Pulsar vs Radicalbit AI Monitoring

Pulsar is a free, open-source, self-hosted system for monitoring machine learning models in production. It tracks model drift, concept drift, and performance by comparing incoming behavior with training data used as a baseline. An SDK and containerised ingestion service collect model inputs and outputs into a time series database, supporting historical and real-time data. The current database integration is InfluxDB, and the project is designed for large ingestion volumes without degraded performance. A scheduler checks drift and can trigger standard or customised alerts when metrics reach thresholds. Grafana displays drift measures across selected time ranges and user-defined features. Available drift tests include Kullback-Leibler divergence, Kolmogorov-Smirnov, Levene, and Wasserstein; performance measures include accuracy, recall, F1 score, Brier score, mean squared error, and R2. Deployment logos cover Flask, FastAPI, TensorFlow Extended, Seldon, AWS, Azure, Google Cloud, and Grafana.

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.