Concepts

Baseten provides two core development workflows: developing a model with Truss and orchestrating models with Chains. Both are building blocks for production-grade ML systems, but they solve different problems.

Developing a model with Truss

Package and deploy any AI/ML model as an API with Truss or a Custom Server.

Developing a Chain

Orchestrate multiple models and logic, enabling complex inference workflows.

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Truss vs. Chains: When to use each

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Developing a model with Truss

Truss is the open-source package you use to turn any ML model into a production-ready API on Baseten.

Use Truss when:

• You’re deploying a single model. Whether it’s a fine-tuned transformer or a scikit-learn classifier, Truss wraps your model with code, configuration, and environment so it can be deployed and served at scale.

• You need control over how your model runs. Truss supports custom Python packages, system dependencies, GPU settings, and custom inference logic. You can define pre- and post-processing, batching, and logging — all versioned and deployable.

• You want to keep development local and reproducible. Truss makes it easy to develop locally in a containerized environment, then push to Baseten with confidence that the same code will run in production.

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Orchestrating with Chains

Chains are for building inference workflows that span multiple steps, models, or tools. You define a sequence of steps — like routing, transformation, or chaining outputs — and run them as a single unit.

Use Chains when:

• You’re combining multiple models or tools. For example, running a vector search + LLM pipeline, or combining OCR, classification, and validation steps.

• You want visibility into intermediate steps. Chains let you debug and monitor each part of the workflow, retry failed steps, and trace outputs with ease — something that’s much harder with a single model endpoint.

• You’re using orchestration libraries like LangChain or LlamaIndex. Chains integrate natively with these frameworks, while still allowing you to insert your own logic or wrap Truss models as steps.