Agentic Index
Fiddler AI vs Monte Carlo (2026)
Both vendors last verified
Both watch production AI systems and they come at it from opposite disciplines. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Fiddler is an AI observability and security control plane for agents, LLM apps and machine learning models, with real time guardrails, explainability and governance. Monte Carlo comes from data reliability and unifies data and agent observability, so teams monitor from the pipelines feeding agents through to the outputs they produce, on usage based credits. If your agents are wrong because the data was wrong, Monte Carlo sees the cause and Fiddler sees the symptom.
On the Agentic Index agent infrastructure ranking, Fiddler AI clears the bar and Monte Carlo does not. Fiddler AI 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. Fiddler AI 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 Fiddler AI if
- Explainability and real time guardrails are the controls you need on model behavior.
- Machine learning models alongside agents and LLM apps are in scope.
- Governance of the model layer is the requirement your risk function set.
Choose Monte Carlo if
- Bad data is your actual failure mode, and pipeline visibility is what finds it.
- One platform across data and agents beats two, with specialized monitoring, troubleshooting and triage agents ready to switch on.
- Usage based credits let cost track your actual monitoring volume.
| Feature | F Fiddler AI |
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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Fiddler AIIntegrations & Tool Calling Fiddler takes in traces from agent frameworks, OpenTelemetry, S3 and AI gateways (Kong, AgentGateway, LiteLLM), reads model data from Snowflake, S3, SageMaker, Databricks and MLflow, and sends alerts to Datadog and PagerDuty. These move telemetry in and alerts out, and there are no connectors that let an agent take authenticated actions in outside systems. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIWorkflow Orchestration There are no workflows that sequence, branch or retry an agent's steps. Fiddler observes, evaluates and guards agents that run elsewhere. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AITriggers & Channel Coverage Guardrails evaluate every agent request and response inline as it passes, continuous evaluations score production traces, and alert rules fire on monitored metrics, so Fiddler's evaluation and enforcement start from each event with no person initiating them. SourceFiddler, fiddler.ai llms.txt and homepage, and docs.fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIKnowledge Grounding & RAG There is no retrieval structure over the customer's knowledge that grounds an agent's answers. Fiddler evaluates RAG applications built elsewhere, including checks for faithfulness and hallucination. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIMemory & State Persistence There is no session, conversation or long term memory that an agent reads back as context. Fiddler records sessions and traces for observability. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIHuman Oversight & Guardrails Guardrails enforce policy inline on an agent's request and response path in under 100 milliseconds, detecting hallucinations, toxicity, PII and PHI leakage, prompt injection and jailbreaks. For coding agents they redact PII, PHI and secrets inline through the customer's existing LLM gateway, and enforceable policy is one of the platform's five pillars. SourceFiddler, fiddler.ai llms.txt and homepage, and docs.fiddler.ai llms.txt (AI gateway guardrail enforcement)read 2026-09-21 |
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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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Fiddler AISecurity, Identity & Governance The SOC 2 Type II report covers security, confidentiality and availability, is audited every year and is available under NDA, and Fiddler states HIPAA compliance. Customers sign in through SSO providers and grant granular permissions with role based access control, data is encrypted with AES-256 at rest and TLS 1.2 or higher in transit, and customer data is kept and deleted under a retention policy. SourceFiddler, fiddler.ai/securityread 2026-09-21 |
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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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Fiddler AIObservability & Auditability Visibility runs from application to session, agent, trace and span. There are native SDKs for LangGraph, LangChain, Google ADK and Strands, OpenTelemetry ingestion, tracing of LLM calls and tool use for coding agents through Claude Code and existing gateways, alerts routed to Datadog and PagerDuty, and customer data kept under a stated retention policy. SourceFiddler, fiddler.ai llms.txt, docs.fiddler.ai llms.txt and fiddler.ai/securityread 2026-09-21 |
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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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Fiddler AIDeployment & Data Residency Deployment runs as SaaS, in the customer's VPC, on premises, in AWS GovCloud or air-gapped, and as a Partner AI App inside the customer's Amazon SageMaker, with Fiddler's Centor evaluation models running entirely in the customer's environment. SourceFiddler, fiddler.ai homepage and llms.txt, and docs.fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIPrebuilt Agents, Templates & Packs There are no ready made agents, workflows or templates a customer adopts and runs. Fiddler ships prebuilt evaluators, a metrics library and cookbooks for common uses. SourceFiddler, docs.fiddler.ai llms.txt and fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIModel Flexibility & Routing Custom prompts and a customer choice of judge model drive Fiddler's LLM-as-a-judge evaluators, beside its own Centor Models that run in the customer's environment, so the customer chooses the model behind Fiddler's own evaluation features. Fiddler does not publish which judge models are supported. SourceFiddler, docs.fiddler.ai llms.txt (Advanced Prompt Specs) and fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIAPIs, SDKs & MCP Extensibility Native SDKs include Fiddler's OpenTelemetry SDK plus LangGraph, LangChain, Google ADK, Strands and Evals SDKs, alongside a Python client for alerts and other resources and a Fiddler MCP server that lets assistants that speak MCP query applications, traces and evaluator results. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AITesting, Debugging & Optimization The Fiddler Evals SDK runs experiments on LLM applications with pre-built evaluators and custom metrics, datasets with golden labels and side-by-side comparison, custom LLM-as-a-judge evaluators, and continuous evaluation in production over more than 100 metrics, scored by Fiddler's Centor Models in the customer's environment. SourceFiddler, docs.fiddler.ai llms.txt and fiddler.ai llms.txtread 2026-09-21 |
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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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Fiddler AIBrowser & Computer Use Agents get no browser, desktop or computer control, since Fiddler observes and guards agents. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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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 | F Fiddler AI |
M Monte Carlo |
|---|---|---|
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Entry price Lowest public entry point |
Free plan (guardrails) · Developer $0.002 per trace · Enterprise custom | 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 |
Public, partial | Public, partial |
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Billing Primary billing axis |
Usage-based on the Developer plan, at $0.002 per trace; Enterprise quoted. | credits consumed by monitors, with tiers gating users, monitor counts, and daily API calls |
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Variable cost Workload / overage exposure |
High variable cost | High variable cost |
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Free tier / trial Try before you buy |
Free tier
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No free tier
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Buying motion Self-serve vs sales call |
Mixed | Mixed |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Fiddler AI
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· MCP / tool calling / API
Evals datasets in Fiddler can now be edited through the REST API in Release 26.25, with every add, update or delete saved as a new numbered version that can still be read later.
Source -
· Human approval / guardrails
Release 26.24 adds secret detection to Fiddler's Sensitive Information guardrail, flagging API keys, tokens, credentials and high entropy strings across more than 35 known secret formats as a new entity type in the PII categories.
Source -
· MCP / tool calling / API
Fiddler shipped a Fiddler MCP Server with OAuth 2.0 authentication for clients such as Claude Code, along with trace routing across multiple applications, in releases 26.15 to 26.19.
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.
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· 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.
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· Agent capability
Monte Carlo introduced a Reinforcement Loop capability designed to enable self-improving AI agents.
Source
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