What Sets Them Apart
The inference provider market has grown beyond just OpenAI and Anthropic, and Groq and Together AI represent the most developer-friendly alternatives for teams that need fast, affordable LLM access. Both provide API access to open-weight models like Llama, Mistral, and others, but their technical approaches and feature sets serve different needs.
Groq Cloud and Together AI at a Glance
Groq's defining advantage is raw speed. Their custom Language Processing Unit (LPU) hardware delivers inference speeds that often exceed 500 tokens per second for supported models — making real-time, streaming AI applications genuinely feel instantaneous. For use cases where latency directly impacts user experience (chatbots, code completion, real-time translation), Groq's speed advantage is transformative.
Together AI takes a broader platform approach. Beyond inference, it offers fine-tuning as a service, custom model training, and a wider selection of available models including larger variants that Groq's hardware may not yet support. The GPU-based infrastructure is more flexible for diverse model architectures and sizes.
Model availability differs. Groq focuses on a curated set of popular open-weight models optimized for their LPU hardware — Llama 3, Mixtral, Gemma, and others. Together AI offers a broader catalog including newer and niche models, plus the ability to deploy custom fine-tuned models. If you need a specific model variant, Together AI is more likely to have it.
Pricing, API Compatibility, and Fine-tuning
Pricing models are both developer-friendly with per-token billing and no minimum commitments. Groq's pricing is competitive for the models it supports, though the extreme speed comes at a slight premium over the cheapest GPU providers. Together AI often offers some of the lowest per-token prices in the market, with promotional free tiers for popular models.
API compatibility is strong in both. Both provide OpenAI-compatible REST APIs, meaning existing code using the OpenAI SDK or libraries like LangChain, LlamaIndex, and LiteLLM can switch to either provider with minimal changes. This interoperability is crucial for developers who want to avoid vendor lock-in.
Fine-tuning is where Together AI has a clear advantage. Their platform supports supervised fine-tuning, RLHF, and custom model deployment. Groq focuses purely on inference — if you need to fine-tune a model on your data, you would train it elsewhere and potentially deploy it on Together AI or another GPU provider.
Reliability and Use Case Fit
Reliability and uptime considerations matter for production use. Groq's specialized hardware means they have a smaller infrastructure footprint, and high-demand periods can lead to longer queue times. Together AI's GPU-based infrastructure benefits from more mature scaling patterns and a larger total compute pool.
For applications where inference speed is the primary concern — real-time chat, streaming code completion, interactive AI features — Groq's LPU-based inference provides a genuinely different user experience. The near-instant responses make AI interactions feel native rather than waiting-for-the-cloud.