Fireworks AI vs Together AI
A side-by-side comparison of Fireworks AI and Together AI, 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 | Together AI |
|---|---|---|
| Category | Inference | Inference |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms | API | API |
| Model support | Multi-model | Multi-model |
| Vendor (differs) | Fireworks AI | Together |
| 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
Together AI
One stop for the open-model stack: hundreds of open-weights models served plus both LoRA and full fine-tuning.
- LoRA and full fine-tuning
- Competitive inference-at-scale pricing
- OpenAI-compatible API
- Dedicated endpoints + GPU clusters
- Open models only, no frontier closed models
- Less specialized than single-model hosts
- Throughput varies by model demand
When to pick which
Both cover Model inference / serving, Fine-tuning / training, Multi-model access, and GPU compute.
Pick Fireworks AI if you need Transcription (STT).
- Transcription (STT) (secondary capability)
They share capabilities, but each leads with different ones as a headline job:
Fireworks AI is built around Multi-model access.
- Multi-model access (primary capability)
Together AI is built around Fine-tuning / training.
- Fine-tuning / training (primary capability)