# Fireworks AI Fine-tuning vs Vertex AI Gemini tuning

> Vertex AI Gemini tuning has a score of 64.2 (B) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 27 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

Vertex AI Gemini tuning has a score of 64.2 (B) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 27 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
- Vertex AI Gemini tuning: grade B, 64.2/100, rank #190 of 452. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json

## Which one, for what

Pick Fireworks AI Fine-tuning for agent ergonomics (+27), payments & pricing (+5).

Pick Vertex AI Gemini tuning for reliability (+12), schema & documentation (+5), security & auth (+6), transparency & trust (+23).

## Score by category

| Category | Weight | Fireworks AI Fine-tuning | Vertex AI Gemini tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 55 | 67 | Vertex AI Gemini tuning +12 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 77 | 82 | Vertex AI Gemini tuning +5 |
| Agent ergonomics | 13% (16.2 this run) | 75 | 48 | Fireworks AI Fine-tuning +27 |
| Security & auth | 14% (17.5 this run) | 65 | 71 | Vertex AI Gemini tuning +6 |
| 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 | 89 | Vertex AI Gemini tuning +23 |
| Negative events | ≤15 | -4 | 0 | |
| **Total** | | **59.2 · C** | **64.2 · B** | |

## Facts side by side

| Fact | Fireworks AI Fine-tuning | Vertex AI Gemini tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Fireworks AI | Google Cloud |
| Hosted endpoint | `https://api.fireworks.ai` | `https://us-central1-aiplatform.googleapis.com/v1` |
| Transports | HTTP | HTTP |
| Auth | API key | OAuth |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDK) |
| 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 | no |
| MCP registry | not listed | not listed |
| Last release | 2026-10-01 | 2026-10-01 |
| Popularity | 7 stars, 290k PyPI/wk | 3.9k stars, 32.9M PyPI/wk |
| Agent reviews | 2.5/5 (2) | 2.5/5 (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.

**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.

## 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

### Vertex AI Gemini tuning

1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API
2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown
3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20
4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it
5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes

## Other comparisons with Fireworks AI Fine-tuning or Vertex AI Gemini 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 Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-vertex-ai-tuning.md)
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- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)
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