Deepchecks vs Pulsar
Deepchecks
deepchecks.comPulsar
pulsar.mlSide by side
| Figure | Deepchecks | Pulsar |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | — |
| Open source | Yes | — |
| Web | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 8 | 0 |
| Spec score · Machine Learning Model Monitoring Software | 8.9 (#2) | 6.8 (#18) |
| Spec score · Model Monitoring Software | 8.9 (#6) | 6.8 (#24) |
| Deployment | Deployment options listed include multi-tenant SaaS, virtual private cloud on GCP or Azure, bare metal, and AWS-managed deployment. | Pulsar provides an SDK and a containerized data ingestion service for integration with a user's machine and production code. |
| Security | The site lists SOC 2 Type 2, GDPR, HIPAA compliance, single sign-on, and AWS GovCloud support. | The homepage says production data is stored in a safe and reliable environment. |
Plans
Deepchecks
- BasicFree
- Open-Source MonitoringFree
- Startup MonitoringPriced on request
- BasicPriced on request
- Dedicated MonitoringPriced on request
- EnterprisePriced on request
- Partnership MonitoringPriced on request
- ScalePriced on request
In their own words
Deepchecks
Deepchecks has no write-up on Specifiction yet; its figures are above.
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.