Groq vs Fireworks AI

Detailed side-by-side comparison to help you choose the right tool

Groq

🔴Developer

AI Model Hosting & Inference

AI inference cloud built on Groq's own LPU (Language Processing Unit) chips that serves open-weight LLMs, Whisper, and vision models at the lowest latency in the market, with an OpenAI-compatible API.

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Starting Price

$0
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Fireworks AI

🔴Developer

AI Model Hosting & Inference

Production inference platform for open-weight LLMs, multimodal models, and custom fine-tunes — known for very fast serving (FireAttention/FireOptimizer), reliable function calling, and JSON mode at low per-token prices.

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Starting Price

Per-million-token pricing per model (text models from ~$0.20/M up depending on size; image models per-image)
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Feature Comparison

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FeatureGroqFireworks AI
CategoryAI Model Hosting & InferenceAI Model Hosting & Inference
Pricing Plans3 listed plans3 listed plans
Starting Price$0Per-million-token pricing per model (text models from ~$0.20/M up depending on size; image models per-image)
Key Features
  • • Very low-latency LLM inference through GroqCloud
  • • OpenAI-compatible style developer workflows for chat and agents
  • • Support for popular open models such as Llama, Mixtral-style, and Whisper-class workloads as available
  • • High-Performance Inference Engine: Fireworks has built a custom inference engine optimized for maximum throughput and minimum latency on open-source models. The engine runs on globally distributed infrastructure using the latest GPU hardware. According to Fireworks case studies, Sourcegraph achieved latency reductions from 2 seconds to 350 milliseconds after migrating to the platform. Techniques like quantization with minimal quality degradation and speculative decoding are applied automatically, allowing developers to get production-grade performance without manual optimization.
  • • Advanced Fine-Tuning Pipeline: The fine-tuning system supports multiple advanced techniques beyond standard supervised fine-tuning, including reinforcement learning from human feedback, quantization-aware tuning that maintains quality while reducing model size, and adaptive speculation for faster generation. This allows teams to customize models for domain-specific tasks like code generation, customer support, or document analysis without needing dedicated ML engineers or training infrastructure.
  • • Enterprise-Grade Security and Compliance: Fireworks provides a comprehensive enterprise security posture with SOC2, HIPAA, and GDPR compliance certifications. The platform offers zero data retention policies ensuring that no customer data is stored after inference, bring-your-own-cloud deployment options for organizations with strict data residency requirements, and complete data sovereignty guarantees. This makes it suitable for regulated industries like healthcare and finance.

Groq - Pros & Cons

Pros

  • ✓Custom LPU silicon delivers tokens-per-second that is typically 5–10x faster than GPU baselines on open LLMs
  • ✓OpenAI-compatible API plus a generous free developer tier make adoption a base-URL change away
  • ✓Per-token pricing on Llama-class models is at or below the open-model market while latency stays predictably low

Cons

  • ✗Model catalog is curated, not exhaustive — niche fine-tunes are easier to find on Together or Fireworks
  • ✗No first-party fine-tuning service today, so custom models must be trained elsewhere and may not port to LPU
  • ✗Capacity for popular models can be rate-limited during demand spikes; dedicated/Enterprise mitigates but adds cost

Fireworks AI - Pros & Cons

Pros

  • ✓Reliable function calling, JSON mode, and parallel tool calls across the open-model catalog — table stakes for production agents
  • ✓FireFunction-V2 is purpose-built for tool-calling accuracy, materially beating generic Llama tool-use in agentic loops
  • ✓Three pricing tiers (serverless / dedicated GPU-hour / Enterprise) cover prototype-to-scale without rehosting

Cons

  • ✗Latency is good but typically not as low as Groq's LPU-based inference
  • ✗Per-token pricing is competitive but not always the cheapest — DeepSeek's official API or OpenRouter aggregation can undercut on specific models
  • ✗Serverless rate limits can surprise high-burst workloads and force an earlier-than-expected jump to dedicated deployments

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Review GroqReview Fireworks AI