Hyperbolic vs Together AI
A side-by-side comparison of Hyperbolic and Together AI, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Hyperbolic | Together AI |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | API, Web | API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Hyperbolic | Together |
| Capabilities (differs) |
|
|
The honest brief
Hyperbolic
Runs partly as a GPU marketplace renting idle H100/H200s, which is how its open-model inference undercuts centralized clouds.
- Serverless inference + GPU marketplace
- On-demand H100/H200 GPU rentals
- OpenAI-compatible API
- Open models: Llama, Qwen, DeepSeek, FLUX
- Marketplace supply reliability varies
- Open-weights only, no frontier closed models
- Smaller/newer than AWS-scale clouds
- Less enterprise tooling
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, GPU compute, and Multi-model access.
Pick Hyperbolic if you need LLM gateway / routing.
- LLM gateway / routing (secondary capability)
Pick Together AI if you need Fine-tuning / training.
- Fine-tuning / training (primary capability)
Hyperbolic leans on Multi-model access as a headline capability; Together AI treats it as secondary.
- Multi-model access (primary capability)
They also differ on:
- Platforms
- API, Web · API