Decision comparison
Groq vs Fireworks AI
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 when latency-critical voice assistants, real-time chat, or interactive agents need LPU-backed sub-50ms time-to-first-token behavior. Choose Fireworks AI for teams that need model breadth, training workflows, image generation, embeddings, or managed GPU capacity alongside inference. Both platforms provide usage-based inference, OpenAI-compatible APIs, function calling, JSON mode, batch discounts, and cached-input savings.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are model hosting platforms.
Quick Comparison
| Decision factor | Groq | Fireworks AI |
|---|---|---|
| Best For | Ultra-low-latency inference on supported open-source models with low per-token pricing, especially real-time chatbots, voice assistants, and interactive agentic applications. | Comprehensive AI platform with 100+ models, fine-tuning, dedicated GPUs, and image generation for experimentation through production-grade generative-AI workloads. |
| Pricing | Groq bills per token on GroqCloud, with a separate input and output rate for each model. GPT OSS 20B is $0.075 per 1M input tokens and $0.30 per 1M output; GPT OSS 120B is $0.15 and $0.60; Llama 4 Scout is $0.11 and $0.34; Qwen3 32B is $0.29 and $0.59; Llama 3.3 70B Versatile is $0.59 and $0.79; Llama 3.1 8B Instant is $0.05 and $0.08. Prompt caching halves the input rate on a cache hit, so GPT OSS 120B input falls from $0.15 to $0.075. Speech recognition is billed per hour transcribed: Whisper Large v3 Turbo at $0.04 and Whisper V3 Large at $0.111. Built-in tools bill separately, including Basic Search at $5 and Advanced Search at $8 per 1000 requests. Minimax M2.5 and Qwen3-VL 32B are enterprise-only. | Fireworks AI bills per token for serverless inference, with $1 in free credits for new accounts; per-model rates across its Standard, Priority and Fast serverless tiers are published in its documentation rather than on the pricing page. Embeddings are $0.008 per 1M input tokens up to 150M parameters, $0.016 from 150M to 350M, and $0.1 for Qwen3 8B. Managed training is priced per 1M training tokens, with LoRA SFT at $0.50 for models up to 16B, $3.00 from 16.1B to 80B, $6.00 from 80B to 300B and $10.00 above 300B; DPO and full-parameter tuning are multiples of those. On-demand GPU deployments from 1 September are $8.00 per hour for H100 and H200, $13.00 for B200, $15.00 for B300 and $20.00 for GB300, with a 1.5x premium on region-restricted deployments. |
| Inference Speed | Prominent; custom LPU delivers sub-50ms time-to-first-token, operating at 3-10x the speed of GPU platforms, with globally deployed LPU-based inference. | Fast GPU-based inference with competitive throughput, but sizable latency than Groq's LPU; FireAttention kernels optimize serving for open-source and custom models. |
| Model Catalog | Curated selection of optimized open-source models including Llama 3.1, 3.3, 4 Scout, Mixtral, Qwen3, Gemma, Whisper, and Orpheus. | 100+ models spanning open-source and proprietary options across text, code, and vision, including Llama, Mixtral, DeepSeek, Qwen, Whisper, Stable Diffusion, and FLUX. |
| Platform Capabilities | Provides batch inference, prompt caching with 50% input savings, Whisper speech-to-text, Orpheus text-to-speech, vision, JSON mode, and built-in search, browser, and code tools. | Provides serverless and dedicated inference, batch inference and cached-input discounts of 50%, LoRA or full-parameter fine-tuning, embeddings, speech-to-text, and image generation. |
| Developer Ecosystem | Offers OpenAI-compatible endpoints, function calling, and tool use. Official Apache-2.0 Python SDK has 610 stars; latest v1.7.0 released August 26, 2026. | Offers OpenAI-compatible API endpoints, function calling, and JSON mode. Official Apache-2.0 Python SDK has 9 stars; latest v1.2.9 released August 12, 2026. |
Groq
- Best For:
- Ultra-low-latency inference on supported open-source models with low per-token pricing, especially real-time chatbots, voice assistants, and interactive agentic applications.
- Pricing:
- Groq bills per token on GroqCloud, with a separate input and output rate for each model. GPT OSS 20B is $0.075 per 1M input tokens and $0.30 per 1M output; GPT OSS 120B is $0.15 and $0.60; Llama 4 Scout is $0.11 and $0.34; Qwen3 32B is $0.29 and $0.59; Llama 3.3 70B Versatile is $0.59 and $0.79; Llama 3.1 8B Instant is $0.05 and $0.08. Prompt caching halves the input rate on a cache hit, so GPT OSS 120B input falls from $0.15 to $0.075. Speech recognition is billed per hour transcribed: Whisper Large v3 Turbo at $0.04 and Whisper V3 Large at $0.111. Built-in tools bill separately, including Basic Search at $5 and Advanced Search at $8 per 1000 requests. Minimax M2.5 and Qwen3-VL 32B are enterprise-only.
- Inference Speed:
- Prominent; custom LPU delivers sub-50ms time-to-first-token, operating at 3-10x the speed of GPU platforms, with globally deployed LPU-based inference.
- Model Catalog:
- Curated selection of optimized open-source models including Llama 3.1, 3.3, 4 Scout, Mixtral, Qwen3, Gemma, Whisper, and Orpheus.
- Platform Capabilities:
- Provides batch inference, prompt caching with 50% input savings, Whisper speech-to-text, Orpheus text-to-speech, vision, JSON mode, and built-in search, browser, and code tools.
- Developer Ecosystem:
- Offers OpenAI-compatible endpoints, function calling, and tool use. Official Apache-2.0 Python SDK has 610 stars; latest v1.7.0 released August 26, 2026.
Fireworks AI
- Best For:
- Comprehensive AI platform with 100+ models, fine-tuning, dedicated GPUs, and image generation for experimentation through production-grade generative-AI workloads.
- Pricing:
- Fireworks AI bills per token for serverless inference, with $1 in free credits for new accounts; per-model rates across its Standard, Priority and Fast serverless tiers are published in its documentation rather than on the pricing page. Embeddings are $0.008 per 1M input tokens up to 150M parameters, $0.016 from 150M to 350M, and $0.1 for Qwen3 8B. Managed training is priced per 1M training tokens, with LoRA SFT at $0.50 for models up to 16B, $3.00 from 16.1B to 80B, $6.00 from 80B to 300B and $10.00 above 300B; DPO and full-parameter tuning are multiples of those. On-demand GPU deployments from 1 September are $8.00 per hour for H100 and H200, $13.00 for B200, $15.00 for B300 and $20.00 for GB300, with a 1.5x premium on region-restricted deployments.
- Inference Speed:
- Fast GPU-based inference with competitive throughput, but sizable latency than Groq's LPU; FireAttention kernels optimize serving for open-source and custom models.
- Model Catalog:
- 100+ models spanning open-source and proprietary options across text, code, and vision, including Llama, Mixtral, DeepSeek, Qwen, Whisper, Stable Diffusion, and FLUX.
- Platform Capabilities:
- Provides serverless and dedicated inference, batch inference and cached-input discounts of 50%, LoRA or full-parameter fine-tuning, embeddings, speech-to-text, and image generation.
- Developer Ecosystem:
- Offers OpenAI-compatible API endpoints, function calling, and JSON mode. Official Apache-2.0 Python SDK has 9 stars; latest v1.2.9 released August 12, 2026.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Groq | Fireworks AI |
|---|---|---|
| GitHub commits, 90d(Developer adoption) | 25 | 44 |
| GitHub stars(Developer adoption) | 610 | 9 |
| Search interest(Market interest) | 1 | 6 |
| Hacker News mentions, 90d(Community interest) | 26 | 0 |
| Hugging Face downloads(Product adoption) | 757 | 11.1k |
| Hugging Face likes(Product adoption) | 470 | 341 |
| npm weekly downloads(Developer adoption) | 673.9k | Not available |
| PyPI weekly downloads(Developer adoption) | 3.6M | 275.6k |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Groq
September 14, 2026Package vulnerabilities
PyPI · groq@1.7.0 · npm · groq-sdk@1.6.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Fireworks AI
September 14, 2026Package vulnerabilities
PyPI · fireworks-ai@1.2.11
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
Groq
Feature Comparison
| Feature | Groq | Fireworks AI |
|---|---|---|
| Core Capabilities | ||
| Inference Speed | Prominent; custom LPU delivers sub-50ms time-to-first-token, operating at 3-10x the speed of GPU platforms | Fast GPU-based inference with competitive throughput, but sizable 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 | Per-token serverless rates published in its documentation; embeddings from $0.008/1M |
| 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 and H200 at $8/hr, B200 at $13/hr, B300 at $15/hr, and GB300 at $20/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 |
Core Capabilities
Inference Speed
Model Catalog
Fine-Tuning
Image Generation
API Compatibility
Hardware Architecture
Pricing & Plans
Small Model Pricing
Large Model Pricing
Batch Processing
Prompt Caching
Dedicated GPU Pricing
Free Tier
Which to choose
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 when latency-critical voice assistants, real-time chat, or interactive agents need LPU-backed sub-50ms time-to-first-token behavior. Choose Fireworks AI for teams that need model breadth, training workflows, image generation, embeddings, or managed GPU capacity alongside inference. Both platforms provide usage-based inference, OpenAI-compatible APIs, function calling, JSON mode, batch discounts, and cached-input savings.
Best-fit scenarios
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. It is particularly suited to supported Llama, Mixtral, Qwen3, and Gemma workloads needing LPU-based inference, speech, or built-in agent tools.
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. Its LoRA and full-parameter training, embeddings, serverless inference, and H100/H200/B200 deployment options fit broader production AI programs.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Is Groq really faster than Fireworks AI?
Yes. Groq's custom LPU hardware delivers 3-10x reduced latency compared to GPU-based platforms including Fireworks AI, with sub-50ms time-to-first-token for most models.
Can I fine-tune models on Groq?
No. Groq is inference-only. For fine-tuning, use Fireworks AI which offers LoRA fine-tuning from $0.50 per million tokens.
Which platform is cheaper for high-volume inference?
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.
Do both platforms support the OpenAI API format?
Yes. Both Groq and Fireworks AI expose OpenAI-compatible REST APIs. You can switch between them with minimal code changes using standard SDKs.