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Arthur vs Radicalbit AI Monitoring

Arthur

arthur.ai

Radicalbit AI Monitoring

radicalbit.ai

Side by side

FigureArthurRadicalbit AI Monitoring
Price from$60/moFree
Free planYesYes
Free trial——
Open source——
WebYesYes
APIYesYes
Self-hostedYesYes
Plans published31
Spec score · Machine Learning Model Monitoring Software8.3 (#7)7.4 (#12)
DeploymentArthur offers hosted SaaS, on-premises, and hybrid deployment options; its hybrid evals engine runs next to customer workloads so sensitive data stays in the environment.The repository provides Docker Compose installation for a local K3s-based deployment and supports MinIO or real AWS S3 storage.

Plans

Arthur

  • FreeFree
  • Premium$60 a month
  • EnterprisePriced on request

Arthur pricing →

Radicalbit AI Monitoring

  • Free / Open SourceFree

Radicalbit AI Monitoring pricing →

In their own words

Arthur

Arthur helps enterprises find AI agents across their environments and provides tools to observe and govern them. Discovery sensors monitor OpenTelemetry streams, MCP servers, network traffic, cloud APIs, and employee endpoints. The platform traces agent steps, tool calls, retrieved context, costs, latency, and behavior over time. Its governance features apply machine-checkable policies and provide real-time guardrail verdicts for prompt injection, PII exposure, restricted models, and off-policy tool use. Deployment options include hosted SaaS, on-premises, and hybrid; for hybrid setups, the evals engine runs beside customer workloads so sensitive data stays in the environment. Arthur lists RBAC, SSO, and deployed engineering support with every deployment option. Documented integrations include OpenAI, Anthropic, LangChain, LiteLLM, CrewAI, LlamaIndex, OpenAI Agents, Google ADK, AWS Bedrock, and Mastra. The Free plan is free, with up to four use cases, two projects, and seven-day data retention. Premium is $60 a month; Enterprise pricing is custom.

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Radicalbit AI Monitoring

Radicalbit AI Monitoring is a free, self-hosted platform for monitoring production LLM and machine-learning model effectiveness and reliability. It checks numerical and categorical data for anomalies, missing values, and outliers, then tracks metrics over time. Available model measures include Precision, Accuracy, Recall, F1, MSE, MAE, Perplexity, and Probability. Drift detection offers Kolmogorov-Smirnov, PSI, Wasserstein, Jensen-Shannon, Chi-square, Kullback-Leibler, and Hellinger methods. Reference and current datasets can be compared for data quality, model quality, and drift. LLM tracing records requests, prompts, and tools and displays sessions, traces, and spans with hierarchy and metadata. Documentation for version 1.4.0 focuses on binary and multiclass classification and regression, with more model types planned. A web UI, Python SDK, and REST API are provided. Local deployment uses Docker Compose with K3s and MinIO or AWS S3.

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