Groq

Groq is an AI infrastructure company focused on high-speed inference—the process of running trained AI models to generate responses. Founded in 2016, Groq developed its own specialized Language Processing Unit (LPU) architecture and built GroqCloud around it. Rather than developing only its own AI models, Groq provides the computing infrastructure and API layer that allows developers and organizations to run a range of AI models with low latency and high throughput.

The company’s central product is GroqCloud, a developer platform for building applications powered by fast AI inference. Groq describes the service as an OpenAI-compatible inference platform that can be integrated into existing applications with relatively little code changes. Developers can access models through the Groq API, Groq’s own Python and TypeScript libraries, or OpenAI-compatible client libraries.

GroqCloud hosts models from several AI providers rather than restricting users to a single model family. Its current production catalog includes models such as OpenAI GPT-OSS 120B and 20B, Llama 3.3 70B, and Whisper, while other models are available in preview or through Groq’s evolving model catalog. This gives developers the ability to choose models according to their requirements for speed, reasoning, cost, context length, or modality.

Groq also provides Groq Compound, an AI system that combines models with built-in tools. Compound can selectively use capabilities such as web search and code execution to answer requests, making it suitable for more agentic applications where a model needs to perform actions or retrieve information rather than simply generate text. Compound and Compound Mini are currently listed as production systems on GroqCloud.

A major strength of the platform is its support for tool use and AI agents. Groq provides integrations with agent frameworks such as Agno, AutoGen, CrewAI, and LangGraph, allowing developers to build applications in which AI models can reason, use tools, maintain workflows, and coordinate multiple tasks. Groq also supports browser automation, code execution, MCP integrations, and other tools that can extend what AI applications can accomplish.

Groq’s API is not limited to text generation. Its current capabilities include speech-to-text, text-to-speech, vision, multilingual models, tool use, reasoning, and safety/content moderation. For speech recognition, Groq hosts Whisper Large V3 and Whisper Large V3 Turbo, both of which support multilingual transcription. Its text-to-speech offering currently includes English and Saudi Arabic Orpheus models.

The platform also supports vision applications. Developers can send images to supported multimodal models for tasks such as visual question answering, image captioning, and OCR. Groq’s current documentation lists Qwen3.6 27B as a supported multimodal model capable of processing text and image inputs.

Another important capability is web and website access through supported Groq systems. Groq Compound can use built-in web search and website-visiting capabilities to retrieve current information and analyze publicly accessible web pages. This enables developers to build applications that combine language models with live information retrieval.

Groq is designed with several service tiers to accommodate different application requirements. On-demand is the standard option, while Flex provides higher throughput on a best-effort basis. Enterprise customers can access the Performance tier, which is designed for production workloads requiring consistent low latency and includes a 99.9% availability SLA under the applicable enterprise agreement.

Pricing is primarily usage-based. Groq provides a Free tier with usage limits, while its Developer tier uses pay-as-you-go billing. Developers are charged according to the models and services they use, with different models having different per-token or per-hour prices. For example, the current model catalog lists GPT-OSS 120B at $0.15 per million input tokens and $0.60 per million output tokens, while Whisper Large V3 Turbo is priced at $0.04 per hour. Enterprise Performance capacity uses provisioned-throughput pricing instead of standard per-token billing.

Groq also emphasizes compatibility with existing AI development tools. Its API uses the OpenAI API format for many common operations, meaning developers can often adapt applications built with OpenAI-compatible clients by changing the API endpoint and credentials. Groq additionally maintains its own Python and TypeScript libraries.

The platform has a growing ecosystem of integrations. Official Groq documentation lists integrations covering AI agent frameworks, browser automation, LLM application development, observability, code execution, UI/UX, real-time voice, MCP, and hardware. Groq also provides ready-made Google Workspace connectors for Gmail, Google Calendar, and Google Drive, allowing AI agents to work with those services.

Groq’s approach makes it particularly useful for applications where response speed and inference latency matter, including real-time assistants, conversational applications, AI agents, coding tools, voice applications, search systems, and other interactive AI products. Its value proposition is less about being a single consumer chatbot and more about providing the infrastructure developers need to deliver AI-powered applications quickly and at scale.

Overall, Groq is best described as a specialized AI inference cloud and developer platform. Its combination of custom LPU infrastructure, fast model execution, OpenAI-compatible APIs, multiple model providers, agentic tools, multimodal capabilities, and developer integrations makes Groq particularly suited to organizations building real-time AI applications that require high throughput and low latency.