MMDeploy provides a model deployment framework from the OpenMMLab project.
Open source
24 alternativesTruss
Package, configure, and iterate on a model with Truss at whatever level of control your model needs.
About Truss
Truss is a model packaging framework for developing and deploying models on Baseten dedicated infrastructure. Users define the container, dependencies, and runtime in config.yaml, add a Python Model class with load and predict methods for custom logic, or bring an existing container based on vLLM, SGLang, Triton, or another image.
Development uses the Baseten CLI to create single-replica deployments with live reload, then publish immutable production deployments with autoscaling and observability. Development deployments are limited to one replica and intended for iteration rather than production traffic. Multi-step workflows can use Chains to run models on separate hardware with independent scaling rules.
Key features
- Define GPU, dependencies, base image, and weights in config.yaml
- Add custom Python Model class with load and predict
- Deploy existing vLLM, SGLang, Triton servers as custom Docker
- Sync local changes with live reload without rebuilding container
- Create immutable production deployments with autoscaling and observability
- Build multi-model Chains with independent hardware and scaling
Best for: Developers who need to package custom or open-source models for deployment on Baseten.
Good for
- Supports config-only deployment without custom serving code
- Supports custom Python and custom Docker for different control levels
- Provides live reload iteration and immutable production deployments with stable endpoint URLs
Watch out for
- Development deployments are limited to one replica and not for production traffic
Truss at a glance
- Website
- truss.baseten.co
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Truss alternatives
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Edge Impulse provides MLOps workflows for developing, deploying, and monitoring edge and physical AI models.
BLACKBOX AI serves selected open-weight models as dedicated deployments or through one inference endpoint.
Apache SINGA supports distributed deep learning through a system and library for model training.
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Truss FAQ
How do I develop a model with Truss?
Configure the model package in config.yaml, add inference code in model/model.py if needed, and deploy it with the Baseten CLI.
Can I use an existing model server?
Yes, servers based on vLLM, SGLang, Triton, or another container image can be deployed as a custom Docker server.
What is the difference between development and production deployments?
Development deployments are single-replica with live reload for iteration, while production deployments are immutable with full autoscaling.
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