supports SSO through Okta OIDC, Google OAuth, SAML and Microsoft Entra ID with group provisioning, user roles and permissions, custom groups and service accounts. Sourcesecurity.datafold.comread 2026-09-28 |
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Monte CarloSecurity, Identity & Governance A SOC 2 Type 2 report, a SOC 3 report and an ISO 27001, 27017 and 27018 certificate are available from Monte Carlo's trust center. Its agents are read only by design and never manipulate data or systems. Monte Carlo never stores customer data or uses it to train models, agent telemetry stays in the customer's own warehouse, and traffic runs over PrivateLink. SourceMonte Carlo, trust.montecarlo.ai and montecarlo.ai mcp-and-toolkit and platform/agent-observabilityread 2026-09-21 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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DatafoldObservability & Auditability An audit log API returns each action with the user, IP, request, object type and ID, status and payload, and agents act through service accounts whose MCP tool calls run under named permissions. Datafold does not say whether the log marks an action as an agent's. Sourcedocs.datafold.com/api-reference/audit-logs/get-audit-logs.mdread 2026-09-28 |
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Monte CarloObservability & Auditability Agent Observability traces every agent run with telemetry across prompts, completions, user queries, latency and errors. It maps agent decisions step by step, surfaces configuration changes for root cause analysis, and alerts on LLM or tool failures and timeouts. Traces arrive through OpenTelemetry from any platform and are stored in the customer's own warehouse or lakehouse. SourceMonte Carlo, montecarlo.ai/platform/agent-observabilityread 2026-09-21 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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DatafoldDeployment & Data Residency Multi tenant SaaS runs in US West (Oregon) or Europe (Ireland) at the customer's choice, and Dedicated Cloud deploys a single tenant instance in a VPC on AWS, GCP or Azure, hosted by Datafold or in the customer's own account and managed by either party. Sourcedocs.datafold.com/datafold-deployment/datafold-deployment-options.mdread 2026-09-28 |
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Monte CarloDeployment & Data Residency Four deployment models are available, among them full SaaS, a customer hosted data store, and a customer hosted agent and data store, each on AWS, GCP or Azure. Agent Observability stores telemetry in the customer's own warehouse or lakehouse. SourceMonte Carlo, trust.montecarlo.ai architecture diagrams and montecarlo.ai/platform/agent-observabilityread 2026-09-21 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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DatafoldPrebuilt Agents, Templates & Packs Datafold ships specialized agents that each do their own job. The Migration Agent translates and validates pipelines, AI Code Reviews review pull requests, and a Slack assistant answers questions about data sources, lineage, monitors and diffs. An optimization agent is also named, without a description. Sourcedatafold.comread 2026-09-28 |
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Monte CarloPrebuilt Agents, Templates & Packs A fleet of specialized agents comes ready for a customer to put to work. The Monitoring, Troubleshooting, Operations, Triage, PR, Tuning and Cost and Performance agents each have their own job, and each works without the others. An open source Agent Toolkit adds skills and Claude Code plugins for data observability workflows. SourceMonte Carlo, montecarlo.ai/platform/agents and mcp-and-toolkitread 2026-09-21 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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DatafoldModel Flexibility & Routing Organization admins register their own LLM provider (OpenAI, Anthropic, Google Gemini, Azure AI Foundry, AWS Bedrock, Google Vertex AI, Databricks, Snowflake Cortex or any OpenAI compatible endpoint) and set models for goals, project defaults and per role overrides; without one the Migration Agent runs on Datafold managed inference. Sourcedocs.datafold.com/integrations/llm-providers.mdread 2026-09-28 |
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Monte CarloModel Flexibility & Routing AWS Bedrock runs Monte Carlo's own agents. Customers cannot choose the model behind them or behind the LLM as judge evaluations. SourceMonte Carlo, montecarlo.ai/mcp-and-toolkit and platform/agent-observabilityread 2026-09-21 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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DatafoldAPIs, SDKs & MCP Extensibility A Datafold API and SDK sit beside an MCP server at app.datafold.com/mcp authenticated with an API key, whose permission table names tools such as list_data_sources, create_datadiff and edit_alert that create and change Datafold's own objects. Sourcedocs.datafold.com/api-reference/mcp-server-setup.mdread 2026-09-28 |
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Monte CarloAPIs, SDKs & MCP Extensibility Any AI agent can reach Monte Carlo's observability layer through its MCP Server, from Claude, VS Code or Cursor, to triage alerts, understand asset dependencies, create monitors and diagnose integration errors. An open source Agent Toolkit on GitHub packages skills and Claude Code plugins over the platform's API. SourceMonte Carlo, montecarlo.ai/mcp-and-toolkitread 2026-09-21 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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DatafoldTesting, Debugging & Optimization The Migration Agent's translated code is checked against the source by value level data diffs, and the agent corrects itself from compilation errors and data discrepancies until parity is reached, so its output does not pass until the data matches. The final report links the diffs proving parity or explaining each discrepancy. Sourcedocs.datafold.com/data-migration-automation/datafold-migration-agent.mdread 2026-09-28 |
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Monte CarloTesting, Debugging & Optimization Agent output is evaluated in production with customizable LLM as judge or deterministic evaluations, with templates for relevancy, prompt adherence and more. Evaluations target specific spans and calls and scale with stratified sampling. Anomaly detection flags meaningful shifts, and alerts fire on low quality responses. The customer's agent is scored on output quality over time. SourceMonte Carlo, montecarlo.ai/platform/agent-observabilityread 2026-09-21 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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DatafoldBrowser & Computer Use Datafold's agents work through data source connections, Git apps, the API and MCP tools and do not control a browser, desktop or computer. Sourcedocs.datafold.com/datafold-mcp.mdread 2026-09-28 |
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Monte CarloBrowser & Computer Use The agents are read only and work through monitors, traces and data. None of them controls a browser, desktop or computer. SourceMonte Carlo, montecarlo.ai/platform/agentsread 2026-09-21 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | D Datafold |
M Monte Carlo |
|---|---|---|
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Entry price Lowest public entry point |
Outcome based fixed price scoped per migration by number of legacy objects and complexity, with timeline and data parity guaranteed by contract. No public dollar figure. | Usage based credits, $0.25 per credit on the Scale tier and $0.45 per credit on Enterprise, with total cost driven by how many monitors run and what they consume |
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Pricing confidence How public the numbers are |
Contact only | Public, partial |
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Billing Primary billing axis |
A fixed price per migration, scoped by the number of legacy objects and the migration's complexity. | credits consumed by monitors, with tiers gating users, monitor counts, and daily API calls |
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Variable cost Workload / overage exposure |
Low variable cost | High variable cost |
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Free tier / trial Try before you buy |
No free tier
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No free tier
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Buying motion Self-serve vs sales call |
Sales call | Mixed |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Monte Carlo
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· MCP / tool calling / API
Admins can now connect their own MCP servers, such as Notion, Linear, GitHub or Datadog, to Monte Carlo.
Source -
· Observability / auditability
Conversations where a monitored agent could not answer, because it lacked a permission, document, table or tool, now get their own issue cluster in Monte Carlo's Agent Observability.
Source -
· Agent capability
Monte Carlo introduced a Reinforcement Loop capability designed to enable self-improving AI agents.
Source
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