Groq vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Groq
🔴DeveloperAI Model Hosting & Inference
AI inference cloud built on Groq's own LPU (Language Processing Unit) chips that serves open-weight LLMs, Whisper, and vision models at the lowest latency in the market, with an OpenAI-compatible API.
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CustomTogether AI
🔴DeveloperAI Model Hosting & Inference
AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
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Starting Price
$0.02/1M tokensFeature Comparison
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Groq - Pros & Cons
Pros
- ✓Custom LPU silicon delivers tokens-per-second that is typically 5–10x faster than GPU baselines on open LLMs
- ✓OpenAI-compatible API plus a generous free developer tier make adoption a base-URL change away
- ✓Per-token pricing on Llama-class models is at or below the open-model market while latency stays predictably low
Cons
- ✗Model catalog is curated, not exhaustive — niche fine-tunes are easier to find on Together or Fireworks
- ✗No first-party fine-tuning service today, so custom models must be trained elsewhere and may not port to LPU
- ✗Capacity for popular models can be rate-limited during demand spikes; dedicated/Enterprise mitigates but adds cost
Together AI - Pros & Cons
Pros
- ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
- ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
- ✓Transparent per-token pricing — easy to model unit economics against closed providers
- ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference
Cons
- ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
- ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
- ✗Many model variants means model selection itself becomes a project
- ✗Dedicated endpoint cost calculations require attention to GPU type and utilization
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