NannyML
What it is
NannyML monitors deployed machine-learning models and estimates performance when ground-truth labels are delayed or unavailable. It detects concept drift and multivariate or univariate data drift, and connects alerts with changes in model performance. Data-quality checks and ranked drift alerts help teams investigate features associated with performance issues. Users can relate model performance to monetary or other business outcomes. NannyML Cloud supports webhooks for retraining actions and an SDK for automating monitoring-data ingestion. Cloud is offered through Azure and AWS marketplaces as SaaS or a managed application; in the managed option, the application and required infrastructure are provisioned inside the customer's cloud subscription. For the Azure managed application, NannyML documents encrypted traffic and disks, but says automated backups were still being implemented and advises customers to safeguard model inputs and outputs elsewhere. The open-source library installs with pip or conda. Direct local dataset uploads through the Cloud UI must be smaller than 100 MB. Open Source is free and self-managed; Starter is listed at $399 a month and Scale at $999 a month.
For and against
For
- Estimates model performance when labels are delayed or unavailable.
- Links drift alerts to changes in model performance.
- Connects model performance with business outcomes.
- Supports webhooks and automated monitoring-data ingestion.
- Offers a free self-managed open-source plan.
Against
- Cloud UI local uploads must be smaller than 100 MB.
- Azure managed-application automated backups were still being implemented.
- Starter is listed at $399 a month.
- Scale is listed at $999 a month.
The verdict
NannyML combines drift monitoring, performance estimation, and business-impact views for deployed models. Consider its upload limit and the documented Azure backup status when choosing a deployment approach.
Who it suits
NannyML suits teams monitoring deployed machine-learning models, especially when ground truth is delayed or absent. The self-managed open-source library may suit users who prefer to install with pip or conda.
Plans and prices
Open Source
self-managed · core monitoring algorithms
Read 15 Oct 2026 · nannyml.com
Starter (beta)
2 models · 1 submodel · unlimited users · 10 million rows
Read 15 Oct 2026 · nannyml.com
Starter
2 models · 10 M predictions · email support · 30-day free trial
Read 15 Oct 2026 · nannyml.com
Scale
6 models · private Slack · 30-day free trial
Read 15 Oct 2026 · nannyml.com
Pro (beta)
10 models · 1 submodel · unlimited users · unlimited rows
Read 15 Oct 2026 · nannyml.com
Enterprise
unlimited models · unlimited predictions · 24/7 support · 30-day free trial
Read 15 Oct 2026 · nannyml.com
Enterprise (beta)
custom models, submodels, users and retention · unlimited rows and features
Read 15 Oct 2026 · nannyml.com
On the record
- Alert ranking
- NannyML ranks drift alerts by linking them to observed performance changes and affected features.docs.nannyml.com · read 15 Oct 2026
- API and SDK
- The Python Cloud SDK can create monitoring models, ingest inference data, add delayed targets and trigger analyses using an API token.github.com · read 15 Oct 2026
- Automation
- NannyML Cloud supports webhooks to trigger retraining actions and an SDK to automate monitoring data ingestion.nannyml.com · read 29 Sep 2026
- Azure authentication
- Azure managed deployments support basic authentication, Azure Entra integration or no authentication.docs.nannyml.com · read 15 Oct 2026
- Business impact
- Users can connect model performance to monetary or other business outcomes through business-value calculations.nannyml.com · read 15 Oct 2026
- Cloud marketplaces
- NannyML Cloud is available through the Azure and AWS marketplaces, with SaaS and managed-application deployment options.docs.nannyml.com · read 29 Sep 2026
- Company headquarters
- NannyML is headquartered in Leuven, Vlaams-Brabant, Belgium.crunchbase.com · read 15 Oct 2026
- Company legal name
- Crunchbase lists the legal name as NannyML NV and operating status as closed under Soda.crunchbase.com · read 15 Oct 2026
- Company mission
- NannyML describes its work as building a post-deployment data-science toolkit for monitoring what matters after model deployment.nannyml.com · read 29 Sep 2026
- Company size
- LinkedIn lists the company size as 2–10 employees and shows Leuven as headquarters.linkedin.com · read 15 Oct 2026
- Concept drift
- NannyML measures whether concept drift is affecting model performance and uses this as a retraining signal.nannyml.com · read 15 Oct 2026
- Data locality
- For the managed-application deployment, NannyML says the application and required infrastructure are provisioned within the customer's Azure or AWS subscription.docs.nannyml.com · read 29 Sep 2026
- Data quality
- The product performs continuous data-quality checks on model inputs and outputs.nannyml.com · read 15 Oct 2026
- Deployment choices
- The cloud product can run as a managed application inside a customer Azure or AWS subscription or as vendor-hosted SaaS.docs.nannyml.com · read 15 Oct 2026
- Documentation depth
- Documentation includes tutorials for tabular, text and computer-vision models, custom metrics, reporting, deployment, SDK usage and API reference.docs.nannyml.com · read 15 Oct 2026
- Drift monitoring
- NannyML detects concept drift and multivariate and univariate data drift, and relates drift alerts to changes in model performance.nannyml.com · read 29 Sep 2026
- Funding
- An October 2024 European Innovation Council list names NannyML NV's EU-AURA project for blended finance funding consideration.eic.ec.europa.eu · read 15 Oct 2026
- Input limits
- The Cloud UI accepts direct local dataset uploads only when the file is smaller than 100 MB.docs.nannyml.com · read 29 Sep 2026
