Capability · Framework — orchestration

Fireworks AI SDK

Fireworks AI runs one of the fastest hosted inference services for open-source LLMs. It's known for custom FireAttention kernels, LoRA hot-swapping (serve many fine-tunes from one base model), function calling on open models, and aggressive batching. The Python / TypeScript SDK follows the OpenAI interface, and Fireworks sells both serverless per-token and dedicated GPU deployments.

Framework facts

Category: orchestration
Language: Python / TypeScript
License: Apache-2.0 (SDK) / Proprietary SaaS
Repository: https://github.com/fw-ai/fireworks-python

Install

pip install fireworks-ai
# or
npm install fireworks-ai

Quickstart

import fireworks.client

fireworks.client.api_key = 'FW_KEY'
resp = fireworks.client.ChatCompletion.create(
    model='accounts/fireworks/models/llama-v3p3-70b-instruct',
    messages=[{'role':'user','content':'hi'}],
)
print(resp.choices[0].message.content)

Alternatives

Together AI
Groq
OpenRouter

Frequently asked questions

What is LoRA hot-swapping?

Fireworks can serve hundreds of fine-tuned LoRA adapters on top of a single base-model GPU instance, picking the right adapter per request. This makes multi-tenant fine-tunes cheap — you don't need a GPU per fine-tune.

Fireworks or Groq?

Groq is ultra-low-latency on a small catalogue of models running on their custom LPU hardware. Fireworks covers a broader catalogue on GPUs with excellent latency. Use Groq for real-time voice, Fireworks for everything else.

Sources

Fireworks AI docs — accessed 2026-04-20
Fireworks Python SDK — accessed 2026-04-20