Beam vs Modal
A side-by-side comparison of Beam and Modal, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Beam | Modal |
|---|---|---|
| Category (differs) | Infra | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | CLI, API, Linux | API, CLI |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Beam | Modal Labs |
| Capabilities (differs) |
|
|
The honest brief
Beam
Deploy GPU endpoints, sandboxes, and queues from a few lines of Python — open-core runtime (beta9) you can self-host.
- Define GPU workloads in pure Python
- Open-source runtime (beta9)
- Fast cold starts and autoscaling
- Free dev tier with monthly credit
- Smaller ecosystem than hyperscalers
- Python-centric; less polyglot
- Newer platform, maturing tooling
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 Sandboxed code execution, GPU compute, Model inference / serving, and App / agent deployment.
Pick Modal if you need Fine-tuning / training.
- Fine-tuning / training (secondary capability)
They also differ on:
- Platforms
- CLI, API, Linux · API, CLI