Fireworks AI vs Groq
A side-by-side comparison of Fireworks AI and Groq, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Fireworks AI
InferenceFast inference + fine-tuning. Production deployments at scale.
View Fireworks AIAt a glance
| Attribute | Fireworks AI | Groq |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms (differs) | API | API, Web |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Fireworks AI | Groq |
| Capabilities (differs) |
|
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The honest brief
Fireworks AI
Runs open models on its own FireAttention serving stack, tuned for lower latency than off-the-shelf inference runtimes.
- Custom FireAttention inference stack
- Vision and audio models, not just text
- Serverless + dedicated options
- Fine-tuning supported
- Usage pricing scales with traffic
- Open-weights focus, not proprietary frontier
- Dedicated capacity costs more
Groq
Custom LPU silicon delivers deterministic sub-100ms TTFT, ideal for voice and latency-critical apps.
- Hundreds of tokens/sec on open models
- Sub-100ms time-to-first-token
- Deterministic, low-variance latency
- OpenAI-compatible API with free tier
- Curated open-weight models only
- No frontier closed models (GPT/Claude)
- SRAM limits large context windows
- Rate limits during peak demand
When to pick which
Both cover Model inference / serving, Multi-model access, and Transcription (STT).
Pick Fireworks AI if you need Fine-tuning / training and GPU compute.
- Fine-tuning / training (secondary capability)
- GPU compute (secondary capability)
Pick Groq if you need Speech synthesis (TTS).
- Speech synthesis (TTS) (secondary capability)
They also differ on:
- Platforms
- API · API, Web