Fireworks AI
FreemiumFast LLM inference with serverless API, batch processing, and dedicated GPU deployments
What is Fireworks AI?
Fireworks AI is a production-focused LLM inference platform built by former Meta PyTorch and Google engineers, designed to deliver the fastest serverless inference for open-weight models with a developer experience that is simpler than building your own vLLM cluster. Fireworks hosts 50+ open models including the full Llama family, DeepSeek R1, Qwen, Mixtral, Gemma, Whisper, and several custom Fireworks fine-tunes optimized for speed, and it consistently ranks alongside Groq and Cerebras for the fastest tokens-per-second in the industry. Pricing comes in three flavors. Serverless inference starts at $0.10 per million tokens for models under 4B parameters and scales up for larger models. Dedicated GPU deployments are available for teams that need guaranteed capacity — on-demand pricing is $2.90/hour for A100 80GB, $6/hour for H100 or H200, and $9/hour for B200, the latest NVIDIA hardware. Batch inference runs at 50% of serverless pricing for both input and output tokens on supported models, which is ideal for offline document processing, embeddings generation, and data labeling. Fireworks also includes fine-tuning infrastructure, LoRA adapter hosting, multi-modal support for vision models, and function calling — making it a genuinely full-stack inference platform rather than just a token API. The $1 starter credit lets new developers test across 50+ models without a commitment.
⚡ Quick Verdict
Production teams that need fast serverless LLM inference plus fine-tuning and dedicated GPU options on one platform
Users who just want the single cheapest per-token API
$0.10/M tokens serverless · $2.90/hr A100 · Batch 50% off
$1 starter credit — enough for initial testing
Full-stack platform with fast inference, batch, fine-tuning, and dedicated GPUs
Per-token rates slightly higher than Groq for Llama 3.3 70B
Bottom line: Fireworks AI scores 4.4/5 — the best pick when you need more than just inference. Use serverless for development, batch API for bulk jobs, and dedicated GPUs for predictable production traffic.
Pricing
Serverless — From $0.10 per million tokens: Pay-per-token for models under 4B parameters. Larger models scale proportionally. Pricing competitive with Together AI and OpenRouter.
Batch API — 50% off serverless: Both input and output tokens priced at half of serverless rates for non-real-time bulk processing.
Dedicated GPUs (on-demand): A100 80GB at $2.90/hour · H100 / H200 at $6/hour · B200 at $9/hour.
Fine-tuning: LoRA adapter hosting and managed fine-tuning for Llama and other open models with usage-based pricing.
$1 starter credit: Enough to test across 50+ models before committing.
Key Features
- 50+ open models including full Llama and Llama 4 family
- Serverless pricing from $0.10 per million tokens
- Batch API at 50% discount for offline workloads
- Dedicated A100, H100, H200, and B200 GPU deployments
- Managed fine-tuning with LoRA adapter hosting
- Vision and multimodal model support
- Function calling and structured outputs
- OpenAI-compatible API format
Pros & Cons
Pros
- One of the fastest inference providers for open-weight models
- Batch API 50% discount is unusually generous
- Dedicated GPU option with NVIDIA B200 access
- Full-stack platform — inference plus fine-tuning plus vision
Cons
- Serverless per-token prices slightly higher than Groq on some models
- $1 starter credit is much smaller than Anyscale's $100
- Dedicated GPU pricing adds up quickly for 24/7 workloads
FAQ
How does Fireworks compare to Together AI?
Both are full-stack LLM inference platforms with similar model catalogs. Together AI has a larger model selection and slightly simpler fine-tuning flow. Fireworks tends to win on raw inference speed for popular models and offers the 50% batch API discount that Together does not match. Fireworks is often chosen for speed and batch economics; Together is chosen for catalog breadth.
Is Fireworks really as fast as Groq?
Close, but not identical. Groq's LPU hardware generally wins on peak tokens-per-second for small models like Llama 3.1 8B. Fireworks wins on some larger models and offers more flexibility (fine-tuning, batch, vision) that Groq does not.
What is the batch API discount?
Fireworks' batch API lets you submit non-real-time bulk inference jobs at exactly 50% of serverless pricing for both input and output tokens. Jobs complete within 24 hours. For teams processing millions of documents or generating large datasets, this is a dramatic cost saving compared to real-time inference.
Can I fine-tune Llama on Fireworks?
Yes. Fireworks offers managed fine-tuning for Llama and other open models, with LoRA adapter hosting so you can serve dozens of custom fine-tunes cheaply on the same base model. Strong advantage for multi-tenant apps.
What GPUs can I rent dedicated?
Fireworks' dedicated GPU deployments include A100 80GB at $2.90/hour, H100 and H200 at $6/hour, and B200 (NVIDIA's latest Blackwell generation) at $9/hour. Deployments include managed vLLM serving, auto-scaling, and monitoring.
Does Fireworks support function calling?
Yes. Fireworks supports OpenAI-compatible function calling on Llama 3.1/3.3, Llama 4, Firefunction (Fireworks' custom fine-tune for function calling), and several other models trained for tool use. The format matches the OpenAI tools API.
📋 Good to know
Sign up at fireworks.ai, generate an API key, and point your OpenAI SDK at api.fireworks.ai/inference/v1. Dedicated deployments via the dashboard.
Fireworks does not train on your data. Enterprise agreements and HIPAA-eligible deployments available.
Start with serverless, move to dedicated GPUs when you need guaranteed capacity, use batch API for offline workloads.
Low — OpenAI-compatible format with solid documentation and Python SDK.