Groq wins on inference speed and pricing for supported models; Fireworks AI wins on platform completeness with fine-tuning, 100+ models, and image generation
| Feature | Groq | Fireworks AI |
|---|---|---|
| Inference Speed: Groq wins with custom LPU hardware delivering 3-10x lower latency than GPU-based platforms | — | — |
| Model Selection: Fireworks AI wins with 100+ models versus Groq's curated catalog of optimized models | — | — |
| Fine-tuning: Fireworks AI wins with LoRA fine-tuning from $0.50/1M tokens; Groq offers no fine-tuning | — | — |
| Pricing: Groq wins with Llama 3.1 8B at $0.05/1M input tokens, 50-75% cheaper than Fireworks AI equivalents | Groq uses pay-per-token pricing for LLM inference on custom LPU hardware. Llama 3.1 8B: $0.05/$0.08 per 1M input/output tokens. Llama 3.3 70B: $0.59/$0.79 per 1M tokens. Llama 4 Scout: $0.11/$0.34 per 1M tokens. Qwen3 32B: $0.29/$0.59 per 1M tokens. Whisper v3: $0.04-$0.111/hour. Batch API: 50% discount. Prompt caching: 50% savings on cached input. Built-in tools: Basic Search $5/1K requests, Advanced Search $8/1K requests. | Fireworks AI uses pay-per-token serverless pricing with $1 free credits for new accounts. Models <4B: $0.10/1M tokens. 4B-16B: $0.20/1M tokens. >16B: $0.90/1M tokens. MoE 0-56B: $0.50/1M tokens. DeepSeek V3: $0.56/$1.68 per 1M input/output. Cached input: 50% discount. Batch inference: 50% discount. Fine-tuning LoRA SFT: $0.50-$10.00/1M training tokens by model size. On-demand GPU: H100 $6.00/hr, B200 $9.00/hr. Image generation: FLUX.1 Kontext Pro $0.04/image. Embeddings from $0.008/1M tokens. |
| Image Generation: Fireworks AI wins with FLUX models at $0.04/image; Groq has no image support | — | — |
| Dedicated GPUs: Fireworks AI wins with H100 at $6/hr and B200 at $9/hr; Groq uses shared infrastructure | — | — |
| Feature | Groq | Fireworks AI |
|---|---|---|
| Core Capabilities | ||
| Inference Speed | Industry-leading; custom LPU delivers sub-50ms time-to-first-token, 3-10x faster than GPU platforms | Fast GPU-based inference with competitive throughput, but higher latency than Groq's LPU |
| Model Catalog | Curated selection of optimized open-source models including Llama 3.1, 3.3, 4 Scout, and Mixtral | 100+ models spanning open-source and proprietary options across text, code, and vision |
| Fine-Tuning | Not available; Groq is an inference-only platform with no training capabilities | LoRA fine-tuning from $0.50/1M tokens, full fine-tuning supported for custom model training |
| Image Generation | Not supported; Groq focuses exclusively on text-based LLM inference | FLUX models at $0.04 per image for text-to-image generation workloads |
| API Compatibility | OpenAI-compatible REST API; works with standard OpenAI Python SDK | OpenAI-compatible REST API; works with standard OpenAI Python SDK |
| Hardware Architecture | Proprietary LPU (Language Processing Unit) ASIC designed for sequential token generation | NVIDIA H100 and B200 GPUs with optimized inference serving stack |
| Pricing & Plans | ||
| Small Model Pricing | Llama 3.1 8B: $0.05 input / $0.08 output per 1M tokens | Sub-4B models at $0.10/1M, 4B-16B models at $0.20/1M tokens |
| Large Model Pricing | Llama 3.3 70B: $0.59 input / $0.79 output per 1M tokens | 16B+ models at $0.90 per 1M tokens for serverless inference |
| Batch Processing | Batch API with 50% discount off standard rates for non-real-time workloads | Standard API rates; no dedicated batch processing discount tier |
| Prompt Caching | 50% discount on cached prompt prefixes for repeated context | No explicit prompt caching discount; standard per-token pricing applies |
| Dedicated GPU Pricing | Not available; all inference runs on shared LPU infrastructure | H100 at $6/hr, B200 at $9/hr for dedicated compute with guaranteed capacity |
| Free Tier | Free tier available with rate limits for evaluation and development | $1 in free credits for new accounts to evaluate the platform |
Inference Speed
Model Catalog
Fine-Tuning
Image Generation
API Compatibility
Hardware Architecture
Small Model Pricing
Large Model Pricing
Batch Processing
Prompt Caching
Dedicated GPU Pricing
Free Tier
Groq wins on inference speed and pricing for supported models; Fireworks AI wins on platform completeness with fine-tuning, 100+ models, and image generation
Choose Groq if:
Choose Groq for latency-critical applications like real-time chatbots, voice assistants, and interactive AI tools where sub-100ms response times and competitive per-token pricing matter most
Choose Fireworks AI if:
Choose Fireworks AI when you need fine-tuning, image generation, dedicated GPU deployments, or access to 100+ models on a single comprehensive platform
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes. Groq's custom LPU hardware delivers 3-10x lower latency than GPU-based platforms including Fireworks AI, with sub-50ms time-to-first-token for most models.
No. Groq is inference-only. For fine-tuning, use Fireworks AI which offers LoRA fine-tuning from $0.50 per million tokens.
Groq is typically cheaper for pure inference. Llama 3.1 8B costs $0.05/$0.08 per million tokens on Groq versus $0.10-$0.20 on Fireworks AI, and the Batch API adds another 50% discount.
Yes. Both Groq and Fireworks AI expose OpenAI-compatible REST APIs. You can switch between them with minimal code changes using standard SDKs.