Fireworks AI vs Groq

Text, vision, audio, image, and embedding models, including recent releases such as Kimi K3, GLM 5.2, DeepSeek V4, MiniMax M3, and Qwen3.7 Plus

Industry-leading throughput and latency with fast inference engine

SFT, DPO, and reinforcement fine-tuning of models up to 1T+ parameters, from guided runs through writing your own trainer and RL loop

Drop-in replacement for closed-model APIs - change the base URL to migrate, with the same SFT data format for fine-tuning

H100, H200, B200, B300, and GB300 deployments with per-second billing and autoscaling

Async bulk inference jobs run at scale for less than standard serverless requests

Custom Language Processing Units run inference, with 256 LPUs and 128 GB of on-chip SRAM per rack

Drop-in replacement for the OpenAI API at api.groq.com/openai/v1, including a Responses API

GroqMetal bare-metal infrastructure, GroqCore inference stack, and GroqAssured enterprise governance, each including the layers below

Text generation plus speech-to-text, text-to-speech, OCR and image recognition, and content moderation models

Compound systems that combine models with web search, code execution, remote MCP tools, and Google Workspace connectors

Performance, flex, and batch processing tiers, plus prompt caching, structured outputs, and LoRA inference