# Fireworks: what AI models say (September 2026)

Source: Memetik Index, https://www.memetik.ai/vendors/fireworks
Website: https://fireworks.ai

Fireworks was named in 43 of 50 AI answers across 1 category. Best position: Inference hosting, 86% answer share, ranked #3, named first in 8%.

## What Fireworks is

Fireworks provides APIs and infrastructure for developers and teams that train, deploy and run AI models, including AI coding workloads. It supports open models and versions produced through its own training paths.

Teams can start with guided training runs or use custom training logic. Fireworks deploys each training checkpoint to production in seconds, then serves models through its inference engine. Its serverless inference uses per-token pricing and supports APIs compatible with OpenAI and Anthropic. Priority and Fast serverless options are available, alongside dedicated on-demand deployments and reserved capacity.

Pricing: Serverless inference is priced per token, with Priority and Fast options.
(From Fireworks's own website, read 2026-09-02.)

## By category

| Category | Rank | Answer share | Named first | Models |
|---|---|---|---|---|
| Inference hosting (https://www.memetik.ai/index/inference-hosting) | #3 of 18 | 86% | 8% | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |

## What the models said

> "How I’d decide by workload Prototype / evals / low or bursty traffic Start with Together serverless or Fireworks."
> — ChatGPT, Inference hosting

> "If your main goal is production inference with minimal ops, Together AI and Fireworks AI are the strongest alternatives to test first."
> — Perplexity, Inference hosting

> "Together AI is often recommended alongside Fireworks as a top “first choice” for hosted open models, with APIs and pricing designed for production and cost‑efficient scaling."
> — Sonar Reasoning Pro, Inference hosting

> "For a high-volume, latency-sensitive LLM product, Together, Fireworks, or a dedicated deployment is usually a better fit."
> — GPT-5.6 Luna, Inference hosting