Baseten vs Modal
A side-by-side comparison of Baseten and Modal, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Baseten | Modal |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | Web, API | API, CLI |
| Model support (differs) | Multi-model | Model-agnostic |
| Vendor (differs) | Baseten | Modal Labs |
| Capabilities (differs) |
|
|
The honest brief
Baseten
Pairs prebuilt Model APIs with dedicated Truss deployments and scale-to-zero, so you don't pay for idle GPUs.
- Prebuilt Model APIs for Llama, DeepSeek
- Dedicated GPU/CPU deploys for custom models
- Open-source Truss packaging format
- Production-grade observability and autoscaling
- Dedicated GPU rates run pricier than Modal
- Per-replica cost doubles for redundancy
- Engineering effort to package custom models
Modal
Define GPU infra in Python decorators with 2-4s cold starts — no YAML, Dockerfiles, or managed-stack lock-in.
- Python-decorator infra, no YAML/Dockerfiles
- Scale-to-zero, pay only when running
- Scales to hundreds of GPUs
- Free monthly starter credits
- SDK lock-in; migrating means rewriting
- No managed vLLM/TensorRT setup
- Costs climb under heavy usage
- Billing hard to predict
When to pick which
Both cover Model inference / serving, GPU compute, Fine-tuning / training, and App / agent deployment.
Pick Baseten if you need Multi-model access and Embeddings.
- Multi-model access (primary capability)
- Embeddings (secondary capability)
Pick Modal if you need Sandboxed code execution.
- Sandboxed code execution (secondary capability)
Modal leans on GPU compute as a headline capability; Baseten treats it as secondary.
- GPU compute (primary capability)
They also differ on:
- Platforms
- Web, API · API, CLI