Deploy production-ready models with one line of Python

Baseten is the MLOps platform for startups to rapidly develop, deploy, and test models in production.

Trusted by top data science and machine learning teams

Straightforward deploys, in one place

Deployment shouldn't mean becoming a Kubernetes expert. Instantly deploy models from any training environment with one line of Python, while keeping versions and inference centralized.

"Baseten provides us with all of the speed and control of self-serving our model deployment without any of the annoying config, infra, and health checks."

Daniel Whitenack
Data Scientist at SIL

Notebook agnostic

Deploy models in-line, using your preferred training environment

Containerized environments

Set global system packages and settings to run models from anywhere, by anyone

Version control

Track model artifacts and training metadata with every new model version

Ship production-ready APIs, fast

Models deployed to Baseten are put behind a REST API for immediate use in production. Auto-scaling resources ensure efficient, low-latency performance even in high-traffic scenarios.

"Baseten gets the process of tool-building out of the way so we can focus on our key skills: modeling, measurement, and problem solving."

Nikhil Harithas
Senior ML Engineer at Patreon

Horizontal scaling

Scale up or down automatically to handle increased traffic, efficiently

Serverless GPUs

Deploy to a GPU instantly. Warm starts ensure fast availability

Full visibility

Comprehensive logs ensure you can quickly debug any issues

Works with any model framework.
Built on open source.

Baseten is built on Truss, an open-source standard for packaging models built in any framework. Share and deploy to any environment, locally or in-production.

Better visibility,
better models

Accelerate iteration cycles between model versions with robust model monitoring, drift detection, and A/B testing.

"Baseten provides an easy way for us to host our models, iterate on them, and experiment without worrying about any of the DevOps involved."

Faaez Ul Haq
Head of Data Science at Pipe

Model monitoring

Gain insight into your model's traffic and resource utilization in real-time

Drift detection

Coming soon

Set custom conditions and get alerts when your model's drift is too large

A/B testing

Coming soon

Test multiple versions simultaneously to optimize for model performance

No more abandoned models

Start shipping machine learning in production and driving business outcomes today.

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