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SPECIFICTION
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Deepchecks vs Pulsar

Deepchecks

deepchecks.com

Pulsar

pulsar.ml

Side by side

FigureDeepchecksPulsar
Price fromFree—
Free planYes—
Free trialYes—
Open sourceYes—
WebYes—
macOSYes—
LinuxYes—
APIYesYes
Self-hostedYesYes
Plans published80
Spec score · Machine Learning Model Monitoring Software8.9 (#2)6.8 (#18)
Spec score · Model Monitoring Software8.9 (#6)6.8 (#24)
DeploymentDeployment 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.
SecurityThe 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

Deepchecks pricing →

Pulsar

No plans published.

Pulsar pricing →

In their own words

Deepchecks

Deepchecks has no write-up on Specifiction yet; its figures are above.

Visit deepchecks.com

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.

Visit pulsar.ml