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Baseten

ML model deployment platform — deploy any model as a production API in minutes

Inference & HostingFreemium

Baseten is a developer-focused platform for deploying machine learning models as production-grade REST APIs. It supports any model — LLMs, diffusion models, custom PyTorch/TensorFlow models — with auto-scaling, GPU selection (A100, H100, T4), and Python-based serving (Truss framework). Used by teams who need custom model deployment without building MLOps infrastructure.

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Key specs
50,000 Models deployed source
as of 2026-03-27

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What we know

PricingFreemiumfrom our original listing, not yet re-checked

Why it matters

  • Deploy any ML model (not just LLMs) as a production API without writing Kubernetes or Docker infrastructure.
  • Truss open-source framework ensures model packaging is reproducible and portable — not locked into Baseten's platform.
  • GPU selection from T4 ($0.59/hr) to H100 ($5.89/hr) lets teams right-size infrastructure for model requirements.
  • Auto-scaling handles traffic spikes without manual intervention; scales to zero when idle to minimize costs.

Key capabilities

  • Universal model deployment: LLMs (Llama, Mistral, Falcon), diffusion models (SDXL, Kandinsky), audio (Whisper), and custom PyTorch/TensorFlow.
  • Truss framework: Open-source packaging standard for reproducible model serving (github.com/basetenlabs/truss).
  • GPU selection: T4, A10G, A100 40GB/80GB, H100 for different performance and cost profiles.
  • Auto-scaling: Scale based on request queue depth; scale-to-zero for cost efficiency.
  • Production endpoints: HTTPS REST API with authentication, monitoring, and logging.
  • Model library: Pre-built Truss packages for Llama, Mistral, SDXL, Whisper, and 50+ popular models.
  • Streaming responses: SSE streaming for LLM token-by-token output.
  • Private networking: VPC peering and private endpoints for enterprise deployments.

Technical notes

  • Framework: Truss (open source; Python-based model packaging)
  • GPUs: NVIDIA T4, A10G, A100 (40/80GB), H100
  • Languages: Python (primary); REST API for any language
  • Model formats: PyTorch, TensorFlow, ONNX, Hugging Face models
  • Scaling: Autoscaling with configurable min/max replicas; scale-to-zero
  • Pricing: Pay-as-you-go GPU hours; T4 ~$0.59/hr, A100 ~$3.20/hr, H100 ~$5.89/hr
  • Company: Baseten; San Francisco; founded 2019; raised $40M (Greylock, Spark Capital)

Ideal for

  • ML teams deploying custom fine-tuned models or proprietary architectures that aren't supported by managed services.
  • Organizations who need GPU inference APIs for diffusion models, audio models, or multimodal models alongside LLMs.
  • Teams who want reproducible model packaging (Truss) without being tied to a single cloud provider.

Not ideal for

  • Simple LLM chat API needs — OpenAI API or Anthropic API directly is simpler and cheaper.
  • Teams who want managed fine-tuning — Predibase or Together AI include training pipelines.
  • Very high-throughput LLM serving — vLLM or SGLang on raw GPU infrastructure offers better performance tuning.

See also

  • Modal — Python-native serverless compute; more flexible for arbitrary ML workloads.
  • RunPod — Raw GPU cloud; cheaper for large-scale or custom deployments.
  • fal.ai — Faster cold starts and specialized for diffusion model inference.

FAQ

What is Baseten?

Baseten is a model deployment platform. You package your ML model (LLM, diffusion model, audio model, or custom PyTorch/TF model) using their open-source Truss framework, push it to Baseten, and get a scalable REST API endpoint. Baseten handles GPU provisioning, auto-scaling, container management, and monitoring. It supports Llama, Mistral, Stable Diffusion, Whisper, and any custom model.

Is Baseten free?

Baseten has a free tier with limited GPU compute credits for development. Production workloads require a paid plan — pricing is usage-based on GPU hours (T4, A10G, A100, H100). Teams pay only for compute used, with no minimum commitment on the pay-as-you-go tier.

What is Truss?

Truss is Baseten's open-source framework for packaging ML models. You define your model in a `model.py` file with `load()` and `predict()` methods, configure hardware requirements (GPU type, memory) in `config.yaml`, and Truss handles containerization. It's like a Dockerfile for ML models — portable and reproducible. GitHub: github.com/basetenlabs/truss.

How does Baseten compare to Modal?

Both deploy ML models on GPUs without managing infrastructure. Modal is more Python-native — you decorate functions with `@app.function(gpu='A100')` and deploy Python code directly. Baseten uses the Truss packaging framework, which is more structured and reproducible. Baseten has better pre-built model pipelines for common models (Llama, SDXL). Modal is more flexible for arbitrary Python workloads.

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Listed 2026-02-28 · updated 2026-11-12