Radicalbit AI Monitoring vs SUPERWISE

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.

SUPERWISE AMP is an AI governance platform that sits between AI applications and models to observe interactions and apply policies. Its guardrails evaluate inputs and outputs, with policy evaluation advertised as taking under 10 ms. Agent Studio supports building, testing, and deploying agents, with governance and automated failure detection. The platform monitors operational health and drift and offers analytics, dashboards, audit trails, compliance reports, and metrics for model performance, data quality, and bias. SUPERWISE says it connects to 50+ providers, vector databases, and enterprise tools, including OpenAI, Anthropic, AWS Bedrock, LangChain, Pinecone, Snowflake, Salesforce, and Slack. It provides REST API and Python SDK access. AMP is managed SaaS by default, with connections to private, on-premises, and air-gapped environments. Sentinel inspects prompts locally and redacts secrets and personal data before they leave the machine; traffic does not pass through SUPERWISE. Plans start at $10/mo, with a 30-day trial and no free plan.