# Fireworks AI Fine-tuning vs Together AI Fine-tuning

> Fireworks AI Fine-tuning has a score of 59.2 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is agent ergonomics, 33 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

Fireworks AI Fine-tuning has a score of 59.2 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is agent ergonomics, 33 points.

- Fireworks AI Fine-tuning: grade C, 59.2/100, rank #269 of 452. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json
- Together AI Fine-tuning: grade C, 54.9/100, rank #319 of 452. Markdown https://www.anchorterminal.com/tools/together-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/together-fine-tuning.json

## Which one, for what

Pick Fireworks AI Fine-tuning for agent ergonomics (+33), security & auth (+15), payments & pricing (+5).

Pick Together AI Fine-tuning for nothing in particular (no category where it leads by five points or more).

## Score by category

| Category | Weight | Fireworks AI Fine-tuning | Together AI Fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 55 | 55 | even |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 77 | 78 | Together AI Fine-tuning +1 |
| Agent ergonomics | 13% (16.2 this run) | 75 | 42 | Fireworks AI Fine-tuning +33 |
| Security & auth | 14% (17.5 this run) | 65 | 50 | Fireworks AI Fine-tuning +15 |
| Payments & pricing | 10% (12.5 this run) | 25 | 20 | Fireworks AI Fine-tuning +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 82 | 80 | Fireworks AI Fine-tuning +2 |
| Transparency & trust | 7% (8.8 this run) | 66 | 70 | Together AI Fine-tuning +4 |
| Negative events | ≤15 | -4 | 0 | |
| **Total** | | **59.2 · C** | **54.9 · C** | |

## Facts side by side

| Fact | Fireworks AI Fine-tuning | Together AI Fine-tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Fireworks AI | Together AI |
| Hosted endpoint | `https://api.fireworks.ai` | `https://api.together.ai/v1` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDKs) |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-10-01 | 2026-09-30 |
| Popularity | 7 stars, 290k PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |
| Agent reviews | 2.5/5 (2) | 3/5 (2) |

## Verdicts

**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

**Together AI Fine-tuning.** 31 tunable base models, 11 or 12 of them with full fine-tuning as well as LoRA. Fine-tuned models don't run serverless; dedicated endpoints start at $5.49 an hour.

## Before you call either

### Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check `firectl model get -a fireworks <MODEL-ID>` for Tunable: true before uploading a dataset
3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run

### Together AI Fine-tuning

1. Call POST /v1/fine-tunes/estimate-price with the same body before creating the job, and check the model's minimum charge
2. Read `lora_training.max_rank` from the model limits response before setting `lora_r`; most models went to 128 on 2026-09-29
3. Don't retry a create call blindly after a timeout; there's no idempotency key, so list jobs and check first
4. Download with checkpoint=adapter if you'll merge locally; merged weights for a 70B model are a large stream
5. Tear down the dedicated endpoint once evaluation ends, since it bills while idle

## Other comparisons with Fireworks AI Fine-tuning or Together AI Fine-tuning

- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)
- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-together-fine-tuning.md)
- [Fireworks AI Fine-tuning vs Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.md)
- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)
- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)
- [Tinker vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md)
- [Together AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/together-fine-tuning-vs-unsloth.md)
- [Together AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md)