---
title: "Groq vs Together AI: Inference Speed Comparison 2026"
description: "Compare Groq vs Together AI for fast LLM inference — pricing, throughput, latency benchmarks, and model availability for production AI applications."
keywords: "Groq vs Together AI, fast AI inference, LPU inference, AI inference speed, cheap fast LLM API"
date: "2026-05-07"
slug: "groq-vs-together-ai"
---

# Groq vs Together AI: Inference Speed Comparison 2026

When you need fast AI inference, Groq and Together AI are two of the most popular dedicated inference providers. Both offer speed advantages over traditional API providers, but they differ significantly in architecture, pricing, and model selection. This comparison helps you choose the right inference platform.

## Platform Overview

| Feature | Groq | Together AI |
|---------|------|-------------|
| Hardware | Custom LPU (Language Processing Unit) | NVIDIA GPUs (A100, H100) |
| Architecture | LPU for sequential text | Standard GPU cluster |
| Speed Focus | Ultra-low latency | High throughput |
| Model Selection | Curated (20+ models) | Extensive (200+ models) |
| Fine-tuning | Limited | Full fine-tuning support |
| Open Source Models | Yes | Yes |
| Enterprise | Available | Available |
| Free Tier | Yes (rate limited) | $5 free credit |

## Pricing Comparison

### Popular Model Pricing

| Model | Groq Input | Groq Output | Together Input | Together Output |
|-------|-----------|-------------|---------------|----------------|
| Llama 3.3 70B | $0.54/M | $0.54/M | $0.88/M | $0.88/M |
| Llama 3.1 8B | $0.05/M | $0.05/M | $0.18/M | $0.18/M |
| Mixtral 8x7B | $0.24/M | $0.24/M | $0.52/M | $0.52/M |
| Qwen 2.5 72B | N/A | N/A | $0.88/M | $0.88/M |
| DeepSeek V3 | N/A | N/A | $0.94/M | $0.94/M |
| Llama4 Maverick | ~$0.30/M | ~$0.30/M | ~$0.28/M | ~$0.28/M |

Groq is cheaper for models it supports, but Together AI has a much wider model selection.

## Latency Comparison

Latency is where Groq truly shines. Its custom LPU hardware delivers exceptional time-to-first-token (TTFT) and tokens-per-second (TPS):

| Model | Groq TTFT | Together TTFT | Groq TPS | Together TPS |
|-------|-----------|---------------|----------|-------------|
| Llama 3.3 70B | ~0.1s | ~0.4s | ~300 TPS | ~80 TPS |
| Llama 3.1 8B | ~0.05s | ~0.2s | ~800 TPS | ~200 TPS |
| Mixtral 8x7B | ~0.08s | ~0.3s | ~400 TPS | ~100 TPS |

Groq's LPU delivers **3-4x faster TTFT** and **3-4x higher throughput** compared to Together AI's GPU-based serving.

## Throughput Comparison

For high-volume applications, throughput (requests per second) matters more than single-request latency:

| Scenario | Groq | Together AI | Winner |
|----------|------|-------------|--------|
| Single request latency | ~0.1s TTFT | ~0.4s TTFT | Groq (4x faster) |
| Streaming experience | Near instant | Fast | Groq |
| Concurrent requests | Limited by LPU count | Scales with GPU count | Together AI |
| Batch processing | Not optimal | Good batch support | Together AI |
| Fine-tuned models | Limited | Full support | Together AI |

## When Groq Wins

### Real-Time Chat Applications

For chatbots and conversational AI where users perceive latency:

- **Groq**: 0.1s TTFT means responses start appearing almost instantly
- **Together AI**: 0.4s TTFT is still fast but noticeably slower

### Interactive Code Completion

For code autocomplete where every millisecond matters:

- **Groq**: Sub-100ms first token is ideal for inline suggestions
- **Together AI**: 200-400ms is acceptable but noticeable

### Streaming Applications

Any application that streams tokens to users benefits from Groq's exceptional TPS:

- **Groq**: 300+ TPS for 70B models means complete responses arrive fast
- **Together AI**: 80 TPS is adequate but users wait longer for completions

## When Together AI Wins

### Model Variety

Together AI supports 200+ models including many that Groq doesn't offer:

- DeepSeek V3, DeepSeek R1
- Qwen 2.5 family (all sizes)
- Fine-tuned models (your own or community)
- Embedding models
- Image generation models

### Fine-Tuning

Together AI offers full fine-tuning support, allowing you to customize models for your specific use case. Groq currently doesn't support fine-tuning.

### Scalability

Together AI's GPU infrastructure can scale more flexibly for concurrent requests. Groq's LPU capacity is more fixed.

### Cost for Less Speed-Critical Workloads

If you don't need ultra-low latency, Together AI's pricing is competitive and its model selection is superior.

## Cost Comparison: Real Workloads

### Scenario 1: Real-Time Chatbot (1000 requests/day)

Average 1K input + 500 output tokens per request:

| Provider | Cost/Day | Cost/Month | User Experience |
|----------|---------|-----------|----------------|
| Groq (Llama 3.3 70B) | $0.81 | $24.30 | Near-instant responses |
| Together AI (Llama 3.3 70B) | $1.32 | $39.60 | Fast responses |

### Scenario 2: Bulk Document Processing (10M tokens/day)

| Provider | Cost/Day | Cost/Month | Speed |
|----------|---------|-----------|-------|
| Groq | $10.80 | $324 | Very fast but limited concurrency |
| Together AI | $17.60 | $528 | Fast with better concurrency |

### Scenario 3: Using DeepSeek V3 (5M tokens/day)

| Provider | Available? | Cost/Day | Cost/Month |
|----------|-----------|---------|-----------|
| Groq | No | N/A | N/A |
| Together AI | Yes | $9.40 | $282 |

## API Quality Comparison

| Feature | Groq | Together AI |
|---------|------|-------------|
| OpenAI-compatible API | Yes | Yes |
| Streaming | Yes (SSE) | Yes (SSE) |
| Function calling | Yes | Yes |
| JSON mode | Yes | Yes |
| Rate limits | Stricter | More generous |
| Uptime SLA | 99.9% | 99.9% |
| Documentation | Good | Excellent |
| Community | Active | Very active |

## Conclusion

Choose **Groq** when latency is critical — real-time chat, code completion, or any application where users directly experience response speed. Groq's custom LPU hardware delivers unmatched performance for supported models.

Choose **Together AI** when you need model variety, fine-tuning, or scalable concurrency. It offers a more complete platform with 200+ models and the flexibility to fine-tune for your use case.

**Best of both worlds**: Use Groq for latency-critical paths and Together AI for everything else. Since both use OpenAI-compatible APIs, switching between them requires minimal code changes.