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Groq vs Together AI vs Fireworks AI

GroqTogether AIFireworks AI
GroqGroq

Very fast LLM inference on custom LPU hardware.

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Together AITogether AI

Inference, fine-tuning, and GPU clusters for open models.

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Fireworks AIFireworks AI

Fast, production inference for open and custom models.

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Votes000
PricingFreemiumFreemiumFreemium
CategoryCodingCodingCoding
Tags
inferencellm-apilow-latencyopen-sourcehardware
inferencefine-tuninggpu-cloudopen-sourcellm-api
inferencellm-apifine-tuningenterpriselow-latency
Best for
  • Developers building latency-sensitive apps
  • Teams running AI agents
  • Voice and real-time product builders
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
  • Teams shipping production AI features
  • Companies hosting custom models
  • Builders of agents and compound AI systems
Pros
  • Exceptional inference speed on supported models
  • Competitive per-token pricing
  • OpenAI-compatible API is easy to adopt
  • Free tier with no credit card
  • Good fit for agents and real-time apps
  • Large catalog of open and open-weight models
  • Competitive per-token pricing
  • Fine-tuning with weight ownership
  • Dedicated GPU clusters for scale
  • OpenAI-compatible API
  • Optimized low-latency inference
  • Supports custom and fine-tuned models
  • Production features like function calling and structured output
  • OpenAI-compatible API
  • Dedicated deployments for consistent performance
Cons
  • Limited to a curated catalog of open models
  • No hosting of arbitrary custom weights
  • Model lineup changes over time
  • Corporate turbulence in 2026 (Nvidia deal, down round)
  • Free-tier rate limits are modest
  • Broad pricing surface across several product lines
  • You own quality and safety evaluation of open models
  • Dedicated clusters require commitment and planning
  • Less turnkey than closed frontier APIs
  • Model catalog and prices change over time
  • Best value is at production scale, not hobby use
  • Per-model pricing varies and needs modeling
  • You evaluate open-model quality and safety
  • Dedicated deployments add cost and planning
  • Less generous free usage than some rivals

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