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 Together AI
Pricing, pros, cons, and ideal use cases — side by side.
Together AIPaid
A cloud platform for fast, cost-efficient inference and fine-tuning of open-weight models at production scale.
At a glance
| Baseten | Together AI | |
|---|---|---|
| Pricing | PaidUsage-based pricing tied to the compute your deployed models consume. | PaidUsage-based per-token pricing. Dedicated endpoints and fine-tuning are priced separately. |
| 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 | Teams running open-weight models in productionCost-sensitive, high-volume inference workloadsEnterprises fine-tuning private model variants |
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
Together AI
Pros
- Fast, cost-efficient open-model inference
- Wide and current model selection
- Fine-tuning and dedicated endpoints available
- Often cheaper than closed APIs at scale
Cons
- Open models shift evaluation and safety onto you
- Quality varies by model and task
- Not a managed governance platform on its own
Which should you choose?
Baseten is built around teams deploying custom or fine-tuned models; Together AI leans more toward teams running open-weight models in production. Shortlist the one whose strengths line up with your biggest constraint.