Capability · Comparison
Groq vs Together AI
If you want to serve open-weights models without running GPUs yourself, the two biggest inference-as-a-service providers are Groq and Together AI. Groq uses its proprietary LPU (Language Processing Unit) chips for ultra-low-latency inference — models run 3-10x faster than on GPUs for their supported catalogue. Together AI runs a broader model catalogue on standard GPU infrastructure with competitive pricing.
Side-by-side
| Criterion | Groq | Together AI |
|---|---|---|
| Hardware | Groq LPU (custom deterministic chip) | NVIDIA GPUs (H100 and successors) |
| Tokens / second (typical large model) | 500-1500 tok/s (e.g. Llama 3.3 70B) | 100-300 tok/s |
| Model catalogue | ~15-25 curated models | 100+ models, community + custom |
| Pricing (Llama 3.3 70B, as of 2026-04) | ~$0.59/M input, ~$0.79/M output | ~$0.60/M input, ~$0.90/M output |
| Fine-tuning service | Not offered | Yes — LoRA + full fine-tuning |
| Dedicated deployments | Enterprise only | Yes — dedicated endpoints at any tier |
| OpenAI-compatible API | Yes | Yes |
| Batch API | No | Yes — 50% discount for batch |
| Best fit | Latency-critical real-time UX on a short-list of models | Model breadth, fine-tuning, batch workloads |
Verdict
Groq is the right choice when latency dominates: voice agents, real-time typing-speed UX, any application where 'instant' matters more than model choice. Their LPU hardware genuinely delivers 3-10x the tokens/second of GPU providers on supported models, and the price is competitive. Together AI is the right choice when you need model breadth (hundreds of models), fine-tuning services, or batch economics. Many teams use both: Together for most traffic and fine-tuning, Groq for user-facing hot paths that need millisecond-tier latency.
When to choose each
Choose Groq if…
- Latency is a hard constraint (voice, real-time UI).
- Your model is in Groq's catalogue (Llama, Mixtral, Qwen, Whisper, etc.).
- You want dramatic tokens/sec improvements without optimizing infra.
- Your workload is mostly serving, not training / fine-tuning.
Choose Together AI if…
- You need model breadth — 100+ open-weights options.
- You want fine-tuning as a managed service.
- You have bulk batch workloads (50% discount matters).
- You want a single vendor for dev / fine-tune / deploy / serve.
Frequently asked questions
Is Groq's quality the same as running the same model on GPU?
Yes — Groq runs full-precision weights on LPU hardware, producing identical output to GPU inference for the same model and sampling settings (with small numeric differences). You're not trading quality for speed.
Why isn't GPT-5 or Claude available on Groq or Together?
Both are closed-weights and API-only from their vendors. Groq and Together host open-weights models (Llama, Mixtral, Qwen, DeepSeek, etc.). For Claude and GPT-5 you go direct to Anthropic and OpenAI (or AWS Bedrock / Azure OpenAI).
Does Groq support function calling?
Yes, for supported models — their API is OpenAI-compatible for function calling / tool use. Quality depends on the underlying model. Llama 3.3 70B on Groq handles tool calls well.
Sources
- Groq — Docs — accessed 2026-04-20
- Together AI — Docs — accessed 2026-04-20