Replicate vs Together AI
A side-by-side comparison of Replicate and Together AI, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Replicate | Together AI |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | Web, API, CLI | API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Replicate | Together |
| Capabilities (differs) |
|
|
The honest brief
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
Together AI
One stop for the open-model stack: hundreds of open-weights models served plus both LoRA and full fine-tuning.
- LoRA and full fine-tuning
- Competitive inference-at-scale pricing
- OpenAI-compatible API
- Dedicated endpoints + GPU clusters
- Open models only, no frontier closed models
- Less specialized than single-model hosts
- Throughput varies by model demand
When to pick which
Both cover Model inference / serving, Fine-tuning / training, and Multi-model access.
Pick Replicate if you need App / agent deployment.
- App / agent deployment (secondary capability)
Pick Together AI if you need GPU compute.
- GPU compute (secondary capability)
They share capabilities, but each leads with different ones as a headline job:
Replicate is built around Multi-model access.
- Multi-model access (primary capability)
Together AI is built around Fine-tuning / training.
- Fine-tuning / training (primary capability)
They also differ on:
- Platforms
- Web, API, CLI · API