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    Groq

    AI & LLM APIs

    GroqCloud is a high-performance AI inference platform built to deliver ultra-low latency, predictable cost, and production-grade reliability for real-world applications.

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    Groq Overview

    GroqCloud is a high-performance AI inference platform built to deliver ultra-low latency, predictable cost, and production-grade reliability for real-world applications. Developers and enterprises use Groq to run state‑of‑the‑art large language models (Llama 4, GPT‑OSS, Qwen3 32B, Kimi K2), speech recognition (Whisper), and text‑to‑speech (Canopy Orpheus) at scale via an OpenAI‑compatible Responses API. With prompt caching, built‑in tools, and Compound Systems for web search, code execution, and browser automation, Groq powers fast, tool‑using agents and complex workflows with linear, transparent pricing. Groq is designed for teams that need speed and stability—AI product builders, enterprise platforms, and latency‑sensitive apps like customer support, real‑time research, dictation, and document-heavy analysis. Customers cite dramatic gains: instant-feeling chat, rapid document analysis, 10x cost reductions on entity extraction, and 20x faster transcription speeds. Remote MCP support (beta) lets you securely connect thousands of tools with zero code changes if you’re already on OpenAI’s Responses API, while Batch API and global availability help you process large workloads cost‑effectively. Whether you’re building AI agents for regulated environments (banking, healthcare, defense), powering high-throughput apps, or migrating from other providers, Groq combines measurable latency improvements, comprehensive model coverage, and clear, usage-based pricing to make production AI both fast and practical.

    Key Features & Capabilities

    Ultra‑low latency inference at scale

    Groq delivers extremely fast token throughput and response times across leading open models, enabling real‑time chat, document analysis, and interactive user experiences. Customers report transformative speedups that unlock workloads previously constrained by latency.

    Broad model catalog with predictable pricing

    Run GPT‑OSS (20B/120B), Llama 4 (Scout, Maverick), Llama 3.3 70B, Llama 3.1 8B, Qwen3 32B (131k context), Kimi K2, Whisper ASR, and Canopy Orpheus TTS. Linear, per‑unit pricing (tokens, characters, hours) makes costs transparent and easy to plan.

    Prompt caching for speed and savings

    Automatic prompt caching on GPT‑OSS models gives a 50% discount on cached input tokens and reduces latency—no configuration required. Cached tokens also don’t count toward GroqCloud rate limits, stretching your quotas further.

    Built‑in tools and Compound Systems

    Groq’s Compound Systems intelligently select tools (web search, browser automation, code execution) to answer queries, with pass‑through pricing. Built‑in tools are available by the request or hour, enabling robust, tool‑using agents without extra infrastructure.

    OpenAI‑compatible API + Remote MCP (beta)

    Drop‑in compatibility with the OpenAI Responses API and Anthropic’s Model Context Protocol lets you connect external tools (GitHub, browsers, databases) with zero code changes. Bring your own MCP servers and keys; third‑party fees billed by providers.

    Pricing Plans

    Starter/Free

    Free (usage-based billing for models/tools)
    • Immediate access to GroqCloud console and Playground
    • OpenAI‑compatible Responses API
    • Access to core model catalog (GPT‑OSS, Llama, Qwen3 32B, Kimi, Whisper, Orpheus)
    • Automatic prompt caching on GPT‑OSS (50% discount on cached input tokens)
    • Global availability in four regions

    Pro/Plus/Core

    Usage‑based (no monthly fee)
    • All Starter features
    • Per‑model token pricing (LLMs): e.g., GPT‑OSS‑20B $0.075/M input, $0.30/M output; GPT‑OSS‑120B $0.15/$0.60; Llama 4 Scout $0.11/$0.34; Llama 4 Maverick $0.20/$0.60; Llama 3.3 70B $0.59/$0.79; Llama 3.1 8B $0.05/$0.08; Qwen3 32B $0.29/$0.59; Kimi K2 $1.00/$3.00
    • ASR (Whisper): $0.111/hr (Large V3), $0.04/hr (V3 Turbo), 10s minimum per request
    • TTS (Canopy Orpheus): $22/M chars (English), $40/M chars (Arabic Saudi)
    • Built‑in tools priced per use/hour (e.g., Basic Search $5/1k req, Visit Website $1/1k, Code Execution $0.18/hr)

    Teams/Business

    Usage‑based with higher limits
    • All Pro features
    • Scale without standard rate limits by upgrading to a paid tier
    • Batch API for large asynchronous workloads (50% lower cost than synchronous)
    • No impact to standard rate limits for Batch jobs
    • 24‑hour to 7‑day processing window for Batch

    Enterprise

    Custom pricing
    • All Teams features
    • Enterprise API solutions and on‑prem deployment options
    • Access to additional and fine‑tuned models by request
    • Dedicated support and governance controls
    • Custom SLAs and security reviews

    Pricing is linear with no hidden costs or elastic pricing. Prompt caching currently applies to GPT‑OSS models (50% discount on cached input tokens; cache hits don’t count toward rate limits). Built‑in tools are billed per 1,000 requests or per hour as listed. ASR is billed per hour with a 10s minimum per request. Batch API offers ~50% lower cost and does not consume standard rate limits. All prices USD.

    Pros & Cons

    Pros

    • Consistently low‑latency, high‑throughput inference that unlocks real‑time UX for chat, RAG, and agents
    • OpenAI‑compatible Responses API and Remote MCP (beta) enable zero‑code migration and broad tool connectivity
    • Transparent, linear, per‑unit pricing with prompt caching that automatically cuts input costs by 50% on cache hits
    • Rich model catalog (GPT‑OSS, Llama 4/3.x, Qwen3 32B with 131k context, Kimi K2, Whisper, Orpheus TTS)
    • Compound Systems and built‑in tools (search, browser, code) support robust, production‑grade agent workflows

    Cons

    • ×Catalog focuses on open and select partner models; some proprietary frontier models may be unavailable
    • ×Remote MCP and certain compound capabilities are in beta, which may limit GA readiness for strict environments
    • ×Usage‑based billing across models and tools can require careful monitoring to manage aggregate costs
    • ×ASR billing has a 10‑second minimum per request, which can be inefficient for very short clips
    • ×Advanced features (e.g., on‑prem, custom fine‑tunes) are gated behind Enterprise engagement

    User Reviews

    Bernard Aceituno, Co‑founder & President, StackAI

    We saw a drastic jump in performance and latency—everything became much faster. That time savings is transformative for our customers.

    Guilherme Garibaldi, Founder Engineer, Cluely

    Prompt caching will be game‑changing for both speed and quality. With high prompt reuse, it accelerates our product and unlocks new use cases.

    Andre Smith, Founder & CEO, ScreenApp

    Using Groq, we reduced costs by 15x and transcription is 20x faster, lifting conversions by 30% and cutting churn in half.

    Igor Gligorevic, Co‑founder & CTO, Recall

    Groq Compound unlocked our knowledge graph—entity extraction and linking are fast and 10x cheaper than Google’s grounded search.

    Karissa Ho, Growth Team, StackAI

    JSON output, tool use, and consistent service in sensitive environments are crucial. GroqCloud gives us the flexibility to meet strict requirements.

    Frequently Asked Questions

    Is Groq beginner friendly?

    Yes. You can sign up and try Groq for free in the Developer Console and Playground, then call models via an OpenAI‑compatible Responses API. Prompt caching works automatically—no configuration required.

    How is Groq pricing structured?

    Pricing is linear and usage‑based. LLMs are billed per million input/output tokens by model (e.g., GPT‑OSS‑20B $0.075/$0.30; GPT‑OSS‑120B $0.15/$0.60; Llama 4 Scout $0.11/$0.34; Qwen3 32B $0.29/$0.59). ASR (Whisper) is per hour with a 10s minimum; TTS (Orpheus) is per million characters. Built‑in tools (search, visit website, code, browser automation) are billed per 1,000 requests or per hour.

    Does Groq offer a free tier or trial?

    Yes. You can get started for free on GroqCloud and upgrade to a paid tier as your needs grow. Paid tiers let you scale without standard rate limits.

    Which models are supported?

    Groq supports GPT‑OSS (20B/120B), Llama 4 Scout and Maverick, Llama 3.3 70B, Llama 3.1 8B, Qwen3 32B (131k context), Kimi K2 0905, Whisper Large V3/V3 Turbo (ASR), and Canopy Orpheus (TTS). Additional and fine‑tuned models are available for enterprise requests.

    What is prompt caching and how is it billed?

    Prompt caching detects identical prompt prefixes and reuses computation, reducing latency and giving a 50% discount on cached input tokens for GPT‑OSS models. There’s no extra fee for caching; discounts apply only on cache hits. Cached tokens also don’t count toward GroqCloud rate limits.

    Does Groq support tool‑using agents and MCP?

    Yes. Groq offers built‑in tools and Compound Systems for search, browsing, and code execution. Remote MCP (beta) lets you connect any MCP server (e.g., GitHub, browsers, databases) through the OpenAI‑compatible Responses API. You pay Groq for model tokens; third‑party MCP fees are billed by the provider.

    Can I run large batches or long‑running jobs?

    Yes. The Batch API processes large asynchronous workloads at ~50% lower cost with a 24‑hour to 7‑day processing window and no impact on standard rate limits.

    Is there an enterprise or on‑prem option?

    Yes. For enterprise API solutions and on‑prem deployments, contact Groq via the Enterprise Access page. Enterprise also offers access to additional models, dedicated support, and custom SLAs.

    Try Groq for Free

    Join thousands of developers who are already using Groq to enhance their workflow and productivity.