Fireworks AI vs Together AI
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | Fireworks AI | Together AI |
|---|---|---|
| Overall | 8.3 | 8.2 |
| Capability fit | 8.6 | 8.5 |
| Price / performance | 8.8 | 9.0 |
| Latency / reliability | 8.4 | 8.0 |
| Developer & agent DX | 9.0 | 8.4 |
| Privacy / data retention | 7.5 | 7.2 |
| Lock-in risk (higher = worse) | 3.5 | 3.5 |
| Maturity / track record | 8.0 | 8.2 |
| Pricing | Serverless per-token + on-demand GPU; day-0 OSS model support; post-training stack. Free credits often available. | As of 2026-09-14: Open-model inference + fine-tune on one OpenAI-compatible API; competitive per-token rates (often ~$0.15–$0.90/MTok class depending on model). Strong for Llama/Qwen/DeepSeek families; Dedicated endpoint… |
| Tagline | Top pick for production open-model serving with real DX. Prefer over random GPU DIY when you want tokens not… | Best catalog breadth for OSS models. Watch minimums and lifetime spend gates before you design around a gated… |
Fireworks AI
Top pick for production open-model serving with real DX. Prefer over random GPU DIY when you want tokens not ops. Strengths: Fast production inference for open models; Strong fine-tune/post-train tooling; Good DX and model freshness.
Strengths
- Fast production inference for open models
- Strong fine-tune/post-train tooling
- Good DX and model freshness
Weaknesses
- Not a frontier closed-model source
- Unit cost can exceed pure discount hosts
- Enterprise features still maturing vs hyperscalers
Together AI
Best catalog breadth for OSS models. Watch minimums and lifetime spend gates before you design around a gated model. Strengths: Huge open model catalog; Fine-tuning + inference under one roof; Competitive open-model pricing.
Strengths
- Huge open model catalog
- Fine-tuning + inference under one roof
- Competitive open-model pricing
Weaknesses
- Billing tiers/spend gates can surprise mid-build
- Quality variance across catalog models
- Support uneven at small spend
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