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

> Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Tinker's 51.2 (D). Both do finetune sft. The largest gap is transparency & trust, 37 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 Tinker's 51.2 (D). Both do finetune sft. The largest gap is transparency & trust, 37 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
- 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

## Which one, for what

Pick Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+30), security & auth (+30), transparency & trust (+37).

Pick Tinker for agent ergonomics (+6), maintenance & community (+32).

## Score by category

| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Tinker | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 35 | Microsoft Foundry fine-tuning (Azure OpenAI) +30 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 67 | 70 | Tinker +3 |
| Agent ergonomics | 13% (16.2 this run) | 47 | 53 | Tinker +6 |
| Security & auth | 14% (17.5 this run) | 85 | 55 | Microsoft Foundry fine-tuning (Azure OpenAI) +30 |
| 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 | 87 | Tinker +32 |
| Transparency & trust | 7% (8.8 this run) | 88 | 51 | Microsoft Foundry fine-tuning (Azure OpenAI) +37 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61.4 · C** | **51.2 · D** | |

## Facts side by side

| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Tinker |
| --- | --- | --- |
| Kind | HTTP API | SDK + MCP |
| Vendor | Microsoft Azure | Thinking Machines Lab |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | no (local only) |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | none | Apache-2.0 (cookbook) |
| 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 | 4k stars, 331k 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.

**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.

## 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

### 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

## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Tinker

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