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Pricing sourced from GroqCloud Platform · Last verified March 2026
An LPU (Language Processing Unit) is Groq's custom-designed chip, pioneered in 2016, built specifically for running AI inference rather than training. Unlike GPUs — which are general-purpose parallel processors adapted for AI — the LPU's architecture eliminates memory bottlenecks that typically slow down sequential token generation. This translates to higher tokens-per-second throughput and more predictable latency, particularly for large language models. The tradeoff is that LPUs are specialized for inference workloads and don't replace GPUs for training.
GroqCloud provides an OpenAI-compatible API, so in most cases you only need to change two things in your existing code: set the base_url to https://api.groq.com/openai/v1 and replace your API key with a GROQ_API_KEY from the Groq developer console. Your existing OpenAI SDK calls (chat.completions.create, etc.) will work against supported open models like Llama and Mixtral. You'll want to swap the model parameter to a Groq-hosted model name, then benchmark latency and cost against your current provider.
For supported open-weight models, GroqCloud typically offers lower per-token pricing than proprietary frontier APIs because you're paying for open-source model hosting rather than access to closed models. Customer Fintool reported an 89% cost reduction after migrating to GroqCloud, and Opennote credits Groq with letting them keep student pricing affordable. However, a direct comparison depends on which model you pick — GroqCloud hosts Llama, Mixtral, Gemma, and similar open models, not GPT-4 or Claude, so the comparison is really between open-model inference providers.
Groq serves more than 3 million developers and teams, with notable enterprise customers including the McLaren Formula 1 Team (which uses Groq for real-time race decision-making and analysis), the PGA of America, AI research startup Fintool, and education platform Opennote. The McLaren partnership is a marquee deployment showing Groq's suitability for latency-sensitive, real-time inference. Customer quotes on Groq's site cite specific outcomes — 7.41x speed improvements, 89% cost reductions, and sustainable pricing for consumer-facing AI products.
GroqCloud hosts popular open-weight models including Llama variants, Mixtral, Gemma, and — as of August 2025 — day-zero support for OpenAI's open models. The platform is specifically optimized for Mixture-of-Experts architectures and other frontier-scale open models, which Groq detailed in its May 2025 engineering blog 'From Speed to Scale.' The full current catalog and per-model pricing is listed on the Groq pricing page. You cannot bring your own fine-tuned weights the way you can on platforms like Together AI or Replicate — GroqCloud focuses on hosted, optimized deployments of publicly available models.
AI builders and operators use GroqCloud Platform to streamline their workflow.
Try GroqCloud Platform Now →AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
Compare Pricing →Production inference platform for open-weight LLMs, multimodal models, and custom fine-tunes — known for very fast serving (FireAttention/FireOptimizer), reliable function calling, and JSON mode at low per-token prices.
Compare Pricing →Run, fine-tune, and deploy thousands of community AI models with a single HTTP API — covering image, video, audio, language, and embedding models, billed per-second of GPU time.
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