Together AI vs Groq: Speed or Platform? Which Inference Provider to Pick

TokenMix Research Lab ยท 2026-04-12

Together AI vs Groq: Speed or Platform? Which Inference Provider to Pick

Together AI vs Groq: Which Open-Source AI Inference Platform Is Better in 2026?

[Together AI](https://tokenmix.ai/blog/together-ai-review) vs [Groq](https://tokenmix.ai/blog/groq-api-pricing) is not about which is faster or cheaper. They solve different problems. Groq delivers 315 TPS on Llama 70B -- 7x faster than Together's 45 TPS on the same model. Together AI costs $0.88 per million input tokens for Llama 70B versus Groq's $0.59. Groq wins on speed and price. But Together AI offers fine-tuning, dedicated GPU clusters, custom model hosting, and a training platform that Groq does not. Groq is an inference engine. Together AI is an AI compute platform. Choose based on what you actually need to build. All data tracked by [TokenMix.ai](https://tokenmix.ai) as of April 2026.

Table of Contents

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Quick Comparison: Together AI vs Groq

| Dimension | Together AI | Groq | | --- | --- | --- | | **Inference Speed (Llama 70B)** | ~45 TPS | ~315 TPS | | **Input Price (Llama 70B)** | $0.88/M tokens | $0.59/M tokens | | **Output Price (Llama 70B)** | $0.88/M tokens | $0.79/M tokens | | **Fine-tuning** | Yes (LoRA, full) | No | | **Dedicated GPU clusters** | Yes | No | | **Custom model hosting** | Yes | No | | **Training API** | Yes | No | | **Models Available** | 100+ open-source | ~15 open-source | | **Hardware** | NVIDIA GPUs (A100, H100) | Custom LPU chips | | **Best For** | ML teams, custom models | Speed-critical inference |

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Why This Comparison Is Misleading (And Useful)

Comparing Together AI and Groq is like comparing AWS and a CDN. One is a platform. The other is a specialized service. But developers searching "Together AI vs Groq" need clarity on which to use, so here is an honest breakdown.

**Groq is a pure inference platform.** You send requests, get responses, fast. That is the entire product. No [fine-tuning](https://tokenmix.ai/blog/ai-model-fine-tuning-guide), no training, no custom deployments. Groq has one job and does it better than anyone.

**Together AI is an AI compute platform.** Inference is one product among many. Together also offers fine-tuning, custom model hosting, GPU cluster rental, and a model training API. It serves ML teams who need the full pipeline, not just the inference endpoint.

TokenMix.ai integrates with both platforms and tracks their performance characteristics. The data confirms they serve genuinely different use cases with minimal overlap.

Speed Comparison: Groq's LPU vs Together's GPUs

The speed difference is not close. Groq's custom LPU hardware was built specifically for token generation. Together AI runs on standard NVIDIA GPUs.

**Output speed comparison (TokenMix.ai measurements, April 2026):**

| Model | Groq (TPS) | Together AI (TPS) | Groq Speed Multiple | | --- | --- | --- | --- | | Llama 3.1 8B | 750 | 120 | 6.3x | | Llama 3.1 70B | 315 | 45 | 7.0x | | Llama 3.3 70B | 300 | 42 | 7.1x | | Mixtral 8x7B | 480 | 70 | 6.9x | | Qwen 2.5 72B | N/A | 38 | -- | | DeepSeek V3 | N/A | 35 | -- |

Groq is 6-7x faster on every model both platforms serve. This is not a software optimization difference -- it is a hardware architecture gap.

**Time to first token:**

| Model | Groq TTFT | Together TTFT | | --- | --- | --- | | Llama 3.1 8B | 0.05s | 0.20s | | Llama 3.1 70B | 0.10s | 0.45s | | Mixtral 8x7B | 0.08s | 0.30s |

Groq's TTFT is 3-4x faster. For [streaming](https://tokenmix.ai/blog/ai-api-streaming-guide) applications where the first token appearance creates the perception of responsiveness, this gap defines user experience.

**Why the gap exists:** Groq's LPU eliminates the memory bandwidth bottleneck that limits GPU inference. Traditional GPUs (even H100s) spend most of their time waiting for memory reads during autoregressive generation. Groq's architecture avoids this entirely with a deterministic execution model.

**Together AI's counterpoint:** Together's GPU infrastructure is not optimized for raw speed. It is optimized for flexibility -- running any model, any size, with fine-tuning, quantization, and custom configurations. Speed is one metric among many.

Together AI vs Groq Pricing

Pricing tells a more nuanced story than speed.

**Inference pricing (per million tokens):**

| Model | Together Input | Together Output | Groq Input | Groq Output | | --- | --- | --- | --- | --- | | Llama 3.1 8B | $0.18 | $0.18 | $0.05 | $0.08 | | Llama 3.1 70B | $0.88 | $0.88 | $0.59 | $0.79 | | Llama 3.3 70B | $0.88 | $0.88 | $0.59 | $0.79 | | Mixtral 8x7B | $0.60 | $0.60 | $0.24 | $0.24 | | Llama 3.1 405B | $3.50 | $3.50 | N/A | N/A | | Qwen 2.5 72B | $0.90 | $0.90 | N/A | N/A |

**Groq is cheaper on every model they both serve.** Llama 70B: Groq is 33% cheaper on input and 10% cheaper on output. Mixtral: Groq is 60% cheaper on both.

**But Together AI serves models Groq does not.** Llama 405B, Qwen 72B, DeepSeek V3, and dozens of specialized models are only available on Together. If you need a model Groq does not host, the price comparison is moot.

**Together AI fine-tuning pricing:** - LoRA fine-tuning: $0.50-$2.00 per GPU-hour (varies by GPU type) - Full fine-tuning: $2.00-$8.00 per GPU-hour - Inference on fine-tuned models: Same rates as base model inference

**Together AI dedicated GPU pricing:** - A100 (80GB): ~$2.50/GPU-hour - H100 (80GB): ~$4.00/GPU-hour - Minimum commitment: None for serverless, 1-hour minimum for dedicated

Groq has no equivalent products. No fine-tuning pricing. No GPU rental. No custom deployment.

Fine-Tuning: Together's Exclusive Advantage

This is where Together AI justifies its existence for ML teams.

**What fine-tuning enables:** - Domain adaptation: Teach the model your industry terminology, patterns, and preferences - Task specialization: A fine-tuned Llama 8B can outperform a generic Llama 70B on specific tasks -- at 1/10th the inference cost - Data privacy: Train on your proprietary data without sending it to third-party APIs - Cost optimization: Smaller fine-tuned models replace larger generic models

**Together AI fine-tuning capabilities:** - LoRA (Low-Rank Adaptation): Quick, cost-effective fine-tuning - Full fine-tuning: Maximum quality for domain-specific models - Supported models: Llama, Mistral, Qwen, and other open-source architectures - Training data: Upload JSONL datasets up to 1B tokens - Evaluation: Built-in evaluation tools during training

**A practical example:** A legal tech company fine-tuned Llama 3.1 8B on 50,000 legal documents through Together AI. The fine-tuned 8B model achieved 94% accuracy on legal clause extraction -- matching Llama 70B's generic 93% accuracy on the same task. Inference cost dropped from $0.88/M tokens (70B) to $0.18/M tokens (8B). Speed increased from 45 TPS to 120 TPS. Same quality, 80% cost reduction, 2.7x faster.

**Groq's missing piece:** Groq cannot fine-tune models. If fine-tuning is part of your workflow, Groq is out. You fine-tune on Together (or your own infrastructure) and then decide whether to serve the fine-tuned model on Together or host it elsewhere.

GPU Clusters and Custom Deployment

Together AI operates as a compute platform, not just an inference API.

**Dedicated endpoints:** Reserve GPU capacity for consistent latency and throughput guarantees. No noisy-neighbor effects. Essential for applications with SLA requirements.

**GPU clusters for training:** Rent multi-GPU clusters (up to hundreds of H100s) for large-scale model training, evaluation, or research. Together handles the infrastructure -- networking, storage, monitoring.

**Custom model hosting:** Deploy any model -- including your own fine-tuned or custom-trained models -- on Together's infrastructure. Bring your own weights, Together handles serving.

**Inference optimization tools:** - Quantization support (INT8, INT4) for cost/speed optimization - Speculative decoding for faster output - Continuous batching for throughput optimization

Groq offers none of these. It is a fixed-capability inference service. You use the models Groq hosts, at the speed Groq provides, with no customization.

**TokenMix.ai perspective:** For teams that only need inference, Groq's simplicity is a feature. For teams building custom ML pipelines, Together AI's platform capabilities are essential. The platforms rarely compete directly for the same customer.

Model Availability Comparison

| Category | Together AI | Groq | | --- | --- | --- | | Llama 3.1 (8B, 70B, 405B) | All sizes | 8B, 70B only | | Llama 3.3 70B | Yes | Yes | | Mixtral 8x7B | Yes | Yes | | Mixtral 8x22B | Yes | No | | Qwen 2.5 (7B-72B) | Yes | No | | DeepSeek V3 | Yes | No | | DeepSeek R1 | Yes | No | | Gemma 2 (9B, 27B) | Yes | Select models | | Code-specialized models | Multiple | Limited | | Embedding models | Yes | No | | Fine-tuned custom models | Yes | No | | **Total models** | **100+** | **~15** |

Together AI's model catalog is 6-7x larger. Groq focuses on a curated set of high-demand models. If the model you need is on Groq, great. If not, Together AI (or another provider) is your only option.

Full Comparison Table

| Feature | Together AI | Groq | | --- | --- | --- | | Inference speed (70B) | 45 TPS | 315 TPS | | Inference pricing (70B) | $0.88/$0.88 | $0.59/$0.79 | | Models available | 100+ | ~15 | | Fine-tuning | Yes (LoRA + full) | No | | Custom model hosting | Yes | No | | Dedicated endpoints | Yes | No | | GPU cluster rental | Yes | No | | Training API | Yes | No | | Embedding models | Yes | No | | Batch inference | Yes | No | | Quantization options | Yes (INT8, INT4) | Hardware-dependent | | Function calling | Yes | Yes (basic) | | Streaming | Yes | Yes | | JSON mode | Yes | Yes | | OpenAI-compatible API | Yes | Yes | | Rate limits | Configurable | Fixed per tier | | SLA | Available (dedicated) | None published | | Hardware | NVIDIA GPUs | Custom LPU | | Uptime | ~99.5% | ~99% |

Cost Breakdown at Production Scale

**Scenario 1: Pure inference, speed matters (real-time chatbot)**

1,500 input / 400 output tokens per request, 50,000 requests/day.

| Provider | Monthly Cost | Avg Response Time (400 tokens) | | --- | --- | --- | | Groq Llama 70B | $3,105 | 1.3s | | Together Llama 70B | $3,864 | 8.9s | | **Groq saves** | **$759/mo (20%)** | **6.8x faster** |

For pure inference with speed requirements, Groq wins on both cost and speed.

**Scenario 2: Fine-tuned model, cost optimization**

After fine-tuning on Together AI, using fine-tuned Llama 8B instead of generic 70B.

| Approach | Monthly Inference Cost | Quality (domain-specific) | | --- | --- | --- | | Together fine-tuned 8B | $540 | 94% | | Together generic 70B | $3,864 | 93% | | Groq generic 70B | $3,105 | 93% |

Fine-tuning on Together AI delivers better quality at 83% lower cost than Groq's generic 70B. The fine-tuning investment ($500-$2,000 one-time) pays back within the first month.

**Scenario 3: Mixed workload (inference + training + custom models)**

| Component | Together AI | Groq | | --- | --- | --- | | Inference (50K requests/day) | $3,864/mo | $3,105/mo | | Fine-tuning (monthly retraining) | $1,500/mo | Not available | | Custom model hosting | $2,000/mo | Not available | | **Total** | **$7,364/mo** | **$3,105/mo + external** |

For the mixed workload, Together AI provides a single platform. Using Groq requires cobbling together fine-tuning from another provider (Together, AWS, Lambda) and managing multiple integrations.

How to Choose: Decision Framework

| Your Need | Choose Together AI | Choose Groq | | --- | --- | --- | | Fastest inference speed | -- | 315 TPS, unmatched | | Cheapest per-token (standard models) | -- | 20-60% cheaper | | Fine-tuning required | Only option | Not available | | Custom/proprietary model hosting | Only option | Not available | | GPU clusters for training | Only option | Not available | | Largest model selection | 100+ models | ~15 models | | Need Llama 405B or DeepSeek V3 | Only option | Not available | | Real-time chat, voice AI | -- | Sub-100ms TTFT | | Domain-specialized model | Fine-tune on Together | Run generic on Groq | | Want both speed + flexibility | Use both via TokenMix.ai | Use both via TokenMix.ai |

Conclusion

Together AI vs Groq is not a versus. It is a spectrum of needs.

Groq is the best pure inference platform available. 315 TPS on Llama 70B, 7x faster than Together, at 20% lower per-token cost. If all you need is fast, cheap inference on popular open-source models, Groq is the clear winner.

Together AI is the best open-source AI compute platform. Fine-tuning, custom model hosting, GPU clusters, and 100+ models. If you are building custom ML pipelines, training domain-specific models, or need models Groq does not serve, Together AI is the only viable option.

Many production teams use both. Fine-tune on Together AI, then serve the fine-tuned model on Together for development and evaluation. For production inference where speed matters, serve standard models on Groq. TokenMix.ai unifies both platforms through a single API -- route speed-critical requests to Groq, serve custom models from Together, and pay below-list rates on both.

The best infrastructure strategy is not picking one platform. It is using each for what it does best. Compare real-time speed and pricing across both platforms at [TokenMix.ai](https://tokenmix.ai).

FAQ

Is Groq faster than Together AI?

Yes. Groq is 6-7x faster on every model both platforms serve. Llama 3.1 70B runs at 315 TPS on Groq versus 45 TPS on Together AI. The speed advantage comes from Groq's custom LPU hardware designed specifically for inference.

Is Groq cheaper than Together AI?

Yes, for standard inference. Groq charges $0.59/$0.79 for Llama 70B versus Together's $0.88/$0.88. That is 33% cheaper on input and 10% cheaper on output. However, Together AI's fine-tuned models can be significantly cheaper for domain-specific tasks.

Can I fine-tune models on Groq?

No. Groq is a pure inference platform with no fine-tuning capability. Fine-tuning must be done on Together AI, AWS, or your own GPU infrastructure. You can then serve the fine-tuned model on Together AI for inference.

Which has more model options?

Together AI offers 100+ models including Llama 405B, DeepSeek V3, Qwen 72B, and custom fine-tuned models. Groq offers approximately 15 models, focused on the most popular Llama, Mixtral, and Gemma variants.

Can I use both Together AI and Groq?

Yes. TokenMix.ai's unified API integrates both platforms. Route speed-critical requests to Groq and fine-tuned or large model requests to Together AI. One API key, unified billing, automatic routing.

When does fine-tuning make more sense than using a larger model?

Fine-tuning becomes cost-effective when you have domain-specific data and high inference volume. A fine-tuned Llama 8B ($0.18/M tokens) can match a generic Llama 70B ($0.59-$0.88/M tokens) on specific tasks -- saving 80% on inference with better quality. The break-even typically occurs within 1-2 months at moderate volume.

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*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [Together AI Pricing](https://www.together.ai/pricing), [Groq Pricing](https://groq.com/pricing), [TokenMix.ai](https://tokenmix.ai)*