Groq vs Together AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Groq | Together AI |
|---|---|---|
| Pricing | Freemium, linear pricing, batch 50% cheaper | Freemium, per-token serverless, dedicated plans |
| Core focus | Ultra-low-latency LPU inference for real-time apps | Full-stack AI cloud: inference, fine-tuning, pre-training |
| Models | Open-source incl. GPT-OSS, Kimi K2, day-zero access | 100+ open-source, incl. DeepSeek V4 Pro, Llama 4 Maverick, Qwen3.7-Max |
| Key differentiator | Sub-200ms latency, Compound AI systems, Orpheus TTS | Batch inference up to 30B tokens, fine-tuning with FlashAttention-4 |
| Integrations | OpenAI SDK, MCP, BrowserBase, Stripe, Tavily | CodeSandbox, Hugging Face, LangChain, Weights & Biases |
| Best for | Real-time agents, voice AI, low-latency apps | Production coding agents, batch processing, fine-tuning |
If you need real-time responsiveness under 200ms — chatbots, voice assistants, agentic systems — Groq's LPU is the clear winner, with day-zero model access and a dead-simple switch from OpenAI. But if your workloads are batch-heavy, require fine-tuning, or need massive async token throughput (up to 30B tokens), Together AI's full-stack cloud — from sandbox to AI Factory — offers more flexibility and training depth. Choose Groq for speed, Together AI for scale and customization.
AI-native cloud for running open-source LLMs at scale—serverless inference, fine-tuning, and GPU clusters.
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The core difference is architectural: Groq's custom LPU is engineered for sub-200ms latency, making it ideal for real-time AI agents, chatbots, and voice applications. Its recent additions enhance this: Compound AI systems (web search, code execution, browser automation) via a single API call, Orpheus TTS at 100+ chars/s, and remote MCP server integration for external tool connectivity. Together AI, by contrast, is a full-stack AI cloud—it offers serverless inference for 100+ models, batch inference scaling to 30 billion tokens per model, and dedicated GPU clusters (B200, H200, GB300) for heavy workloads. It also supports fine-tuning with advanced kernels (FlashAttention-4, ATLAS) and pre-training via the Together Kernel Collection, plus managed storage with zero egress fees and sandbox environments via CodeSandbox SDK. While Groq provides OpenAI-compatible API for easy switching and prompt caching to cut costs, Together AI's model library and playground (Together Chat) are more extensive for experimentation and comparison. Groq's day-zero support for new models like GPT-OSS and Kimi K2 is a differentiator, but Together AI emphasizes production-grade custom infrastructure and research-driven optimizations.
Pricing compared
Both are freemium, but the cost structures diverge sharply. Together AI uses per-token pricing for serverless inference, with dedicated plans requiring commitment — ideal for predictable but not sporadic usage. Batch inference scales to 30B tokens but careful with token costs. Groq offers linear and predictable pricing with no idle infrastructure costs, and its Batch API cuts cost by 50% for async workloads. Prompt caching on GPT-OSS models yields up to 50% savings on cached tokens. For real-time apps, Groq's sub-200ms latency is bundled with competitive per-token rates, but if you're doing massive batch processing, Together AI's batch inference might be more cost-effective per token, depending on volume. Groq's pricing is transparent for startups; Together AI's dedicated GPU clusters (B200, etc.) have no published prices, so enterprises need to contact sales. Overall, Groq is cheaper for bursty, low-latency needs; Together AI may be cheaper for high-volume batch and training.
Who should pick which
- Solo founder building a real-time chatbotPick: Groq
Groq's sub-200ms latency and easy switch from OpenAI SDK make it ideal for quick, responsive MVPs without infrastructure complexity.
- Enterprise running batch inference on massive datasetsPick: Together AI
Together AI's batch inference scales to 30B tokens per model, handling async heavy loads efficiently with per-token pricing.
- ML researcher fine-tuning open-source modelsPick: Together AI
Together AI offers fine-tuning with FlashAttention-4 and ATLAS kernels, plus pre-training support—Groq only provides inference.
- Voice AI developer needing instant TTSPick: Groq
Groq's Orpheus TTS delivers 100+ chars/s for real-time speech, paired with low-latency inference.
- Startup scaling from prototype to production with custom infrastructurePick: Together AI
Together AI's sandbox via CodeSandbox and AI Factory custom infrastructure let you grow without migration, unlike Groq's fixed LPU setup.
Frequently Asked Questions
Groq vs Together AI: which should you choose?
If you need real-time responsiveness under 200ms — chatbots, voice assistants, agentic systems — Groq's LPU is the clear winner, with day-zero model access and a dead-simple switch from OpenAI. But if your workloads are batch-heavy, require fine-tuning, or need massive async token throughput (up to 30B tokens), Together AI's full-stack cloud — from sandbox to AI Factory — offers more flexibility and training depth. Choose Groq for speed, Together AI for scale and customization.
Can I use Groq for fine-tuning models?
No, Groq's features listed are inference-focused only; fine-tuning isn't mentioned in its offering. For fine-tuning, Together AI is the go-to.
Which platform supports the latest open-source models first?
Groq claims day-zero support for new models like GPT-OSS and Kimi K2, as shown in its news. Together AI also hosts many models but doesn't stress day-zero in its description.
Does Together AI offer a low-latency option?
Together AI doesn't claim sub-200ms latency like Groq; it emphasizes high TPS and throughput. For real-time critical apps, Groq is the safer bet.
Is there a free tier on both?
Yes, both are freemium, but the free tier limits are not detailed here. Expect trial credits or limited usage.
Which is easier for a developer familiar with OpenAI's API?
Groq's OpenAI-compatible API can be switched in two lines of code, making it the quickest transition. Together AI also integrates with OpenAI SDK but may require more setup.
Can Groq handle heavy batch processing?
Groq offers a Batch API with 50% lower cost, but its focus is on latency, not massive throughput like Together AI's 30B token scaling. For extreme batch volumes, Together AI is stronger.
What about voice and multi-modal support?
Groq has Orpheus TTS and Whisper ASR. Together AI mentions container inference for video, audio, and image models, but no specific TTS/ASR products are listed.
Which platform is better for agentic workflows?
Groq's Compound AI systems integrate web search, code execution, and browser automation, plus MCP server support, making it more agent-ready. Together AI lacks such bundled agent tools.
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Last reviewed: August 3, 2026