Arize Phoenix vs Pulsar
Arize Phoenix
arize.comPulsar
pulsar.mlSide by side
| Figure | Arize Phoenix | Pulsar |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Machine Learning Model Monitoring Software | 8.0 (#8) | 6.8 (#18) |
| Spec score · Model Monitoring Software | 8.1 (#16) | 6.8 (#24) |
| Deployment | Phoenix can run locally or be self-hosted using Docker, Kubernetes, or a cloud environment. | Pulsar provides an SDK and a containerized data ingestion service for integration with a user's machine and production code. |
| Maker | Phoenix is built by Arize AI and the open-source community. | Rocket Science Development is identified as Pulsar.ml's maker, and its site lists an address in Longueuil, Québec, Canada. |
| Purpose | Phoenix helps teams trace, evaluate, debug, and improve AI applications. | Pulsar monitors machine learning models in production, including data and model drift and model performance. |
| Support | The Phoenix documentation points users to a Slack community for questions and developer discussion. | The help center links to a quick-start guide, how-to guides, contact support, a user community, and expert help. |
Plans
In their own words
Arize Phoenix
Arize Phoenix is free, open-source software for tracing, evaluating, debugging, and improving AI applications. It captures model calls, retrieval, tool use, and custom logic as OpenTelemetry traces. Teams can score traces and spans with LLM evaluators, code checks, or human labels. Prompt tools provide versioning, side-by-side testing, and call replay, while dataset experiments compare evaluation results across application versions. Documented integrations include LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic, plus evaluation libraries such as Harbor, Ragas, Cleanlab, and MLflow. Phoenix runs locally or can be self-hosted with Docker, Kubernetes, or a cloud environment; Arize says self-hosting keeps traces in the user’s environment. Self-hosted authentication supports OAuth2 identity providers with an OpenID Connect well-known endpoint. The Phoenix plan costs nothing and leaves trace spans, ingestion, projects, and retention under user management. The project is ELv2 licensed and built by Arize AI and the open-source community.
Pulsar
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.