Groq vs Fireworks AI
A detailed comparison to help you choose between Groq and Fireworks AI.
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Verdict: Fireworks AI wins
Fireworks AI scores 4.3/5, edging out Groq at 4.0/5. Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics.
Groq The fastest LLM inference in the world | Fireworks AI Fastest open-source model inference | |
|---|---|---|
| Rating | 4.0 (1 review) | 4.3 (1 review) |
| Pricing Model | usage-based | usage-based |
| Starting Price | Pricing varies | Pricing varies |
| Best For | Developers building latency-sensitive, high-volume applications — voice, extraction, classification, moderation — on open-weight models who want published speeds and rock-bottom token prices. | Developers and ML teams shipping open-weight models where serving latency and fine-tuning cost determine the product economics. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | api accessfree tier | api accessfree tier |
| Visit Groq → | Visit Fireworks AI → |
Groq
Pros
- + 800-1,000 t/s measured on GPT-OSS 20B, matching the published speed figure within about 15%
- + Per-model speed, price, context and rate limits are all published in one table — rare transparency
- + Document summarisation workload priced under $2 vs $10-20 on frontier APIs for the same task
- + Whisper transcription at $0.04/hr on turbo is the cheapest speech input I have priced
- + OpenAI-compatible API means switching cost is mostly a base URL
Cons
- - Nvidia's ~$20B asset deal (Dec 2025) left Groq restructuring as a neocloud; roadmap risk is real
- - Llama 3.3 70B and 3.1 8B appear priced publicly but 'Contact Sales / Enterprise' in the docs — unresolved conflict
- - 131K context ceiling is a fraction of what frontier APIs offer
- - Open-weight models lag frontier reasoning on my logic smoke tests
- - Self-serve catalogue is a handful of production language models, not a broad library
Fireworks AI
Pros
- + DeepSeek R1 Fast reached 80-95 output tokens/sec in my low-batch tests, close to the vendor's 90 tok/s serverless figure
- + Grammar-constrained decoding on DeepSeek V3 returned schema-valid JSON on all 200 extraction calls in my batch
- + LoRA SFT on a 7B-class model went from dataset upload to deployable adapter in under an hour, served at base-model pricing
- + Training price card is fully public: $0.50/1M tokens (LoRA SFT, <=16B) up to $10.00/1M (>300B), DPO double
- + On-demand GPU pricing is published per hour (H100/H200 $8, B200 $13, B300 $15) with zero charge for start-up time
Cons
- - Open models only — my scanned-forms extraction task needed a frontier fallback Fireworks does not offer
- - Same model prices differently across Standard/Priority/Fast paths, and third-party trackers disagree with the vendor blog
- - DeepSeek R1 model card lists serverless as unsupported while a Fast serverless deployment exists — documentation contradicts itself
- - Self-serve stops at shared serverless: R1 fine-tuning, QAT, region pinning and enterprise rate limits are sales-gated
- - Shared-serverless latency is load-dependent by the vendor's own statement, so demo numbers are not an SLA
Frequently Asked Questions
Which is better, Groq or Fireworks AI?▼
Fireworks AI scores higher at 4.3/5 vs 4.0/5 for Groq, making it the better-rated option in our comparison.
What is the difference between Groq and Fireworks AI?▼
Groq: Groq is an AI inference cloud built on its own LPU chips, giving developers and companies fast, low-cost access to run large language models and other AI models in their apps. Fireworks AI: Fireworks AI is an inference and fine-tuning platform for open models, used to serve and specialise them in production.
Is Groq or Fireworks AI more affordable?▼
Groq starts at €0/mo, Fireworks AI starts at €0/mo. Check the comparison table above for current pricing.
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