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Perpetual ML

Runs AutoML training, monitoring, and deployment in one platform that plugs directly into your data warehouse.

Checked 4 Oct 2026 Reviewed by Alex Listed since October 2024

Screenshot of Perpetual ML

At a glance

Starts at $0.90 per month Price as published by the vendor, September 2026.
Free tier No
Platforms Web
Best for Solo data scientists needing an end-to-end ML platform
Not for Teams without Snowflake or a compatible data warehouse

3.8 out of 5

Scored by Alex after real use

Our verdict

Perpetual ML bundles AutoML training with its own PerpetualBooster algorithm, experiment tracking, a model registry, drift monitoring, and both batch and real-time deployment into a single studio connected directly to your data warehouse. Continual learning claims to cut retraining time substantially as new data batches arrive, and a built-in Marimo notebook covers exploration alongside the automated pipeline. Pricing runs pay-as-you-go on compute, storage, and network egress rather than a flat fee, and native integration currently covers Snowflake, with Databricks still listed as upcoming.

✓What it does well

Automated model trainingPerpetualBooster automates training and claims leading AutoML benchmark performance.
Unified ML workflowExperiment tracking, a model registry, monitoring, and deployment all live in one platform.
Data warehouse integrationIt connects natively to Snowflake, working directly with data you already have.

✕Where it falls short

Usage-based cost adds upPricing is metered by compute, storage, and egress, so costs are not fixed the way a flat subscription fee would be.
Databricks not yet supportedNative integration currently covers Snowflake only, with Databricks support still listed as upcoming.
No free tierThere's no free plan, so usage-based charges start from the first training run.

Key features

AutoML trainingPerpetualBooster automatically trains models from your data.
Experiment trackingIt tracks, compares, and reproduces every training experiment in one place.
Drift monitoringIt monitors data and model drift without needing retraining or ground truth.
Batch & real-time deploymentTrained models deploy for either batch or real-time inference from the same platform.

Questions about Perpetual ML

What does Perpetual ML do?

Perpetual ML runs AutoML training, monitoring and deployment in one platform connected directly to a data warehouse. It trains models with its own PerpetualBooster algorithm, tracks experiments, keeps a model registry, monitors data and model drift, and deploys models for batch or real-time inference.

How much does Perpetual ML cost?

Perpetual ML starts at $0.90 per month and runs pay-as-you-go on compute, storage and network egress rather than a flat fee. There is no free tier, so charges begin with the first training run, and the reviewer notes that metered costs can add up.

Does Perpetual ML work with Snowflake and Databricks?

Perpetual ML connects natively to Snowflake and works directly with the data already stored there. Databricks support is still listed as upcoming, and Toolio lists the tool as not for teams without Snowflake or a compatible data warehouse.

What is continual learning in Perpetual ML?

Perpetual ML uses continual learning, which the vendor claims cuts retraining time substantially as new data batches arrive. A built-in Marimo notebook covers data exploration alongside the automated pipeline, and drift monitoring works without needing retraining or ground truth.

Who is Perpetual ML best for?

Toolio lists Perpetual ML as best for solo data scientists who need an end-to-end ML platform. Alex scored it 3.8 out of 5 after real use on 4 Oct 2026, crediting its automated training and unified workflow while noting its usage-based costs and Snowflake-only integration.

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