Modal vs vLLM
A side-by-side comparison of Modal and vLLM, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Modal | vLLM |
|---|---|---|
| Category | Inference | Inference |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Proprietary | Open source |
| Deployment (differs) | Cloud | Self-host |
| Platforms (differs) | API, CLI | Linux, CLI, API |
| Model support (differs) | Model-agnostic | Multi-model |
| Vendor (differs) | Modal Labs | vLLM Project |
The honest brief
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
vLLM
PagedAttention pages the KV cache like OS virtual memory — the throughput trick that made it the OSS serving default.
- Serves most Hugging Face transformer models
- High throughput via continuous batching
- Apache-2.0, fully self-hostable
- OpenAI-compatible server
- Huge contributor community
- You manage the GPU infrastructure
- Setup/tuning learning curve
- Less turnkey than hosted APIs
- Optimized mainly for NVIDIA GPUs