tools
Truss
Truss packages, configures, and deploys machine-learning models as production APIs through Baseten.
Truss is an open-source CLI and model-serving framework for data scientists and machine-learning engineers. It defines model dependencies, runtime settings, hardware, weights, and optional Python serving logic, then deploys models to Baseten.
It supports configuration-only models, custom Python models, and custom Docker servers, including models built with frameworks such as vLLM, SGLang, TensorRT-LLM, PyTorch, and TensorFlow. Truss itself is free, but Baseten hosting, GPU compute, model APIs, and training are billed separately.
Features
- Packages model code, weights, dependencies, and runtime configuration
- Deploys configuration-only models from config.yaml
- Supports custom Python Model classes with load and predict methods
- Supports custom Docker servers and existing model containers
- Provides CLI commands for local development, deployment, and promotion
- Supports live reload and development deployments
- Works with vLLM, SGLang, TensorRT-LLM, PyTorch, and TensorFlow
- Open-source under the MIT License
Use cases
- Package a trained model as a shareable serving artifact
- Deploy an open-source model as a production API
- Iterate on model code with live reload during development
- Bring an existing vLLM or SGLang container to deployment
- Configure GPU hardware, dependencies, and model weights
- Promote a tested model deployment to production
Pros
Cons
Pricing
- Starting price
- $0 per month, pay as you go
- Pricing checked
- 2026-09-19
Basic
$0 per month, pay as you go
- Dedicated deployments
- Model APIs
- Training
- Fast cold starts
- Email and in-app chat support
Pro
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- Priority access to high-demand GPUs
- Dedicated compute
- Higher Model API rate limits
- Hands-on engineering expertise
- Dedicated support on Slack and Zoom
Enterprise
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- Custom SLAs
- Self-host deployments
- On-demand flex compute
- Full control over data residency
- Advanced RBAC with Teams