TokenMix Research Lab · 2026-04-12

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

Last Updated: 2026-04-29
Author: TokenMix Research Lab

Different products, not direct competitors. Groq Llama 70B: 315 TPS at $0.59/$0.79 — 7x faster, 20-60% cheaper than Together. But Groq has 15 models, no fine-tuning, no custom hosting, no GPU rental. Together has 100+ models, LoRA + full fine-tuning, dedicated GPU clusters, custom model hosting. Groq = inference engine. Together = AI compute platform.

Together AI vs Groq 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 as of April 2026.

Table of Contents

• Quick Comparison: Together AI vs Groq
• Why This Comparison Is Misleading (And Useful)
• Speed Comparison: Groq's LPU vs Together's GPUs
• Together AI vs Groq Pricing
• Fine-Tuning: Together's Exclusive Advantage
• GPU Clusters and Custom Deployment
• Model Availability Comparison
• Full Comparison Table
• Cost Breakdown at Production Scale
• How Should You Choose Between Together and Groq?
• What's the Bottom Line on Together vs Groq?
• FAQ

Quick Comparison: Together AI vs Groq

Speed: Groq 315 TPS vs Together 45 TPS (Llama 70B). Pricing: Groq $0.59/$0.79 vs Together $0.88/$0.88. Models: 100+ (Together) vs ~15 (Groq). Fine-tuning + GPU clusters + custom hosting: Together only. Hardware: Groq custom LPU vs Together NVIDIA GPUs (A100/H100). Groq wins inference speed/price; Together wins ML pipeline flexibility.

Why This Comparison Is Misleading (And Useful)

Groq = pure inference platform — send requests, get fast responses, end of product. Together = AI compute platform — fine-tuning, custom model hosting, GPU rental, training API, plus inference. Like comparing a CDN to AWS. Direct head-to-head ignores most of Together's value. The platforms serve different audiences with minimal overlap on workload requirements.

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, 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

Groq is 6-7x faster on every shared model: Llama 8B 750 vs 120 TPS, Llama 70B 315 vs 45, Mixtral 8x7B 480 vs 70. TTFT 3-4x faster (Llama 70B 0.10s vs 0.45s). Reason: LPU eliminates GPU memory bandwidth bottleneck via deterministic execution. Together's GPU infrastructure trades speed for flexibility — running any model size with quantization, fine-tuning, custom configs.

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):

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:

Groq's TTFT is 3-4x faster. For streaming 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

On shared models, Groq cheaper everywhere: Llama 70B 33% input / 10% output, Mixtral 60% both sides. Together-only models (Llama 405B, Qwen 72B, DeepSeek V3) make price comparison moot for those workloads. Together fine-tuning: $0.50-$2.00 LoRA / $2-$8 full per GPU-hour. Together dedicated GPUs: A100 $2.50/h, H100 $4/h. Groq has zero equivalent products.

Inference pricing (per million tokens):

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

Real example from legal tech: Together fine-tuned Llama 8B on 50K legal docs → 94% clause extraction accuracy, matching generic 70B's 93%. Inference cost dropped from $0.88/M (70B) to $0.18/M (8B). Speed jumped 45 → 120 TPS. Net: same quality, 80% cost reduction, 2.7x faster. Groq has no fine-tuning. ML teams must use Together (or AWS/own infra) for tuning, then choose where to serve.

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-only capabilities: dedicated endpoints (no noisy neighbor effects, SLA guarantees), GPU cluster rental (up to hundreds of H100s for training), custom model hosting (bring own weights), inference optimization (INT8/INT4 quantization, speculative decoding, continuous batching). Groq is fixed-capability — Groq's models, Groq's speed, no customization. Different products, different audiences.

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

Together has 6-7x larger catalog: Llama 405B (only on Together), Mixtral 8x22B, Qwen 2.5 7B-72B, DeepSeek V3/R1, Gemma 2 27B, code-specialized models, embeddings, plus custom fine-tunes. Groq's 15-model catalog focuses on highest-demand Llama, Mixtral, Gemma variants. If your model isn't on Groq, Together (or another provider) is your only option.

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

Together-only: fine-tuning (LoRA + full), custom hosting, dedicated endpoints, GPU cluster rental, training API, embedding models, batch inference, configurable quantization, dedicated SLA. Groq advantages: 7x faster, 20-60% cheaper on shared models, OpenAI-compatible. Tied: function calling, streaming, JSON mode, OpenAI-compatible API, ~99% uptime.

Cost Breakdown at Production Scale

Three scenarios at 50K req/day. Pure inference: Groq Llama 70B $3,105/mo vs Together $3,864/mo + 6.8x faster — Groq wins both. Fine-tuned 8B (Together-only): $540/mo at 94% domain quality vs $3,105 generic 70B at 93% — 83% savings. Mixed workload (inference + training + custom): Together $7,364/mo unified vs Groq $3,105/mo + cobble together fine-tuning elsewhere.

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

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

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.

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)

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 Should You Choose Between Together and Groq?

Fastest inference: Groq (315 TPS unmatched). Cheapest standard models: Groq (20-60% cheaper). Fine-tuning required: Together (Groq doesn't offer it). Custom/proprietary model hosting: Together only. Llama 405B / DeepSeek V3 / Qwen 72B: Together only. Real-time chat/voice: Groq (sub-100ms TTFT). Domain-specialized model: fine-tune on Together → serve on Together. Want both: TokenMix.ai unified API.

What's the Bottom Line on Together vs Groq?

Not a versus — a spectrum. Groq = best pure inference (7x faster, 20% cheaper, 15 popular models). Together = best AI compute platform (fine-tuning, custom hosting, GPU clusters, 100+ models). Many production teams use both: fine-tune on Together → serve standard models on Groq for speed. TokenMix.ai unifies both via single API + below-list pricing — pick each platform for what it does best.

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.

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.

Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: Together AI Pricing, Groq Pricing, TokenMix.ai