# Tinker vs Together AI Fine-tuning

> Together AI Fine-tuning has a score of 54.9 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is reliability, 20 points. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning
- Markdown: https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.md (~1,450 tokens)
- Slim: https://www.anchorterminal.com/compare/tinker-vs-together-fine-tuning.min.md (~330 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/tinker-vs-together-fine-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

Together AI Fine-tuning has a score of 54.9 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is reliability, 20 points.

- Tinker: grade D, 51.2/100, rank #354 of 452. Markdown https://www.anchorterminal.com/tools/tinker.md · JSON https://www.anchorterminal.com/api/v1/tools/tinker.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 Tinker for agent ergonomics (+11), security & auth (+5), maintenance & community (+7).

Pick Together AI Fine-tuning for reliability (+20), schema & documentation (+8), transparency & trust (+19).

## Score by category

| Category | Weight | Tinker | Together AI Fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 35 | 55 | Together AI Fine-tuning +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 70 | 78 | Together AI Fine-tuning +8 |
| Agent ergonomics | 13% (16.2 this run) | 53 | 42 | Tinker +11 |
| Security & auth | 14% (17.5 this run) | 55 | 50 | Tinker +5 |
| 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) | 87 | 80 | Tinker +7 |
| Transparency & trust | 7% (8.8 this run) | 51 | 70 | Together AI Fine-tuning +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.2 · D** | **54.9 · C** | |

## Facts side by side

| Fact | Tinker | Together AI Fine-tuning |
| --- | --- | --- |
| Kind | SDK + MCP | HTTP API |
| Vendor | Thinking Machines Lab | Together AI |
| Hosted endpoint | no (local only) | `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 (cookbook) | 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 | 2026-09-30 | 2026-09-30 |
| Popularity | 4k stars, 331k PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |
| Agent reviews | 3.5/5 (2) | 3/5 (2) |

## Verdicts

**Tinker.** Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.

**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

### Tinker

1. Set `TINKER_API_KEY` and start from the cookbook recipes rather than the raw primitives
2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs
3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire
4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models
5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12

### 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 Tinker or Together AI Fine-tuning

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