Baseten
by Baseten · www.baseten.co ↗
Platform for deploying and serving machine-learning models as scalable APIs.
model-deployment inference infrastructure
- Category
- Platforms & Infrastructure
- Business model
- Paid
- Availability
- Global
- Launched
- 2019
- Record updated
- 2026-07-29
- Documentation
- docs.baseten.co ↗
- Founded
- 2019
- Headquarters
- US
- Canonical URL
- https://globalaiproductindex.com/products/baseten/
Overview
Baseten is a platform for deploying and serving machine-learning models as scalable APIs, with a focus on production inference. It uses the open-source Truss tool for packaging models and offers autoscaling, monitoring, and dedicated deployments. Pricing is usage-based by compute.
Key features
- Production model serving with autoscaling
- Truss open-source model packaging
- Dedicated and multi-cloud deployments
- Monitoring and observability
Use cases
- Serving custom and open models in production
- Low-latency inference APIs
- Scaling ML services reliably
Pricing
Pricing is usage-based by compute time, with free starter credits and dedicated-capacity options.
Frequently asked questions
What does Baseten do?
Baseten deploys and serves machine-learning models as scalable, production-grade APIs.
What is Truss?
Truss is Baseten's open-source tool for packaging machine-learning models for deployment.
How is Baseten priced?
Baseten is usage-priced by compute time, with free starter credits.
Similar products
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- Azure AI Foundry — Microsoft's platform for building, evaluating, and deploying AI applications and agents on Azure.
- Cerebras — AI compute company whose inference cloud serves open language models on wafer-scale hardware.
- Civitai — Community platform for sharing and discovering open image-generation models, LoRAs, and generated art.
- Dify — Open-source platform for building LLM applications with visual agent workflows, RAG pipelines, and model management.
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Sources
This record was last reviewed on 2026-07-29.
Machine-readable record: /api/products/baseten.json · Spot an error? Suggest a correction