Groq is a low-latency AI inference provider that runs open-weight models on its custom Language Processing Unit (LPU) hardware instead of the GPUs used by AWS Bedrock or together.ai. We rate it 85/100 — if you need the fastest token output you can get from a hosted API for chatbots, agents, and voice apps, Groq is the benchmark. If you need a reliable production surface beyond a few hundred requests a day on the free tier, the Developer plan is unavoidable.

What is Groq?

Groq is a Mountain View, California chip and inference company founded in 2016 by Jonathan Ross, who designed and built the first generation of Google’s Tensor Processing Unit (TPU) as a 20% project before leaving to start the company. Groq’s thesis is that inference workloads, where every user is waiting for the next token, are fundamentally different from training workloads, and that a deterministic, single-core, software-scheduled architecture (the LPU) can serve them with far lower latency than a GPU stack designed for matrix-multiply throughput.

The consumer-facing product is GroqCloud, a hosted inference API that launched in early 2024. By late 2025 the company reported roughly 2 million developers on the platform and accounts inside 75% of Fortune 100 companies. In December 2025 Nvidia agreed to license Groq’s inference IP and absorb a portion of its team in a deal valued at approximately $20 billion — Nvidia’s largest transaction on record. Groq itself continues to operate as an independent company under new CEO Simon Edwards.

GroqCloud developer console showing model selection and token-per-second performance — GroqCloud’s developer console: model picker with live tokens-per-second readouts.

Key Features of Groq

LPU-accelerated inference: Llama 3.1 8B Instant pushes 1,000+ tokens/second on Groq, and Llama 3.3 70B serves at roughly 275 t/s — 3–7× faster than competing GPU providers in independent benchmarks.
OpenAI-compatible API: The chat-completions endpoint mirrors OpenAI’s schema, so most SDKs work after changing one base URL and one API key.
Sub-200ms TTFT: Time-to-first-token is consistently under 200ms for most served models, which is what makes Groq usable for voice agents and real-time UX where 1-second latency feels broken.
Open-weight model catalog: Llama 3.x, Llama 4 Maverick, Gemma 2, Mixtral, Whisper Large, and other open models — no GPT-class proprietary frontier models, by design.
Free tier with no credit card: Every model is available at 30 RPM / 6,000 TPM / 1,000 RPD, which is enough to ship a working prototype before you ever pay a dollar.

What Users Say About Groq

On Hacker News, Groq threads consistently surface the same two reactions: amazement at the raw speed (one customer cited an internal 7.4× chat-speed gain and an 89% cost reduction after switching from a GPU provider), and frustration with daily request caps that throttle anything past a single-developer side project. Reddit’s r/LocalLLaMA points to Groq as the go-to hosted option when local inference isn’t fast enough, but Reddit users echo the production complaint — the requests-per-day ceiling is the binding constraint, not RPM. The Groq community forum has long-running threads from teams asking how to escalate to enterprise rate limits, with developers describing slow turnaround on those requests.

Groq LPU hardware rack used to run inference workloads — The Groq LPU hardware: a deterministic, single-core inference accelerator that powers GroqCloud.

Groq Pricing

Groq offers three tiers. The free tier covers prototyping; the Developer tier removes daily caps and is the realistic minimum for production use; Enterprise unlocks dedicated capacity and custom SLAs.

Plan	Price	Key Limits
Free	$0	30 RPM / 6,000 TPM / 1,000 RPD on most models; every model available; no credit card required.
Developer	From $0.05 / M input tokens (per-model rates apply)	Up to 10× the free-tier rate limits; published 25% discount on selected models; pay-as-you-go billing.
Enterprise	Contact sales	Dedicated capacity, custom rate limits, SLAs, and procurement-friendly contracts.

Per-model token prices vary — the cheapest open-weight models at $0.05/M input are dramatically below GPT-5.5 Mini economics, but flagship Llama 4 Maverick costs more.

Who Should Use Groq?

Best for: AI engineers building voice agents, real-time chat, autonomous agent loops, and latency-sensitive RAG pipelines on open-weight models. Indie developers who want the fastest free hosted inference in the market for prototyping. Teams that have already chosen Llama or Mixtral and just need to run them faster.

Not ideal for: Teams that need GPT-5.5, Claude Opus 4.7, or Gemini 3 — Groq doesn’t host proprietary frontier models. High-volume batch workloads where throughput matters more than latency: a GPU provider is usually cheaper for offline jobs. Anyone who needs a fully managed enterprise stack on day one without a sales conversation.

Pros and Cons

Pros:

Fastest sustained tokens-per-second on hosted open-weight models — not marginally, but multiples ahead.
Drop-in OpenAI-compatible API; existing code can switch in minutes.
Generous free tier covers full development cycles before a single dollar is spent.
Strong recent enterprise validation: Fortune 100 penetration plus the Nvidia licensing deal.

Cons:

Daily request caps (1,000 RPD on free tier) bite hard once a real product has even a handful of users.
No GPT, Claude, or Gemini family models — open-weight only.
Enterprise rate-limit increase requests have a reputation for being slow on the community forum.
The Nvidia licensing deal introduces uncertainty about long-term independence and roadmap alignment.

Alternatives to Groq

The closest direct competitors are Together AI (broader model catalog on GPUs, slower), Fireworks AI (similar positioning, strong fine-tuning story), and Replicate (broader generative-media coverage, not latency-focused). For proprietary frontier models you need OpenAI, Anthropic, or Google directly — Groq doesn’t play in that lane.

Verdict: Is Groq Worth It?

Yes, with one caveat. If your application’s success depends on inference latency — voice, real-time agents, fast chat — Groq is the strongest hosted option in 2026, and the free tier is generous enough that there is no excuse not to benchmark it against your current provider this week. The caveat is that the moment you need to ship to real users, you will outgrow the free tier’s daily cap and need to commit to paid usage. At our 85/100 rating, Groq earns the “very good” label for delivering on its core promise (speed) better than anyone else, while losing points on the daily-cap experience and the strategic uncertainty introduced by the Nvidia deal.

Frequently Asked Questions

Is Groq free?: Yes. Groq offers a free tier with no credit card required, providing 30 requests per minute, 6,000 tokens per minute, and 1,000 requests per day on most models. Paid Developer-tier pricing starts at $0.05 per million input tokens for the cheapest open-weight models.
What platforms does Groq support?: Groq is API-only. You access it through the GroqCloud REST API, an OpenAI-compatible chat-completions endpoint, and SDKs for Python, JavaScript/TypeScript, and other major languages. There is no desktop or mobile app.
How does Groq compare to OpenAI?: Groq runs open-weight models (Llama, Mixtral, Gemma) at multiples of OpenAI’s tokens-per-second on equivalent model classes, but it does not host GPT-5.5 or any proprietary frontier model. Use Groq for speed and cost on open models; use OpenAI when you need GPT-class capability.
Is Groq open source?: The LPU hardware and GroqCloud platform are proprietary. Groq publishes open-source SDKs and helper libraries on GitHub, but the core inference stack is closed.
Did Nvidia buy Groq?: Nvidia did not acquire Groq. In December 2025 the two companies signed a non-exclusive inference technology licensing agreement valued at approximately $20 billion; founder Jonathan Ross and several senior leaders moved to Nvidia, while Groq continues independently under CEO Simon Edwards.

#open-weight-models

Groq