GroqFreemium
An inference provider whose custom LPU hardware delivers exceptionally low-latency responses for open-weight models.
AI Infrastructure comparison
Groq vs Together AI
Pricing, pros, cons, and ideal use cases — side by side.
Together AIPaid
A cloud platform for fast, cost-efficient inference and fine-tuning of open-weight models at production scale.
At a glance
| Groq | Together AI | |
|---|---|---|
| Pricing | FreemiumFree tier for evaluation. Usage-based paid tiers for production volume. | PaidUsage-based per-token pricing. Dedicated endpoints and fine-tuning are priced separately. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Latency-sensitive applications like voice agentsReal-time and interactive AI experiencesTeams running supported open models | Teams running open-weight models in productionCost-sensitive, high-volume inference workloadsEnterprises fine-tuning private model variants |
Pros & cons
Groq
Pros
- Exceptional inference speed and low latency
- OpenAI-compatible API, easy to adopt
- Strong fit for real-time use cases
- Competitive usage pricing
Cons
- Curated model selection, not every model
- Pure inference — no platform or governance layer
- Capacity can be constrained at peak demand
Together AI
Pros
- Fast, cost-efficient open-model inference
- Wide and current model selection
- Fine-tuning and dedicated endpoints available
- Often cheaper than closed APIs at scale
Cons
- Open models shift evaluation and safety onto you
- Quality varies by model and task
- Not a managed governance platform on its own
Which should you choose?
Groq is the lighter-weight option (Freemium), while Together AI sits higher on the pricing ladder (Paid). Groq is built around latency-sensitive applications like voice agents; Together AI leans more toward teams running open-weight models in production. Shortlist the one whose strengths line up with your biggest constraint.