Groq
Groq · Other
Groq is an AI inference platform that runs open models on its own LPU chips, delivering very fast responses through the GroqCloud API and console.
About Groq
What Is Groq
Groq is an American AI company, founded in 2016 by a group of former Google engineers led by Jonathan Ross, one of the designers of Google's TPU chip. Rather than train models, Groq focuses on serving them fast. It built a custom chip it calls the LPU, short for Language Processing Unit, and rents access to fleets of those chips through a cloud platform named GroqCloud.
The pitch is speed and cost. Because the LPU is built specifically for running models rather than training them, Groq says it returns text far quicker than general purpose GPUs, which matters when you're powering a chat app, a voice agent, or a coding assistant that needs to feel instant. Developers reach it through an API that copies the OpenAI format, so most existing code works after a small change to the base URL and key.
The main catch is scope. Groq serves open models built by other labs, such as Llama, Mistral, Qwen, and the GPT OSS line, so it won't give you a closed frontier model like GPT or Claude directly. You also depend on Groq's servers staying up and on its free tier's rate limits, which are generous for testing but tight for production traffic until you move to a paid plan.
Getting Started
- Go to console.groq.com and create an account with an email address or a Google account.
- Generate an API key from the console, then keep it private since it bills against your account.
- Send a request to the chat completions endpoint. Reuse any OpenAI SDK, then swap in Groq's base URL and your key.
- Try it in the built-in playground first if you want to compare models before writing code.
- Watch your usage under the free tier, then add a payment method or request a higher tier when you need more throughput.
Product Information
A quick look at Groq's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Developers building AI features
- Startups watching inference bills
- Product teams shipping voice and chat agents
Tasks
- Serving chat and completion requests
- Speech to text and text to speech
- Running batch jobs
- Prototyping new ideas
Scenarios
- Real-time assistants
- Cost-sensitive scaling
- Migrating off another provider
Key features
Fast Inference on Custom LPU Chips
Groq designs its own chips, the Language Processing Unit, specifically to run AI models rather than train them. The company says this architecture returns text far quicker than general purpose GPUs, which is the reason most people come to Groq in the first place. For anything interactive, that speed is what users actually feel.
An OpenAI-Compatible API
GroqCloud exposes a chat completions endpoint that matches OpenAI's format, so in most cases you point an existing SDK at Groq's base URL, drop in a Groq key, and it works. That keeps migration cheap and lets teams try Groq without rewriting their integration. It also lowers the learning curve for anyone who's already built with OpenAI.
A Catalog of Open Models
The platform serves open models from several labs: the Llama, Mistral, and Qwen families along with the GPT OSS line. You can switch between them by changing one parameter, which makes it easy to compare quality and cost on the same workload. Groq keeps adding and retiring models as the open model market shifts.
Voice, Speech, and Transcription
Beyond text, Groq handles speech to text and text to speech. Those cover the Orpheus voice models as well. That means a single platform can power a transcription tool, an audiobook reader, or a voice agent without a patchwork of several vendors. For teams that build conversational products, everything in one API makes the stack simpler.
Service Tiers and Batch Processing
Groq splits work into service tiers so you can trade speed against price. The main tier runs on demand for interactive use, while flex and batch options cost less and suit workloads that can wait. That gives cost-conscious teams a lever to pull without leaving the platform.
Tools, Structured Outputs, and MCP
The API supports tool use, structured outputs, code execution, and remote tools through MCP connectors. These features let a model call functions, return clean JSON for a downstream system, or reach outside data sources, which is what turns a chat model into something that can actually complete a task.
Pros and cons
Pros
- Responses come back fast, which suits real-time chat, voice, and coding tools. That speed is the core of what fast LLM inference buys you.
- The API matches OpenAI's format, so existing code often works with a small change.
- A free tier with a usable rate limit makes it easy to test before paying.
- Per-token pricing on open models keeps costs predictable as usage grows.
Cons
- It serves open models only, so you can't run a closed frontier model like GPT or Claude on it.
- Free tier rate limits are tight for production traffic, so an upgrade often comes sooner than some expect.
- You rely on Groq's uptime and model lineup, both of which change as the open model field shifts.
- Pricing and tier details vary by model, so estimating a bill takes a little homework.
Frequently asked questions
Groq is used to run AI models fast. Developers use it to power chat apps, voice agents, coding assistants, and batch jobs, calling open models through an API that mirrors OpenAI's format.
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