Groq is an AI inference provider built on its own Language Processing Unit (LPU) chips instead of GPUs. You call GroqCloud through an OpenAI-compatible API and run open-weight models like Llama, GPT-OSS, Kimi K2 and Whisper at speeds most GPU providers can't match. The hardware is designed for predictable latency, so response times stay consistent rather than drifting under load. It fits developers building voice agents, chatbots and agentic loops where every extra millisecond is noticeable to the user.

• LPU inference hardware: Groq's custom Language Processing Units are built specifically for AI inference, using deterministic execution and on-chip memory to deliver predictable, fast processing.
• High-speed inference: Groq publishes model-specific inference speeds ranging from hundreds to around 1,000 tokens per second on some current models, including 1,000 t/s for GPT-OSS 20B.
• OpenAI-compatible API: Developers can connect existing OpenAI client libraries to Groq by changing the API base URL to https://api.groq.com/openai/v1 and using a Groq API key.
• Multiple AI models: GroqCloud provides access to models including GPT-OSS, Llama and Whisper, with some models supporting context windows up to 131,072 tokens.
• Speech processing: Whisper models support speech-to-text and audio translation through the Groq API.
• Tool and structured output support: Developers can build applications that use tool calling, streaming and structured responses where supported by the selected model and API.
• Batch processing: The Batch API handles large asynchronous workloads at 50% lower cost than synchronous API pricing, with processing windows from 24 hours to 7 days.
• Flexible service tiers: On-demand, Flex and Performance tiers give developers different options for latency, throughput and production workloads.

• Fast inference: Groq's LPU infrastructure delivers high token-generation speeds, with current models reaching around 500 to 1,000 tokens per second.
• Easy API migration: Its OpenAI-compatible API makes it easier to adapt applications already built around OpenAI's API format.
• Low token costs: Several models are priced below $1 per million input or output tokens, including GPT-OSS 120B at $0.15 input and $0.60 output.
• Pay-as-you-go pricing: The Developer tier uses usage-based billing, so teams can scale API usage without committing to a fixed monthly subscription.
• Good for real-time voice: Fast inference and integrations with platforms such as ElevenLabs make Groq suitable for low-latency voice applications.
• Large developer community: Groq says more than six million developers use GroqCloud, giving the platform a broad developer base and ecosystem.

• Rate limits: Model-specific RPM, TPM, and daily limits can restrict high-volume or parallel workloads, with 429 errors returned when limits are exceeded.
• Limited model choice: Groq's catalog is smaller than platforms that provide access to a wider range of proprietary and open models, so some applications may still need other AI APIs.
• Limited fine-tuning: Groq supports LoRA inference for enterprise customers, but developers must create their LoRA adapters externally.
• Latency can vary: The standard on-demand tier provides high inference speeds but can experience queue latency during peak periods, while Flex processing is best effort.
• Changing catalog and pricing: Model availability and pricing can change, so teams should check current rates before planning long-term usage.

• Real-time voice agents: Combine fast speech-to-text, LLM inference, and text-to-speech to build responsive voice assistants and conversational agents.
• Customer support chatbots: Build conversational support systems that can generate responses quickly and handle multi-turn customer interactions.
• AI agent workflows: Run multi-step agents that use tools, APIs, MCP servers, and external services to complete tasks.
• Live transcription and translation: Power meeting transcription, live captions, voice interfaces, and audio translation with Groq's Whisper models.
• Code assistants: Add fast AI-generated responses and streaming completions to developer tools, coding assistants, and other software products.