Groq Overview, Features & Pricing (2026)
Overview
Groq provides an LPU-based inference stack for production AI, optimized for low-latency model serving. It targets engineering and ML teams deploying LLMs and other demanding models in data-center or edge environments. The platform pairs custom inference hardware with a managed console to run and monitor model workloads. Teams adopt it to shorten response times and reduce operational complexity for live services.
Use cases
- Real-time conversational agents and chatbots powered by LLMs.
- Low-latency model serving for streaming analytics and decisioning.
- Accelerated inference for image generation and vision models.
- Edge or data-center deployments requiring consistent response times.
How it helps
- Delivers faster model responses to improve user experience and throughput.
- Reduces inference operating costs through efficient hardware utilization.
- Provides predictable latency for production workloads.
- Simplifies deployment and monitoring with a managed console.
Key features
- Low-latency LPU runtime for consistent inference performance.
- OpenAI-compatible API for easy integration with existing LLM tooling.
- Managed console for deployment, monitoring, and scaling.
- Cost-efficient inference through hardware-optimized execution for Data Analytics and other workloads.
- Support for on-premise and cloud data-center deployments to meet operational needs.
Pricing
Paid plans are available for inference. Check the official site for current details.
Why to choose Groq?
Groq pairs purpose-built LPU silicon with a managed console to prioritize low-latency, predictable inference for production models.
