Groq vs Together AI
Side-by-Side Comparison
How Groq, Together AI compare across the features that matter most.
| Feature | Groq | Together AI |
|---|---|---|
| Focus | Ultra-low-latency inference (LPU hardware) | Full-stack inference + fine-tuning platform |
| Model catalog | Curated 15-30 open-weight models | 200+ models incl. multimodal |
| Speed | Very fast time-to-first-token, high tokens/sec | GPU-based, strong but not LPU-fast |
| Fine-tuning | No | LoRA and full fine-tuning |
| Dedicated infra | Serverless | Dedicated H100/H200/B200 endpoints & clusters |
| Cost model | Per-token + batch/cache discounts | Per-token + dedicated GPU (hourly) |
| API | OpenAI-compatible | OpenAI-compatible |
| Best for | Voice agents, real-time chat, coding assistants | Model breadth, fine-tuning, multimodal, SLAs |
KLYROO Test
Our comparison philosophy is to evaluate tools with consistent tasks and realistic workflows across categories such as writing, coding, research, reasoning, image generation, summarization and data analysis. Where we have not run a controlled head-to-head, we label the assessment clearly: this comparison is an editorial evaluation based on documented features and available evidence. We never invent benchmark numbers.
Which One Should You Choose?
- Choose Groq when latency is the product requirement — voice agents, real-time chat, interactive coding.
- Choose Together AI for model breadth, fine-tuning, multimodal pipelines and dedicated production infrastructure.
- Both use OpenAI-compatible APIs, so switching is largely a base-URL swap.