Groq's main differentiator is hardware: it built its own chip, the LPU, specifically for LLM inference rather than running models on general-purpose GPUs the way almost every other inference provider does. That custom silicon translates into token generation speeds that are often dramatically faster than GPU-based providers for the same model, which matters a lot for latency-sensitive applications like voice agents or interactive tools where a user is waiting on the response.
It offers an API for running popular open models, including Llama and DeepSeek variants, at that higher speed, competing with Together AI and Fireworks AI on raw inference performance rather than on model breadth or fine-tuning capability. The speed advantage comes with less flexibility than a general GPU cloud, since it's built around a fixed set of supported models rather than arbitrary custom deployments.
Pricing is usage-based and competitive with other inference providers, and Groq has carved out a specific reputation among developers building latency-sensitive applications who've found GPU-based inference just a bit too slow for their use case.