Groq wins on speed and per-token cost with its custom LPU hardware, making it ideal for latency-critical inference workloads. Together AI wins on platform completeness with fine-tuning, dedicated endpoints, and the broadest model catalog, making it the better choice for teams needing a full AI development lifecycle.
| Feature | Groq | Together AI |
|---|---|---|
| Best For | Ultra-low latency inference on popular open-source models at competitive prices | Full-lifecycle AI platform with inference, fine-tuning, and dedicated GPU endpoints |
| Hardware | Custom LPU (Language Processing Unit) chips purpose-built for sequential token generation | NVIDIA A100/H100 GPUs with optimized inference serving |
| Pricing Model | Groq uses pay-per-token pricing for LLM inference on custom LPU hardware. Llama 3.1 8B: $0.05/$0.08 per 1M input/output tokens. Llama 3.3 70B: $0.59/$0.79 per 1M tokens. Llama 4 Scout: $0.11/$0.34 per 1M tokens. Qwen3 32B: $0.29/$0.59 per 1M tokens. Whisper v3: $0.04-$0.111/hour. Batch API: 50% discount. Prompt caching: 50% savings on cached input. Built-in tools: Basic Search $5/1K requests, Advanced Search $8/1K requests. | Serverless inference: from $0.10/M tokens (small models) to $2.50/M tokens (large models). Dedicated endpoints: from $0.80/GPU/hour (A100). Fine-tuning: from $3/M tokens. Free tier: $5 in credits. Pay-as-you-go with no minimum. |
| Model Catalog | Focused selection: Llama, Mixtral, Qwen, Gemma, Whisper | 100+ open-source models across multiple architectures; broadest selection |
| Fine-Tuning | Not available; inference-only platform | LoRA and full fine-tuning from $3/M tokens on supported architectures |
| Latency | Industry-leading; time-to-first-token often under 100ms, 500+ tokens/sec on 70B models | Competitive with GPU providers; typically 200-500ms TTFT |
| Feature | Groq | Together AI |
|---|---|---|
| Inference Capabilities | ||
| Hardware Architecture | Custom LPU chips designed for sequential token generation | NVIDIA A100/H100 GPUs with standard inference optimization |
| Inference Latency | Industry-leading; TTFT often under 100ms | Competitive with GPU providers; typically 200-500ms TTFT |
| Model Catalog Breadth | Focused: Llama, Mixtral, Qwen, Gemma, Whisper | Broad: 100+ open-source models across multiple architectures |
| Batch Processing | Batch API with 50% discount on standard pricing | Batch endpoints available for high-throughput workloads |
| Prompt Caching | 50% savings on cached input tokens | Context caching available for repeated prompts |
| Training & Customization | ||
| Fine-Tuning | ❌ | LoRA and full fine-tuning from $3/M tokens |
| Dedicated Endpoints | Not available; shared LPU infrastructure only | From $0.80/GPU/hour on A100 GPUs with reserved capacity |
| Custom Model Deployment | Limited to models supported on LPU hardware | Deploy custom fine-tuned models on dedicated endpoints |
| API & Integration | ||
| API Compatibility | OpenAI-compatible REST API | OpenAI-compatible API with additional fine-tuning endpoints |
| Built-in Tools | Search tools at $5-$8 per 1K requests | Function calling support on compatible models |
| Audio/Speech Support | Whisper v3 at $0.04-$0.111/hour | Speech models available through model catalog |
| Pricing & Access | ||
| Free Tier | No free tier; pay-as-you-go from first token | $5 free credits for new accounts |
| Minimum Commitment | None; pure pay-as-you-go pricing | None; pay-as-you-go with no minimum |
Hardware Architecture
Inference Latency
Model Catalog Breadth
Batch Processing
Prompt Caching
Fine-Tuning
Dedicated Endpoints
Custom Model Deployment
API Compatibility
Built-in Tools
Audio/Speech Support
Free Tier
Minimum Commitment
Groq wins on speed and per-token cost with its custom LPU hardware, making it ideal for latency-critical inference workloads. Together AI wins on platform completeness with fine-tuning, dedicated endpoints, and the broadest model catalog, making it the better choice for teams needing a full AI development lifecycle.
Choose Groq if:
Choose Groq for latency-critical applications, real-time conversational AI, coding assistants, and high-volume inference where speed and per-token cost are the primary decision factors.
Choose Together AI if:
Choose Together AI when you need fine-tuning on proprietary data, dedicated GPU endpoints with guaranteed capacity, or access to the broadest catalog of open-source models for experimentation and production.
Choose Groq if:
Choose Groq for batch processing workloads where the 50% Batch API discount makes it significantly cheaper than alternatives for offline token processing at scale.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes, significantly. Groq's custom LPU hardware is purpose-built for sequential token generation and consistently delivers 3-10x faster inference speeds compared to GPU-based providers including Together AI. Time-to-first-token on Groq is often under 100 milliseconds, while GPU-based services typically range from 200-500 milliseconds.
No. Groq is an inference-only platform and does not offer fine-tuning capabilities. If you need to customize model weights, Together AI (from $3/M tokens) or other training platforms are your options. You can fine-tune a model elsewhere and run inference on Groq if the model is among supported architectures.
For pure inference without fine-tuning, Groq is generally 30-50% cheaper per token than Together AI on comparable models. Llama 3.1 8B on Groq costs $0.05/$0.08 per 1M tokens versus approximately $0.10/$0.10 on Together AI. The Batch API discount of 50% makes Groq even more cost-effective for offline workloads.
Yes. Together AI offers dedicated endpoints starting at $0.80/GPU/hour on A100 GPUs, providing reserved compute capacity with consistent latency for production applications with strict SLA requirements. Groq does not offer dedicated capacity.