---
title: Fireworks AI
slug: fireworks-ai
url: "https://toolweight.com/options/fireworks-ai"
homepage: "https://fireworks.ai"
categories: llm-apis
last_verified: 2026-01-15
license: CC-BY-4.0
---

# Fireworks AI

> Fast open-weight inference with strong structured-output support

Fireworks competes with Together on the same ground, serverless open-weight inference, dedicated deployments, fine-tuning, and differentiates on latency engineering and constrained decoding, with grammar and JSON-schema enforcement that is more reliable than most hosts. A reasonable default when your open-weight workload depends on output that must parse every time.

## Identity

|  |  |
| --- | --- |
| Name | Fireworks AI |
| Company | Fireworks AI |
| One-liner | Fast open-weight inference with strong structured-output support |
| Site | https://fireworks.ai |
| Docs | https://docs.fireworks.ai |
| Founded | 2022 |
| Funding | Venture-backed |
| Open source | No |
| Brand | https://toolweight.com/vendors/Fireworks AI |
| Compared in | 1 |

## Where it is compared

### [LLM APIs](https://toolweight.com/compare/llm-apis)
Ranked **#10 of 18** on default weights.
| Field | Value | Confidence | Verified | Source | Note |
| --- | --- | --- | --- | --- | --- |
| Flagship model | Open-weight catalogue, DeepSeek, Kimi K2, Qwen3, Llama 4 | Vendor-claimed | 2026-01-15 | https://docs.fireworks.ai/models/overview | - |
| Context window | - | Unknown | - | - | Per-model. |
| Max output | - | Unknown | - | - | Per-model. |
| Image input | ◐ | Inferred | 2026-01-15 | - | - |
| Audio in/out | ◐ | Inferred | 2026-01-15 | - | Transcription models are offered; no native speech output. |
| Open weights | ● | Inferred | 2026-01-15 | - | - |
| OpenAI-compat API | ● | Vendor-claimed | 2026-01-15 | https://docs.fireworks.ai/tools-sdks/openai-compatibility | - |
| TTFT p50 | - | Unknown | - | - | - |
| Output tok/s | - | Unknown | - | - | - |
| $/M input | - | Unknown | - | - | Per-model tiers by parameter count, comparable to Together. |
| $/M output | - | Unknown | - | - | - |
| $/M cache read | - | Unknown | - | - | - |
| Batch discount | - | Unknown | - | - | - |
| Tool use | ◐ | Inferred | 2026-01-15 | - | - |
| Schema output | ● | Vendor-claimed | 2026-01-15 | https://docs.fireworks.ai/structured-responses/structured-response-formatting | Grammar-based and JSON-schema constrained decoding, more reliable than most open-weight hosts. |
| Effort control | ○ | Inferred | 2026-01-15 | - | - |
| Computer use | ○ | Inferred | 2026-01-15 | - | - |
| MCP support | ○ | Inferred | 2026-01-15 | - | - |
| Cache TTL | - | Unknown | - | - | - |
| Continuity policy | Same shape as Together: a published deprecations list, serverless models retired on weeks of notice when demand falls, and dedicated deployments as the stable option. Open weights throughout mean no retirement is terminal, only inconvenient. | Inferred | 2026-01-15 | - | - |
| Notice period | - | Unknown | - | - | - |
| Pinnable versions | ● | Inferred | 2026-01-15 | - | - |
| Zero retention | - | Unknown | - | - | No contractual zero-retention programme was located, and this column's bar includes abuse-monitoring logs. Regraded from an unevidenced yes, absence of a published retention policy is not evidence of a favourable one. |
| Trains on your data | ○ | Inferred | 2026-01-15 | - | - |
| AU region | ○ | Inferred | 2026-01-15 | - | - |
| API since | 2023 | Inferred | - | - | - |
| Positioning | Low-latency open-weight inference with strict structured output | Inferred | - | - | - |

**Verdict.** Hard to separate from Together on paper; the practical split is that Fireworks invests more in constrained decoding and latency tuning. If your pipeline breaks when a response fails to parse, its grammar enforcement is the more reliable of the two. Benchmark both on your own workload, the difference is real but small.

## Alternatives

- [Qwen](https://toolweight.com/options/alibaba-qwen), Alibaba's model family, huge open-weight range, closed flagship
- [Amazon Bedrock](https://toolweight.com/options/amazon-bedrock), Multi-vendor model access inside your existing AWS account
- [Anthropic](https://toolweight.com/options/anthropic), Claude models, built around long agentic runs and tool use
- [Cerebras](https://toolweight.com/options/cerebras), Wafer-scale inference, the fastest tokens per second available
- [Cohere](https://toolweight.com/options/cohere), Enterprise-focused models built for RAG and private deployment
- [DeepSeek](https://toolweight.com/options/deepseek), Frontier-adjacent models at a small fraction of Western prices
- [Google Gemini](https://toolweight.com/options/google-gemini), Gemini via AI Studio for prototyping or Vertex AI for production
- [Groq](https://toolweight.com/options/groq), Custom LPU silicon serving open-weight models at extreme speed
- [Meta Llama](https://toolweight.com/options/meta-llama), Open-weight Llama models, hosted almost everywhere but Meta
- [Mistral AI](https://toolweight.com/options/mistral-ai), European lab with an open-weight lineage and EU-resident hosting
- [Moonshot AI](https://toolweight.com/options/moonshot-ai), Kimi models, open-weight agentic performance at low cost
- [OpenAI](https://toolweight.com/options/openai), GPT models plus audio, images and embeddings on one bill

## Licence and attribution

Data from toolweight (https://toolweight.com), licensed CC-BY-4.0.

- Licence: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
- Canonical HTML: https://toolweight.com/options/fireworks-ai
- Machine-readable: https://toolweight.com/options/fireworks-ai.md · https://toolweight.com/api/v1 · https://toolweight.com/mcp
- toolweight takes no affiliate revenue and sells no placements. Corrections: https://toolweight.com/suggest