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 Portkey
Pricing, pros, cons, and ideal use cases — side by side.
PortkeyFreemium
An AI gateway that adds routing, caching, observability, and guardrails to LLM traffic through a single control plane.
At a glance
| Baseten | Portkey | |
|---|---|---|
| Pricing | PaidUsage-based pricing tied to the compute your deployed models consume. | FreemiumFree developer tier. Paid Pro and Enterprise plans add volume, self-hosting, and governance. |
| 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 standardizing LLM access across teamsPlatform teams needing routing and failoverOrgs wanting cost and reliability controls in one place |
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
Portkey
Pros
- Routing, failover, and caching out of the box
- Built-in observability and cost analytics
- Guardrails enforced at the gateway
- Managed or self-hosted
Cons
- Another vendor in the critical request path
- Overlaps with observability tools you may already run
- Full governance features need a paid plan
Which should you choose?
Portkey is the lighter-weight option (Freemium), while Baseten sits higher on the pricing ladder (Paid). Baseten is built around teams deploying custom or fine-tuned models; Portkey leans more toward enterprises standardizing llm access across teams. Shortlist the one whose strengths line up with your biggest constraint.