BasetenPaid
A platform for deploying and serving machine-learning models in production, with autoscaling, fast cold starts, and GPU infrastructure managed for you.
AI Infrastructure comparison
Baseten vs Google Vertex AI
Pricing, pros, cons, and ideal use cases — side by side.
Google Vertex AIPaid
Google Cloud's unified AI platform — access to Gemini and partner models, plus tools to build, deploy, and govern AI and agents.
At a glance
| Baseten | Google Vertex AI | |
|---|---|---|
| Pricing | PaidUsage-based pricing tied to the compute your deployed models consume. | PaidUsage-based pricing across model and platform services, billed through Google Cloud. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Teams deploying custom or fine-tuned modelsEnterprises needing dedicated, autoscaling model servingOrgs that want to avoid managing GPU infrastructure | Enterprises already on Google CloudTeams wanting Gemini under enterprise governanceOrganizations consolidating the AI lifecycle on one platform |
Pros & cons
Baseten
Pros
- Production model serving without managing GPUs
- Autoscaling with fast cold starts
- Works with open, fine-tuned, and custom models
- Removes most MLOps overhead
Cons
- Unnecessary if you only use hosted frontier APIs
- Compute-based cost grows with traffic
- Still requires model and evaluation expertise
Google Vertex AI
Pros
- Gemini plus a broad Model Garden
- End-to-end build, deploy, evaluate, and govern
- Inside Google Cloud IAM and compliance
- Strong data and analytics integration
Cons
- Large, complex platform surface area
- Most valuable only if you are already on GCP
- Getting value requires real platform investment
Which should you choose?
Baseten is built around teams deploying custom or fine-tuned models; Google Vertex AI leans more toward enterprises already on google cloud. Shortlist the one whose strengths line up with your biggest constraint.