Opik vs Radicalbit AI Monitoring

Opik helps teams building applications or agents that call an LLM log actions and evaluate behavior across development, testing, and production. It captures and visualizes traces, supports annotation, and provides audit logs. Its evaluation tools include more than 30 metrics for outcomes such as answer relevance, context precision, task completion, and hallucination. In production, Opik can evaluate traces in real time, alert when criteria fail, and track token use and model costs. Test Suites offer pass/fail assertions, an Agent Playground supports end-to-end tests, and a Prompt Optimizer provides six algorithms. Core observability and evaluation features are available as open-source code for local use. Opik includes a REST API and Python client, with Python and TypeScript SDKs listed, and integrations with more than 40 frameworks, model providers, and gateways. Free Cloud allows up to 10 team members, 25,000 spans per month, and 60-day retention. Pro Cloud costs $19 a month and lists up to 50 team members and 100,000 spans monthly.

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.