Modal vs Replicate
A side-by-side comparison of Modal and Replicate, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Modal | Replicate |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | API, CLI | Web, API, CLI |
| Model support (differs) | Model-agnostic | Multi-model |
| Vendor (differs) | Modal Labs | Replicate |
| Capabilities (differs) |
|
|
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
Replicate
Any model is a Cog container behind one API billed per second — the low-commitment way to ship a model you didn't train.
- Image, video, audio, and language models
- No idle cost, no infra to manage
- Cog packaging for custom deploys
- Fine-tuning supported
- Cold starts on less-popular models
- Per-second cost adds up at scale
- Less control than raw GPU rental
When to pick which
Both cover Model inference / serving, Fine-tuning / training, and App / agent deployment.
Pick Modal if you need Sandboxed code execution and GPU compute.
- Sandboxed code execution (secondary capability)
- GPU compute (primary capability)
Pick Replicate if you need Multi-model access.
- Multi-model access (primary capability)
They also differ on:
- Platforms
- API, CLI · Web, API, CLI