Baseten
Input: open-source or custom ML models and training data. Actions: deploy models to managed cloud, self-host, or hybrid environments; serve inference with per-token and per-minute compute metering. Output: production model APIs, dedicated deployments, and billing by token and compute minutes. Workflow: choose deployment type, provision compute, and expose model APIs.
The pricing pages list: (1) a Basic deployment described as “Baseten $0 per month, pay as you go”; (2) model API unit prices per 1M tokens for named models (for example GLM-5.3 shows $4.40 per 1M tokens); and (3) per-minute and per-hour GPU instance prices for Dedicated Deployments and Training. Pro and Enterprise plans are offered but require contacting sales for quotes.Is Baseten right for you?
Good for
- Teams needing production inference with options to self-host or use a managed multi-cloud service for low-latency model serving.
- Documents a 99.99% uptime goal for its multi-cloud infrastructure.
- Supports self-hosted and hybrid deployments so teams can use their own VPCs and cloud commitments.
Keep in mind
- Pro and Enterprise plan prices are not listed; the site requires contacting sales for a quote.
- Buyers who require publicly listed enterprise contract prices or detailed SLA metrics beyond the platform’s summary uptime claim; confirm enterprise terms with sales.
Pricing and plan limits
The linked official pages show specific per-model token prices and per-minute GPU instance prices; Pro and Enterprise require a quote. The pages state new accounts receive credits to experiment. The linked official pages do not list fixed Pro or Enterprise prices.
More about capabilities and limits
Documented strengths
Documents a 99.99% uptime goal for its multi-cloud infrastructure.
Supports self-hosted and hybrid deployments so teams can use their own VPCs and cloud commitments.
Limitations to consider
Pro and Enterprise plan prices are not listed; the site requires contacting sales for a quote.
Choosing a plan
Validate expected token and GPU-minute consumption by running a representative inference workload on Baseten’s trial credits and request a written quote tying projected usage to Pro/Enterprise options.
Based on the linked product documentation.
Available on
Api.
Official sources
Alternatives to Baseten
Choose around the work you need to do.
BentoML
Use BentoML to package, deploy, and scale custom or open-source models with a flexible self-hostable inference stack that supports multi-GPU distributed inference and observability across environments.
