Groq vs Together AI: which open-weight inference platform wins in 2026?
Groq wins on inference speed via its LPU architecture. Together AI wins on model catalog breadth and fine-tune-serving. Pick Groq for latency-critical, Together for breadth.
At a glance
| Dimension | Groq | Together AI |
|---|---|---|
| Inference speed | Industry-leading via LPU hardwareWIN | Fast, GPU-based |
| Open-weight model catalog | Curated | Best in the categoryWIN |
| Fine-tune + serve workflow | Limited | First classWIN |
| Free tier | GenerousWIN | Decent |
| Pricing per 1M tokens | Competitive at low-medium scale | Aggressive at scale |
| Custom model deployment | Curated only | Yes — bring your own weightsWIN |
| Best for | Latency-critical realtime apps | Multi-model serving at scale |
Verdict
Groq is the right pick for realtime apps where end-to-end latency under 800ms is non-negotiable — its LPU architecture is materially faster than GPU alternatives. Together AI is the right pick for broader open-weight serving, fine-tune-and-serve workflows, and teams that want to host their own weights. Many production stacks use both: Groq for realtime conversation, Together for batch inference and custom models.
When to pick which
Pick Groq
Realtime voice agents, latency-critical inference, fastest open-weight serving.
Pick Together AI
Broad open-weight catalog, fine-tune + serve, custom model deployment, batch scale.
FAQ
Is Groq really faster than other inference platforms?
Yes — Groq's LPU architecture is materially faster than GPU-based competitors for LLM inference in 2026.
Best for fine-tuned open-weight models?
Together AI — strongest fine-tune + serve story with custom-weight deployment.
Cheapest at scale?
Together AI tends to be cheaper at multi-billion-token scale; Groq is competitive at low-medium scale plus free tier.
Last updated: 2026-06-01.