AI Model Hosting & Inference Software Pricing 2026
Compare pricing for 4 ai model hosting & inference tools. Find the right software for your budget.
AI Model Hosting & Inference software pricing ranges from $0 to $6500 per user/month in 2026. The typical cost is around $217/user/month across 4 popular tools. Top picks: Baseten (Free–$6.5K/user/mo), BentoML (Free–$5K/user/mo), Cerebrium (Free–$100/user/mo), and 1 more. 3 of 4 tools offer free tiers for small teams or limited use.
All AI Model Hosting & Inference Tools
Compare all side-by-side →Baseten
Free–$6.5K/monthBentoML
Free–$5K/monthCerebrium
Free–$100/monthBanana.dev
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AI Model Hosting & Inference Pricing FAQ
01 What are AI model hosting platforms?
AI model hosting platforms let you deploy trained ML models as API endpoints without managing GPU infrastructure. They handle scaling, load balancing, and GPU allocation so you can focus on your models.
02 How much does AI model hosting cost?
Pricing is typically usage-based — pay per GPU-second or per request. Serverless options start at $0.0001/second. Dedicated GPU instances range from $0.50-$4/hour depending on GPU type.
03 What's the cheapest way to deploy ML models?
For low traffic, serverless platforms (Replicate, Cerebrium) are cheapest — you only pay when models are running. For sustained traffic, dedicated instances on RunPod or Lambda are more cost-effective.
04 How do serverless GPU platforms work?
Serverless GPU platforms cold-start your model when a request arrives, run inference, and shut down after. You pay only for active inference time. Cold start latency (2-30 seconds) is the tradeoff.
05 Can I host open-source models like Llama or Stable Diffusion?
Yes. Most platforms support custom model deployment including Llama, Mistral, Stable Diffusion, and Whisper. BentoML and Baseten specialize in packaging any model for deployment.
06 What's the difference between model hosting and LLM API providers?
LLM API providers (OpenAI, Anthropic) host their own proprietary models. Model hosting platforms let you deploy YOUR models — whether open-source or custom-trained — on GPU infrastructure you control.