Ultra-fast LLM inference platform
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Reviews onG2
1 reviews trackedThe Bottom Line
Entry price
Paid plans only
Biggest pro
Fastest inference speeds available, often 500-1000+ tokens per second on supported models
Biggest con
No proprietary frontier model, relies entirely on open-source model ecosystem
TL;DR - Groq
- AI inference platform using custom LPU chips for the fastest open-source model execution available
- Pay-per-token pricing starting at $0.05/M input tokens, with batch and caching discounts up to 50%
- Best for developers and teams who need low-latency inference on open-source LLMs without managing infrastructure
Pricing: pay_per_use
Best for: Enterprises & pros
What is Groq?
Groq is an AI inference platform built on its custom Language Processing Unit (LPU), a purpose-built semiconductor designed for fast, low-latency model execution. GroqCloud provides developers with an OpenAI-compatible API to run large language models like Llama 4, Qwen3, and GPT-OSS, along with speech models like Whisper and text-to-speech via Orpheus. The LPU architecture uses onboard SRAM, direct chip-to-chip connectivity, and static scheduling to deliver deterministic performance without batching delays. Over 3 million developers use Groq, with enterprise options including on-premises deployment via GroqRack. Pricing is purely usage-based with per-token billing and no monthly subscription fees.
Available on: Web
Pros & Cons
Pros
- Fastest inference speeds available, often 500-1000+ tokens per second on supported models
- Transparent per-token pricing with no monthly fees or minimum spend
- Drop-in replacement for OpenAI API with minimal integration effort
- Wide model selection spanning LLMs, speech recognition, and text-to-speech
- Prompt caching and batch API cut costs significantly for high-volume workloads
- Enterprise deployment flexibility with cloud, on-premises, and hybrid options
Cons
- No proprietary frontier model, relies entirely on open-source model ecosystem
- Model selection is narrower than major cloud providers like AWS Bedrock or Azure AI
- Text-to-speech limited to a small number of languages and voices
- No built-in fine-tuning or model customization capabilities
- Enterprise on-premises pricing requires custom sales engagement with no public rates
Ratings Across the Web
5(1 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Custom LPU inference chip delivering sub-second latency on large language modelsOpenAI-compatible API requiring minimal code changes to migrate existing applicationsSupport for 10+ open-source LLMs including Llama 4, Qwen3, and GPT-OSS familiesWhisper-based automatic speech recognition at up to 228x real-time speedText-to-speech generation via Canopy Labs Orpheus models in multiple languagesPrompt caching with 50% input token discount for repeated contextBatch API for asynchronous large-scale workloads at 50% reduced costBuilt-in compound tools: web search, code execution, and browser automationMulti-region cloud deployment with on-premises GroqRack option for enterprisesLinear usage-based pricing with no hidden fees or minimum commitments
Pricing Plans
Free Tier
Free
- Rate-limited access to all models
- OpenAI-compatible API
- Community support
Pay-as-you-go
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- Llama 3.1 8B from $0.05/M input tokens
- Llama 4 Scout from $0.11/M input tokens
- Qwen3 32B from $0.29/M input tokens
- Whisper transcription from $0.04/hr
- Prompt caching at 50% discount
- Batch API at 50% discount
Enterprise
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- Custom rate limits and SLAs
- On-premises GroqRack deployment
- Dedicated support
- Volume discounts
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Groq FAQ
What is a Language Processing Unit (LPU) and how does it differ from a GPU?
The LPU is Groq's custom-designed chip built specifically for AI inference, not training. Unlike GPUs that use shared memory hierarchies and caches, the LPU uses hundreds of megabytes of onboard SRAM with direct chip-to-chip connectivity and static scheduling. This eliminates batching delays and delivers deterministic, low-latency performance for sequential token generation.
How does Groq pricing work?
Groq uses pure pay-per-token pricing with no monthly subscription. You pay per million input and output tokens, with rates varying by model — for example, Llama 3.1 8B costs $0.05/$0.08 per million input/output tokens, while larger models like Llama 3.3 70B cost $0.59/$0.79. Prompt caching and batch API each offer 50% discounts.
Can I use Groq as a drop-in replacement for OpenAI?
Yes. Groq's API is OpenAI-compatible, so you typically only need to change the base URL and API key in your existing code. Most OpenAI SDK features including function calling, streaming, and JSON mode are supported.
What models are available on Groq?
Groq supports open-source LLMs including Llama 4 Scout and Maverick, Llama 3.3 70B, Qwen3 32B, GPT-OSS 20B and 120B, and Kimi K2. For audio, it offers Whisper v3 Large and Turbo for transcription, and Canopy Labs Orpheus for text-to-speech.
Does Groq offer on-premises deployment?
Yes, through GroqRack — a purpose-built inference appliance for enterprises that need to run models in their own data centers. GroqRack uses the same LPU technology as GroqCloud. Pricing and availability require contacting the Groq sales team.
What are the rate limits on Groq's free tier?
Groq offers free-tier access with rate limits that vary by model. Exact limits are displayed in the GroqCloud developer console upon signup. Paid usage removes or significantly raises these limits, and enterprise plans offer fully custom rate limits.
Source: groq.com