Fireworks AI

Freemium

Fast LLM inference with serverless API, batch processing, and dedicated GPU deployments

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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.

Fireworks AI demo video

Watch Fireworks AI's official demo to see Fireworks AI in action before reading our full review.

Official video by Fireworks AI via YouTube, embedded for reference. ToolChase does not host or claim this video.

⚡ Quick Verdict

Best for

Production teams that need fast serverless LLM inference plus fine-tuning and dedicated GPU options on one platform

Not ideal for

Users who just want the single cheapest per-token API

Starting price

$0.10/M tokens serverless · $2.90/hr A100 · Batch 50% off

Free plan

$1 starter credit, enough for initial testing

Key strength

Full-stack platform with fast inference, batch, fine-tuning, and dedicated GPUs

Limitation

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

✅ Pricing verified May 2026 · ✅ Independently reviewed · ✅ Scoring methodology

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.