Buying snapshot
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| Decision point | What Baseten states |
|---|---|
Deployment | Baseten Cloud is fully managed, with single-tenant clusters for extra workload isolation. Self-hosted runs in your own VPCs, optionally hybrid with on-demand flex capacity on Baseten Cloud. |
What you can run | Start with an off-the-shelf model from Baseten's model library, or deploy any open-source or custom model you package with Truss. |
Packaging and licence | Models are packaged with Truss, Baseten's MIT-licensed CLI for deploying and serving models, installed with pip install --upgrade truss. |
Hardware choice | You choose which GPUs your models use and can ask Baseten for additional GPU types. |
Regions | Compute in other countries and regions goes through sales. |
Our take
Baseten fits a product team that wants to put its own or a fine-tuned open model behind a production API without building the serving layer: for engine-based models, Truss turns a config file into a deployed endpoint, and the Basic plan starts at $0 with usage billing. If you only need a hosted open model, its per-token Model APIs cover that without a dedicated deployment.
Evaluate Baseten
Where it stands out
Bring a model from any framework. Truss serves models built with vLLM, SGLang, TensorRT-LLM, transformers, diffusers, PyTorch, TensorFlow and more, so you are not limited to a model catalogue.
Config-only deploy in the quickstart. Baseten's Truss quickstart deploys Qwen 2.5 3B Instruct from a config.yaml naming the model, hardware and engine;
truss pushbuilds a TensorRT-optimized container with no Python code or Dockerfile.OpenAI-compatible endpoints. Engine-based deployments serve an OpenAI-compatible API, so code that works with the OpenAI SDK works by pointing
base_urlat the model endpoint.Training and inference on one stack. Baseten says you can train models with the Loops SDK and deploy them to production inference on the same stack.
Engineering help on hand. Baseten offers forward deployed engineers who work with you from prototype to production.
Product screenshot
Trade-offs to weigh
Volume prices need a sales conversation. Discounts on compute are negotiated as part of Pro and Enterprise, so you learn your price at scale only after you ask for a quote.
Deploying and scaling time is billed. Billed time includes the time a model is deploying and scaling up or down, not only predictions, so frequent redeploys and scaling events add compute time. You control how the model scales.
Your own cloud means Enterprise. Self-host deployments, hybrid deployment and use of existing cloud commitments sit on the Enterprise plan, which is quote-based.
No live-reload dev mode for TRT-LLM. Development mode with live reload (
--watch) does not work for TRT-LLM deployments; those push as published deployments.Performance figures are Baseten's own. Baseten states 99.99% uptime out of the box and says its Embeddings Inference has over 2x higher throughput than any other solution. Test your own model before relying on these figures.
Pricing and plan conditions
Basic is pay as you go. You pay only for the time your model uses compute on Baseten, and you do not pay for idle time. New accounts come with credits to try the UI and experiment with deployments for free. Model APIs are priced per 1M tokens; dedicated deployments are priced per instance, and you pay down to the minute. Training uses GPU instances listed at the same prices as dedicated deployments.
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| Plan | Published price | What it adds |
|---|---|---|
Basic | $0 per month, pay as you go | Dedicated deployments, Model APIs, Training, fast cold starts, SOC 2 Type II and HIPAA compliance, and email and in-app chat support. Deploys on Baseten. |
Pro | Volume discounts available; get a quote | Everything in Basic, plus priority access to high-demand GPUs, dedicated compute, higher Model API rate limits, hands-on engineering expertise, and dedicated support on Slack and Zoom. Deploys on Baseten. |
Enterprise | Volume discounts available; get a quote | Everything in Pro, plus custom SLAs, self-host deployments, on-demand flex compute, use of existing cloud commitments, full control over data residency, advanced security and compliance, custom global regions, and advanced RBAC with Teams. Deploys on Baseten, your VPC or hybrid. |
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| Model | Input | Cache input | Output |
|---|---|---|---|
GLM-5.3 | $1.40 | $0.14 | $4.40 |
GLM-5.3-Flash | $0.15 | $0.03 | $0.50 |
DeepSeek V4 Pro 0813 | $1.32 | $0.132 | $3.96 |
Kimi K3 | $3.00 | $0.30 | $15.00 |
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| Instance | Memory | Price |
|---|---|---|
T4 GPU | 16 GiB VM | $0.01052 |
L4 GPU | 24 GiB VRAM | $0.01414 |
A10G GPU | 24 GiB VM | $0.02012 |
A100 GPU | 80 GiB VRAM | $0.06667 |
H100 MIG GPU | 40 GiB VRAM | $0.0625 |
H100 GPU | 80 GiB VRAM | $0.10833 |
B200 GPU | 180 GiB VRAM | $0.16633 |
1x2 CPU | 1 vCPU, 2 GiB RAM | $0.00058 |
4x16 CPU | 4 vCPUs, 16 GiB RAM | $0.00346 |
16x64 CPU | 16 vCPUs, 64 GiB RAM | $0.01382 |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
95 GitHub commits 90d1.2k GitHub stars0 vulnerabilities across 1 package
See all signals from 4 sources
PyPI · truss@0.18.38
Alternatives to Baseten
The reviewed substitutes for Baseten among the model hosting platforms, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Fireworks AI
- Both are managed inference platforms for open and custom models. A team sends a workload to one of them.
- Together AI
- Both host open-weight models as a managed service billed by usage. They are bought for the same job.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- TensorRT-LLM
- Both answer 'how do I serve this model in production', but one is a managed platform and the other a library you run on your own GPUs. They compete only once the team has decided whether it will own accelerators.Applies to: Serving an open or fine-tuned model in production
- Modal
- Both run model workloads on managed GPUs, but Modal is a general serverless compute platform where you write the serving code and Baseten optimises the runtime for you. They compete when the workload is plain model serving and diverge when it is not.Applies to: Managed GPU inference for a self-supplied model