Ultra-fast LLM inference platform
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Reviews onG2
1 review 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, Qwen, 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
Pricing checked Jul 11, 2026
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
null
- Custom rate limits and SLAs
- On-premises GroqRack deployment
- Dedicated support
- Volume discounts
Reviews
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Groq FAQ
How does Groq achieve its ultra-fast inference speeds for large language models?
Groq achieves its speed through a custom Language Processing Unit (LPU) architecture, which utilizes onboard SRAM, direct chip-to-chip connectivity, and static scheduling. This design delivers deterministic performance and eliminates batching delays, resulting in very high token generation rates.
Which teams would benefit most from using Groq?
Teams requiring extremely low-latency AI inference for applications like real-time chatbots, interactive AI experiences, or high-throughput language processing will find Groq most beneficial. It is also well-suited for developers looking for a drop-in replacement for the OpenAI API with enhanced speed.
What kind of models can be deployed on the Groq platform?
Groq supports a range of large language models such as Llama, Qwen, and GPT-OSS, along with speech models like Whisper for recognition and Orpheus for text-to-speech. These models are accessible via an OpenAI-compatible API.
How is Groq priced for developers?
Groq uses a purely usage-based pricing model with per-token billing for its services. There are no monthly subscription fees or minimum spend requirements, making it transparent for varying workloads.
Can Groq be integrated into existing applications that use the OpenAI API?
Yes, Groq provides an OpenAI-compatible API, allowing for minimal integration effort. This makes it a straightforward drop-in replacement for applications already utilizing the OpenAI API.
What are the main trade-offs when choosing Groq for AI model deployment?
A primary trade-off is that Groq does not offer proprietary frontier models, relying instead on the open-source model ecosystem. Additionally, it lacks built-in fine-tuning or model customization capabilities, and its text-to-speech options are limited in languages and voices.
How does Groq compare to Anyscale for AI model deployment?
Groq specializes in ultra-fast, low-latency inference for large language models using its custom LPU architecture, often achieving 500-1000+ tokens per second. Anyscale offers a broader platform for building, deploying, and managing AI applications, including distributed computing frameworks.
Source: groq.com