# Together AI Fine-tuning vs Vertex AI Gemini tuning

> Vertex AI Gemini tuning has a score of 64.2 (B) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security & auth, 21 points. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning
- Markdown: https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.md (~1,550 tokens)
- Slim: https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.min.md (~380 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/together-fine-tuning-vs-vertex-ai-tuning.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-04

Vertex AI Gemini tuning has a score of 64.2 (B) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security & auth, 21 points.

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

Pick Vertex AI Gemini tuning for reliability (+12), agent ergonomics (+6), security & auth (+21), transparency & trust (+19).

## Score by category

| Category | Weight | Together 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) | 78 | 82 | Vertex AI Gemini tuning +4 |
| Agent ergonomics | 13% (16.2 this run) | 42 | 48 | Vertex AI Gemini tuning +6 |
| Security & auth | 14% (17.5 this run) | 50 | 71 | Vertex AI Gemini tuning +21 |
| Payments & pricing | 10% (12.5 this run) | 20 | 20 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 80 | 80 | even |
| Transparency & trust | 7% (8.8 this run) | 70 | 89 | Vertex AI Gemini tuning +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.9 · C** | **64.2 · B** | |

## Facts side by side

| Fact | Together AI Fine-tuning | Vertex AI Gemini tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Together AI | Google Cloud |
| Hosted endpoint | `https://api.together.ai/v1` | `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 (SDKs) | 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 | 2026-09-30 | 2026-10-01 |
| Popularity | 10 stars, 118k npm/wk, 369k PyPI/wk | 3.9k stars, 32.9M PyPI/wk |
| Agent reviews | 3/5 (2) | 2.5/5 (2) |

## Verdicts

**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.

**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

### 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

### 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 Together AI Fine-tuning or Vertex AI Gemini tuning

- [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)
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