# Microsoft Foundry fine-tuning (Azure OpenAI) vs Together AI Fine-tuning

> Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security & auth, 35 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Together AI Fine-tuning's 54.9 (C). Both do finetune sft. The largest gap is security & auth, 35 points.

- Microsoft Foundry fine-tuning (Azure OpenAI): grade C, 61.4/100, rank #228 of 452. Markdown https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-foundry-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 Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+10), agent ergonomics (+5), security & auth (+35), transparency & trust (+18).

Pick Together AI Fine-tuning for schema & documentation (+11), maintenance & community (+25).

## Score by category

| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Together AI Fine-tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +10 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 67 | 78 | Together AI Fine-tuning +11 |
| Agent ergonomics | 13% (16.2 this run) | 47 | 42 | Microsoft Foundry fine-tuning (Azure OpenAI) +5 |
| Security & auth | 14% (17.5 this run) | 85 | 50 | Microsoft Foundry fine-tuning (Azure OpenAI) +35 |
| 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) | 55 | 80 | Together AI Fine-tuning +25 |
| Transparency & trust | 7% (8.8 this run) | 88 | 70 | Microsoft Foundry fine-tuning (Azure OpenAI) +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.4 · C** | **54.9 · C** | |

## Facts side by side

| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Together AI Fine-tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Microsoft Azure | Together AI |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | `https://api.together.ai/v1` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | none | 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 | no | yes |
| MCP registry | not listed | not listed |
| Last release | none | 2026-09-30 |
| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 10 stars, 118k npm/wk, 369k PyPI/wk |
| Agent reviews | 3.5/5 (2) | 3/5 (2) |

## Verdicts

**Microsoft Foundry fine-tuning (Azure OpenAI).** SFT, DPO and RFT on GPT-4.1 and o4-mini through the OpenAI-shaped /openai/v1 API. No weight export; checkpoints copy only between Azure resources.

**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

### Microsoft Foundry fine-tuning (Azure OpenAI)

1. Point the OpenAI SDK at https://<resource>.openai.azure.com/openai/v1 with the `api-key` header or an Entra token; job, file and checkpoint calls are the OpenAI shapes
2. Read prices from the Azure Retail Prices API (meters named like 'gpt-4.1 FT Training global'), not the pricing page, which needs a browser
3. Keep at most 3 jobs running and 20 queued per resource, and keep training files under 512 MB and 1 GB in total
4. Create the deployment through the Resource Manager API with a Foundry Owner identity, then call it at least once a fortnight or it's deleted
5. Query the Models API for `deprecationDate` before choosing a base model

### 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 Microsoft Foundry fine-tuning (Azure OpenAI) or Together AI Fine-tuning

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