Groq vs Fireworks AI
The question that decides it: Do you need maximum output speed on a handful of models, or a broad open-weight menu with a business associate agreement?
Our verdict
Regulated data, fine-tuning, or a model that is not on a short list: Fireworks AI, which states around 100 models and will sign a HIPAA business associate agreement. Raw output speed on a small set of open-weight models, where EU residency is not a requirement: Groq.
Why
Start with what these are. Neither is a gateway — both are inference providers that serve open-weight models on their own stacks, and they appear in this catalog because teams routinely use one as the routing layer itself rather than putting a gateway in front of it. Both are managed-only, both bundle inference into the token price, and neither has a separable gateway fee to compare. That means the usual markup and credit-fee questions do not apply; you are comparing per-token rates and what surrounds them.
The reach difference is stark and it is the first filter. Fireworks states around 100 models. Groq's catalog gives between 6 and 16 depending on which kinds you count, and that is the deliberate consequence of its design: a narrow menu is what makes running models on its own LPU hardware viable. If the model you need is not on Groq's list, the speed argument never gets made.
Compliance separates them more sharply than most pairs at this end of the catalog. Both publish a SOC 2 report and both state zero data retention with a retention figure of zero days, which is a genuinely strong answer from either. But Fireworks will sign a HIPAA business associate agreement and Groq does not publish one, and Groq explicitly records EU data residency as not offered — one of very few outright noes in this catalog rather than an unpublished field. For a European team or a regulated workload, that pair of facts decides it.
The training question splits the other way, and it is the one place Fireworks is weaker. Fireworks states it trains on customer data only if you opt in, which is a clear and acceptable answer. Groq does not publish a position on training at all, which is recorded as unpublished rather than as a no. Both log metadata only with a documented off switch. Fireworks also offers fine-tuning and dedicated deployments, which is a category of capability Groq's narrow-menu model does not cover.
Which one, concretely
Choose Groq if
- Output speed on open-weight models is the primary requirement
- The model you need is on a short list you have already checked
- You want zero stated retention and metadata-only logging
- EU data residency is not a requirement
Choose Fireworks AI if
- You need a signed HIPAA business associate agreement
- You need a broad open-weight menu — around 100 models
- You need fine-tuning or dedicated deployments
- You want an explicit opt-in-only position on training
What catches people out
- Groq records EU data residency as not offered — an explicit no, not an unpublished field.
- Groq publishes no HIPAA business associate agreement and no position on training on customer data.
- Groq's catalog gives between 6 and 16 models depending on what you count, so confirm your model is served before anything else.
- Fireworks trains on customer data if you opt in, so check the default state of that setting on your account.
- Neither is a gateway: adopting either as your routing layer means no multi-provider failover unless you add one.
- Both bundle inference into the token price, so there is no separable gateway fee to compare — compare per-token rates.
Side by side
Interpret these fields: How much does an LLM gateway lock you in? · LLM gateway compliance: SOC 2, HIPAA and evidence · How LLM gateway failover actually works
4 of 13 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 | Groq | Fireworks AI |
|---|---|---|
| Ease of leaving Derived score, higher is easier | 32/68 Hard to leave | 44/64 Hard to leave |
| What kind of product Category | Inference provider | Inference provider |
| Who runs it Deployment model | Managed only | Managed only |
| Licence Licence | Proprietary | Apache-2.0 |
| Models available Models available | 11 | ~27 |
| SOC 2 audited SOC 2 audited | Yes | Yes |
| Will sign a HIPAA agreement HIPAA BAA | Not published | Yes |
| Can keep data in the EU EU data residency | No | Not published |
| Does not retain your data Zero data retention | Yes | Yes |
| What gets logged Logged content | Metadata only | Metadata only |
| You can turn logging off Body-logging opt-out | Yes | Yes |
| How long they keep it Default content retention (days) | Nothing kept by default | Nothing kept by default |
| Could they train on your prompts Training on customer data | Not published — silence, not a no | Only if you opt in |
| Free tier Free tier | Free tier with per-model rate limits (for example gpt-oss-120b at 30 requests/min, 1,000 requests/day, 8,000 tokens/min); the paid Developer plan raises this to about 1,000 RPM and 250,000 TPM and adds Batch and Flex processing. | $1 in free credits on signup, then postpaid billing. |
for Groq and for Fireworks AI. Want more fields, or a third option in the mix? Open these two in the full comparison tool.
Common questions
Are Groq and Fireworks AI gateways?
No. Both are inference providers that serve open-weight models on their own infrastructure. They appear in this catalog because teams often use one directly as the routing layer instead of putting a gateway in front of it. Neither gives you multi-provider failover, which is the main thing a gateway adds.
Which one can I use with health data?
Fireworks AI, which will sign a HIPAA business associate agreement. Groq does not publish one — recorded as unpublished rather than a refusal, so it is worth asking. Both publish a SOC 2 report and state zero data retention with a retention figure of zero days, so the gap is specifically the BAA.
How many models does each one serve?
Fireworks AI states around 100. Groq's catalog gives between 6 and 16 depending on which kinds you include, because a narrow menu is what makes its own hardware viable. Check that the specific model you need is served by Groq before weighing any speed advantage, since reach is the binding constraint.