🎥 Unraveling prompt engineering is up on YouTube

Model observability for
Fraud detectionLife Time Value (LTV)Claim fraudSpam detectionContent creationDemand forecastingPredictive maintenanceTranslationQuestion answeringSummarizationAudio-to-textLending approvalFacial recognitionChatbotsClick Through Rate optimizationRecommendation engines

Get in minutes what used to take years to build.
Simple, customizable, scalable, secure, ML monitoring.
lottie image mobile placeholder

Data quality

Input drift

Bias monitoring

Performance analysis

Model shifts

Explainability

Version comparison

Anomaly investigation

Observability management

LLM monitoring

Create custom integrity metric

Just select the data quality metric that you need to monitor or build a custom integrity metric for your use case.

Input drift detection settings

Customize your drift metrics from a-z – distance functions, features, datasets, timeframes, sensitivity, and much more.

New bias metric

Build any bias metric your business needs and monitor them across different protected classes and sub-groups.

Performance analysis

Track performance continuously and analyze changes, and drill into behavior segment by segment.

Distribution chart

Easily identify and investigate model shifts and drill down into granular data to pinpoint the root cause.

Fraud probability

Explain model behaviors on the global, cohort, and individual decision level.

Analytics table

Analyze and compare versions, datasets, and production timeframes to detect changes.

Predictions over time

Correlate and group anomalies to quickly pinpoint casualty and resolve issues before they impact your business.

Projects

Centralized model monitoring management per use case. Build segments, manage configurations, and create monitors once for multiple models.

Easily gain visibility into LLM and prompt/responce behaviors and detect drift, security issues, and privacy violations.

Data quality

Create custom integrity metric

Just select the data quality metric that you need to monitor or build a custom integrity metric for your use case.

Input drift

Input drift detection settings

Customize your drift metrics from a-z – distance functions, features, datasets, timeframes, sensitivity, and much more.

Bias monitoring

New bias metric

Build any bias metric your business needs and monitor them across different protected classes and sub-groups.

Performance analysis

Performance analysis

Track performance continuously and analyze changes, and drill into behavior segment by segment.

Model shifts

Distribution chart

Easily identify and investigate model shifts and drill down into granular data to pinpoint the root cause.

Explainability

Fraud probability

Explain model behaviors on the global, cohort, and individual decision level.

Version comparison

Analytics table

Analyze and compare versions, datasets, and production timeframes to detect changes.

Anomaly investigation

Predictions over time

Correlate and group anomalies to quickly pinpoint casualty and resolve issues before they impact your business.

Observability management

Projects

Centralized model monitoring management per use case. Build segments, manage configurations, and create monitors once for multiple models.

LLM monitoring

Easily gain visibility into LLM and prompt/responce behaviors and detect drift, security issues, and privacy violations.

Try the community edition

No credit card required. FREE forever.

Don’t just take our word for it

We use Superwise on a daily basis to get transparency and detect drift and model decay. Enabling us to better understand changes, connect them to real events and data bugs, and make better decisions and move even faster than before.

Ohad Hegedish
monday.com logo

Superwise named a Gartner Cool Vendor in the 2020 Enterprise AI Governance report.

Gartner cool vendor 2020

Today, we use Superwise to monitor over 6K metrics in real-time. Giving us control over our dynamic business and peace of mind that we’ll always know about unwanted issues.

Paz Aviv
Riskified Logo

Superwise was highlighted as an MLOps solution vendor in Forrester’s 2020 “Introducing ModelOps to Operationalize AI” report.

Forrester Logo

By selecting Superwise we can ensure accuracy and efficiency of our data science efforts.

Aviad Klein
Fundbox Logo

Superwise is part of the NVIDIA inception program.

Nvidia Logo

We’re excited to partner with Superwise. Superwise’s model observability integration with Datadog will help MLOps teams ensure models maintain calibration and accuracy during their lifetime in production.

Michael Gerstenhaber
Datadog logo icon