Fireworks AI vs Together AI
The question that decides it: Do you need selectable latency tiers and reinforcement fine-tuning, or broader modality coverage and cheaper supervised fine-tuning?
Our verdict
Together AI for most teams: broader catalog, cheaper fine-tuning, more modalities. Fireworks when you need selectable latency tiers on the same model, reinforcement fine-tuning, or documented CMEK encryption states.
Why
These two are genuinely hard to separate, which is itself the useful finding. Both are single-source inference providers serving open-weight models on their own stacks with OpenAI-compatible APIs. Both state SOC 2 Type II and HIPAA compliance with public trust centers. Both offer batch inference and fine-tuning. Neither offers closed frontier models, cross-provider routing or failover. Neither documents EU residency or zero-data-retention on any vendor page found. If you are choosing between them on compliance, you will not find a winner — you will find two similar gaps.
Fireworks differentiates on latency control and training sophistication. It offers selectable DEFAULT, PRIORITY and FAST service tiers, so you can pay for predictable latency on the same model rather than accepting whatever multi-tenant serverless gives you. It supports both supervised and reinforcement fine-tuning, with managed LoRA from $0.50 per million tokens — reinforcement fine-tuning in particular is a capability Together does not list. Fireworks also documents CMEK encryption states and offers region-restricted deployments when a workload must stay in a specific geography.
Together differentiates on breadth and price. 200+ models against Fireworks' 100, spanning text, vision, image, video, audio and embeddings, where Fireworks is open-weight text-centric. Fine-tuning is cheaper at $0.48 per million tokens with a $4 job minimum. And Together publishes a model list endpoint with per-model pricing, licence and context length, which makes programmatic catalog checks possible — useful if you want to detect a model deprecation before it breaks your product.
One practical difference in getting started: Fireworks gives a one-time $1 credit, which is too small for realistic load testing. Together publishes no free tier at all and third-party reporting says new accounts need a prepaid minimum balance. Neither lets you evaluate seriously without spending money, so budget for a paid proof-of-concept either way.
Which one, concretely
Choose Fireworks AI if
- You need selectable latency tiers — DEFAULT, PRIORITY, FAST — on the same model
- You want reinforcement fine-tuning, not just supervised
- You need documented CMEK encryption states for a security review
- You need region-restricted deployments for a geography requirement
Choose Together AI if
- You want breadth — 200+ models across six modalities
- You want cheaper fine-tuning at $0.48 per 1M tokens
- You want a programmatic model list endpoint with pricing, licence and context length
- You need a documented path to dedicated endpoints and GPU clusters as you grow
What catches people out
- Both are single-source: no closed frontier models, no cross-provider routing, no failover. Use a gateway in front if availability matters.
- Neither documents GDPR posture, EU data residency or zero-data-retention on any vendor page found. If those are hard requirements, look elsewhere.
- Fireworks' $1 signup credit is too small for realistic load testing, and Together publishes no free tier with reports of a prepaid minimum.
- Both are multi-tenant serverless by default, so throughput varies with neighbouring load. Predictable latency means paying for a priority tier or dedicated capacity.
- Together can rotate or deprecate model IDs, so pin versions and monitor the catalog endpoint.
Side by side
1 of 14 fields differ, marked with a dot. Every figure links to the vendor page it came from. Blank values read Not published rather than No — silence from a vendor is not a negative answer.
| Field | Fireworks AI | Together AI |
|---|---|---|
| Ease of leaving Derived score, higher is easier | 44/64 Hard to leave | 32/68 Hard to leave |
| What kind of product Category | Inference provider | Inference provider |
| Who runs it Deployment model | Managed only | Managed only |
| Licence Licence | Proprietary | Proprietary |
| Models available Models available | ~100 | 110–200 |
| Model providers reachable Upstream providers | Not published | Not published |
| SOC 2 audited SOC 2 audited | Yes | Yes |
| Will sign a HIPAA agreement HIPAA BAA | Yes | Yes |
| Batch processing Batch processing | Yes | Yes |
| Embeddings Embeddings | Yes | Yes |
| Image generation Image generation | Yes | Yes |
| Video generation Video generation | Not published | Yes |
| Speech and audio Speech and audio | Yes | Yes |
| Content guardrails Content guardrails | Not published | Yes |
| You can export your request history Logs / usage data export | Yes | Not published |
Verified 3 days ago for Fireworks AI and Verified 3 days ago for Together AI. Want more fields, or a third option in the mix? Open these two in the full comparison tool.
Common questions
Which is cheaper for fine-tuning?
Together AI, at $0.48 per 1M tokens with a $4 job minimum, against Fireworks' managed LoRA from $0.50 per 1M tokens. But Fireworks supports reinforcement fine-tuning as well as supervised, which Together does not list — so the cheaper option is not always the capable one.
Do either offer EU data residency?
Neither documents EU data residency or zero-data-retention on any vendor page found. Fireworks does offer region-restricted deployments when a workload must stay in a specific geography, which is the closer thing available, but it is not a documented residency guarantee.
Which has more models?
Together AI, with 200+ models spanning text, vision, image, video, audio and embeddings, against Fireworks' 100 open-weight models which are more text-centric.