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

> Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Unsloth's 51.7 (D). Both do finetune sft. The largest gap is transparency & trust, 54 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 Unsloth's 51.7 (D). Both do finetune sft. The largest gap is transparency & trust, 54 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
- Unsloth: grade D, 51.7/100, rank #347 of 452. Markdown https://www.anchorterminal.com/tools/unsloth.md · JSON https://www.anchorterminal.com/api/v1/tools/unsloth.json

## Which one, for what

Pick Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+22), security & auth (+50), transparency & trust (+54).

Pick Unsloth for agent ergonomics (+6), payments & pricing (+40), maintenance & community (+27).

## Score by category

| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Unsloth | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 43 | Microsoft Foundry fine-tuning (Azure OpenAI) +22 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 67 | 66 | Microsoft Foundry fine-tuning (Azure OpenAI) +1 |
| Agent ergonomics | 13% (16.2 this run) | 47 | 53 | Unsloth +6 |
| Security & auth | 14% (17.5 this run) | 85 | 35 | Microsoft Foundry fine-tuning (Azure OpenAI) +50 |
| Payments & pricing | 10% (12.5 this run) | 20 | 60 | Unsloth +40 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 55 | 82 | Unsloth +27 |
| Transparency & trust | 7% (8.8 this run) | 88 | 34 | Microsoft Foundry fine-tuning (Azure OpenAI) +54 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.4 · C** | **51.7 · D** | |

## Facts side by side

| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Unsloth |
| --- | --- | --- |
| Kind | HTTP API | Agent framework |
| Vendor | Microsoft Azure | Unsloth |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | no (local only) |
| Transports | HTTP |  |
| Auth | OAuth or key | None |
| Pricing | Pay per use | Free |
| x402 | no | no |
| Licence | none | Apache-2.0 (core), AGPL-3.0 (Studio UI) |
| 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 | 2026-09-28 |
| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 77k stars, 230k PyPI/wk |
| Agent reviews | 3.5/5 (2) | 3.5/5 (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.

**Unsloth.** The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.

## 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

### Unsloth

1. Install with `uv pip install unsloth --torch-backend=auto` on a CUDA machine; the desktop app is for people
2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template
3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship
4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel
5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes

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