Arthur AI: AI Evaluation and Monitoring Platform
Arthur helps AI-driven organizations manage the reliability and security of their AI models. It is designed for teams in regulated industries that require observability and governance over AI agents.
At a glance
- Category
- Software Development
- Best for
- Enterprise AI teams, Regulated industry AI developers, AI product managers, AI-driven organizations
- Pricing
- Pricing includes a Free tier for up to 4 use cases, a Premium tier at $60/mo for up to 100 use cases, and custom pricing for Enterprise needs.
- Key use cases
- Generative AI Monitoring, AI Agent Evaluation, Traditional ML Model Oversight, Compliance in Regulated Industries
- Integrations
- AWS, GCP, Slack, OpenTelemetry, Docker
- Official website
- arthur.ai
Arthur is a platform designed to help technical teams evaluate, monitor, and govern AI systems across their lifecycle. It supports traditional machine learning, generative AI, and agentic AI, providing tools to identify performance drift and support model reliability.
The software is intended for AI developers, product managers, and compliance leaders. It helps users define KPIs, track hallucinations in LLMs, and enforce acceptable use policies through runtime guardrails.
The platform uses a federated architecture where the evaluation engine can run within a customer's own environment. This is designed to keep sensitive data local while sending only metrics to a central dashboard.
Buyers should confirm which deployment model best fits their needs, as the platform offers options ranging from a multi-tenant SaaS version to air-gapped on-premises installations.
Key Features
Supports testing and monitoring of AI systems from pre-production through runtime and live deployment.
Supports detection for PII leakage, prompt injections, toxicity, and hallucinations.
Includes tools to discover AI agents, enforce policies, and maintain oversight of agentic workflows.
Tracks metrics such as data drift, classification accuracy, and precision/recall for traditional ML models.
Can be deployed as a managed SaaS, within AWS/GCP environments, or as an on-premises installation.
Allows teams to create custom metrics using SQL or Python to measure domain-specific performance.
Use Cases
Tracking hallucination rates and enforcing acceptable use policies for RAG and co-pilot applications.
Analyzing tool selection accuracy and groundedness for autonomous AI agents.
Monitoring for data drift and accuracy in recommender systems and forecasting models.
Using federated data planes to support data residency for banking and healthcare AI applications.
Best For
Integrations
Pricing
Pricing includes a Free tier for up to 4 use cases, a Premium tier at $60/mo for up to 100 use cases, and custom pricing for Enterprise needs.
FAQ
Arthur supports traditional Machine Learning (ML), Generative AI (GenAI), and Agentic AI, providing a unified framework for all three.
It uses a federated control plane/data plane architecture where the data plane runs in the customer's VPC or on-prem, which is designed to keep raw sensitive data from leaving the environment.
The Free plan supports up to 4 use cases, while the Premium plan ($60/mo) supports up to 100 use cases and includes customizable dashboards and alerting.
Source category: Software Development
Source subcategory: AI Development Platform
