Fiddler AI vs Radicalbit AI Monitoring

Fiddler AI provides observability and security for AI agents and predictive applications. It evaluates agents and models during development, then monitors them in production, with diagnostics spanning more than 100 built-in and custom metrics such as hallucination, toxicity, PII/PHI, drift, performance, and business KPIs. Real-time guardrails can detect risks and pause, reroute, or escalate agent actions. The platform records decisions, actions, evaluations, and policy outcomes to support audit evidence for requirements including GDPR, HIPAA, NAIC, and SR 11-7. It names LangGraph, Amazon Bedrock, and AWS Strands Agents as native framework support, and is built on OpenTelemetry standards. Deployment options include cloud SaaS, VPC, on-premises, and air-gapped environments. Fiddler says it is SOC 2 Type II certified, complies with HIPAA, and encrypts data in transit and at rest. Plans include Free at free, Developer at a price agreed with the maker billed per trace, and Enterprise with no listed price. The company was founded in 2018 and is headquartered in Palo Alto, California.

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.