Groq vs Together AI
A side-by-side comparison of Groq and Together AI, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Groq | 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) | Groq | Together |
| Capabilities (differs) |
|
|
The honest brief
Groq
Custom LPU silicon delivers deterministic sub-100ms TTFT, ideal for voice and latency-critical apps.
- Hundreds of tokens/sec on open models
- Sub-100ms time-to-first-token
- Deterministic, low-variance latency
- OpenAI-compatible API with free tier
- Curated open-weight models only
- No frontier closed models (GPT/Claude)
- SRAM limits large context windows
- Rate limits during peak demand
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 and Multi-model access.
Pick Groq if you need Transcription (STT) and Speech synthesis (TTS).
- Transcription (STT) (secondary capability)
- Speech synthesis (TTS) (secondary capability)
Pick Together AI if you need Fine-tuning / training and GPU compute.
- Fine-tuning / training (primary capability)
- GPU compute (secondary capability)
Groq 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