# Source: https://docs.baseten.co/inference/concepts.md

# Source: https://docs.baseten.co/development/concepts.md

# Source: https://docs.baseten.co/development/chain/concepts.md

# Source: https://docs.baseten.co/deployment/concepts.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Concepts

Baseten provides a flexible and scalable infrastructure for deploying and managing machine learning models. This page introduces key concepts - [deployments](/deployment/deployments), [environments](/deployment/environments), [resources](/deployment/resources), and [autoscaling](/deployment/autoscaling) — that shape how models are served, tested, and optimized for performance and cost efficiency.

## Deployments

[Deployments](/deployment/deployments) define how models are served, scaled, and updated. They optimize resource use with autoscaling, scaling to zero, and controlled traffic shifts while ensuring minimal downtime. Deployments can be deactivated to pause resource usage or deleted permanently when no longer needed.

<img noZoom src="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=833c7f33c3a39b50502326b9cec14090" data-og-width="964" width="964" data-og-height="552" height="552" data-path="images/deployments.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=280&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=85501a364b60a6f564dfbdfb79af9038 280w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=560&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=bc9e92a71bc5bc07b8a712e8583f00b8 560w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=840&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=687778fd0bf01755091841abba9f2c16 840w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=1100&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=12ca03f794ef57bb9b92d87ece692527 1100w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=1650&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=cea81f5e83fad83853169baae69d24b7 1650w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployments.png?w=2500&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=17a1fbe9a5dd58f733b350400a9acfe7 2500w" />

## Environments

[Environments](/deployment/environments) group deployments, providing stable endpoints and autoscaling to manage model release cycles. They enable structured testing, controlled rollouts, and seamless transitions between staging and production. Each environment maintains its own settings and metrics, ensuring reliable and scalable deployments.

<img noZoom src="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=e922f3e12c24577d6594ca58f80431ee" data-og-width="964" width="964" data-og-height="552" height="552" data-path="images/deployment-environments.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=280&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=210053dfe7b0d3398f261f1105ac6bf9 280w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=560&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=3a7e2675275ae1f78ac5fe83330e926c 560w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=840&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=874b4335f21d8939c58dcd2c2b978c8c 840w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=1100&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=09f864b743a3921b713612d54ed9708b 1100w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=1650&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=a6397fff11d9c0edfd8103f44b8b501e 1650w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-environments.png?w=2500&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=23a5dd05c6ee1ab42cecb3698098cbb3 2500w" />

## Resources

[Resources](/deployment/resources) define the hardware allocated to a model server, balancing performance and cost. Choosing the right instance type ensures efficient inference without unnecessary overhead. Resources can be set before deployment in Truss or adjusted later in the model dashboard to match workload demands.

<img noZoom src="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=028f7b8a7c5e1d92f0b55f2eec8aad11" data-og-width="964" width="964" data-og-height="552" height="552" data-path="images/deployment-resources.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=280&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=5bc11192879c6aa199388df6c531a5a0 280w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=560&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=acbafbbe854fa0701fc63f47ab84a8f0 560w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=840&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=5d677c56115318ebfb994e554a1f657c 840w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=1100&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=b1fd3e5c69b2b965884044cb6975d2c6 1100w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=1650&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=5ed48475146334e0a2fc56f11511d599 1650w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-resources.png?w=2500&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=c8045c2ee7a60d89a940119536d71005 2500w" />

## Autoscaling

[Autoscaling](/deployment/autoscaling) dynamically adjusts model resources to handle traffic fluctuations efficiently while minimizing costs. Deployments scale between a defined range of replicas based on demand, with settings for concurrency, scaling speed, and scale-to-zero for low-traffic models. Optimizations like network acceleration and cold start pods ensure fast response times even when scaling up from zero.

<img noZoom src="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=f867eb7ccef178bae2fd11117365bebb" data-og-width="964" width="964" data-og-height="552" height="552" data-path="images/deployment-autoscaling.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=280&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=b88950b4dd0eb765a37d74991ce29062 280w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=560&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=f0b5d2412ebf1a9117c165f9b5711f18 560w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=840&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=5eb7a1b115bd723b732d5889382742df 840w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=1100&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=74fb3e685e86eddade1e04ef92b9e424 1100w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=1650&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=b9c25eb323072c47121cde1c20c12a44 1650w, https://mintcdn.com/baseten-preview/W3NbEem9OZkF5rdB/images/deployment-autoscaling.png?w=2500&fit=max&auto=format&n=W3NbEem9OZkF5rdB&q=85&s=61671a961d7da633fb3a95ab4becd92c 2500w" />