Together AI vs Groq

Architecture: Software Optimization vs. Custom Silicon

The foundational difference between Together AI and Groq is where the optimization happens. Together AI takes commodity NVIDIA GPUs and squeezes maximum performance out of them through software — speculative decoding, quantization, FP8 kernels, and research breakthroughs like FlashAttention-4. This approach benefits from NVIDIA's massive ecosystem and lets Together AI offer the latest GPU generations (B200, GB200) as soon as they ship.

Groq took the opposite bet: design silicon from scratch specifically for the sequential, memory-bound nature of autoregressive token generation. The Groq 3 LP30 chip carries 512 MB of on-chip SRAM per die with 150 TB/s of memory bandwidth — seven times faster than NVIDIA's Rubin GPU. A full LPX rack houses 256 interconnected LPUs delivering 40 PB/s of aggregate bandwidth. This deterministic, compiler-orchestrated execution eliminates the scheduling overhead that plagues GPU-based inference.

The NVIDIA-Groq partnership announced in 2026 signals that the industry sees these approaches as complementary rather than competitive at the hardware level — with GPUs handling training and prefill while LPUs accelerate the latency-sensitive decode loop. But at the API level, developers still must choose between them.

Model Ecosystem and Flexibility

Together AI wins decisively on breadth. With over 150 optimized models spanning LLMs, image generators, video models, and audio (TTS/STT), Together AI functions as a one-stop AI infrastructure shop. The platform expanded dramatically in 2026, adding video generation via OpenAI Sora 2 and Google Veo 3.0, plus real-time audio streaming with Orpheus 3B and Kokoro 82M models.

Groq's model library is deliberately narrow — a curated set of popular open-source LLMs optimized for its LPU hardware. You won't find image generation, video, or audio models on GroqCloud. This is a natural consequence of custom silicon: each model must be compiled specifically for the LPU architecture, making the long tail of models impractical to support. For teams that need only fast LLM inference, this constraint is irrelevant. For teams building multimodal AI agents, it's a dealbreaker.

Training, Fine-Tuning, and the Full ML Lifecycle

Together AI covers the complete AI development lifecycle: train models on GPU clusters, fine-tune them with LoRA or full fine-tuning, deploy them on serverless endpoints, and iterate with model versioning and A/B traffic splitting. The GPU cluster offering — now generally available with H100 through GB200 hardware — means teams can stay on a single platform from research to production.

Groq is purely an inference platform. There's no fine-tuning, no training infrastructure, and no custom model deployment on GroqCloud. Enterprise customers can discuss custom-tuned models via sales, but the self-serve platform is inference-only. Teams using Groq will need a separate provider for training and fine-tuning — which often means Together AI, Lambda, or CoreWeave for the training phase.

Latency and the Agentic Imperative

For real-time agentic applications, latency is the metric that matters most. When an AI agent chains multiple LLM calls — reasoning, tool use, response generation — every millisecond compounds. Groq's sub-second response times and target of 1,500 tokens/sec with Groq 3 create a fundamentally different user experience for conversational and agentic workloads.

Together AI's inference is fast by GPU standards but cannot match purpose-built silicon on raw token generation speed. The ~1,659ms benchmark for 100 tokens on Llama 70B versus Groq's ~851ms tells the story. However, Together AI's time-to-first-token can be competitive, and for many applications the total response time difference is less dramatic than the raw throughput numbers suggest.

NVIDIA claims the Groq 3 LPX rack paired with Vera Rubin NVL72 delivers 35x higher throughput per megawatt than Blackwell NVL72 alone for trillion-parameter models — a metric that matters enormously as inference workloads scale and energy costs become a limiting factor in the inference economy.

Pricing and Cost Structure

Both platforms use pay-per-token pricing for inference, but the economics differ in important ways. Groq's aggressive pricing (as low as $0.05/M input tokens for some models) is subsidized by hardware efficiency — when your silicon is purpose-built for inference, you can undercut GPU-based providers on cost per token. Groq also offers a free tier, a 25% developer discount, and 50% batch processing discounts.

Together AI's token pricing tends to run slightly higher for equivalent models, but the platform offers value through breadth: fine-tuning, GPU clusters, and multimedia generation all under one billing relationship. For teams that would otherwise pay multiple providers, Together AI's consolidated pricing can be more economical overall. The per-minute GPU cluster billing is particularly attractive for burst training and experimentation workloads.

Hardware Composability and the Future

Groq's approach embodies composability at the hardware level — the idea that different specialized components can be assembled for different workloads. The NVIDIA partnership formalizes this: Vera Rubin GPUs for training and prefill, Groq 3 LPUs for decode, assembled into a unified system. This mirrors the software composability that defines the Creator Era, applied to the silicon layer.

Together AI's composability is at the software and platform level — assembling GPU clusters, inference endpoints, fine-tuning pipelines, and multimedia APIs into coherent workflows. Their research contributions (FlashAttention-4, ThunderAgent, together.compile) push the boundaries of what GPU-based systems can achieve, potentially narrowing the performance gap with custom silicon over time.