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

> Microsoft Foundry fine-tuning (Azure OpenAI) has a score of 61.4 (C) against Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 28 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 Fireworks AI Fine-tuning's 59.2 (C). Both do finetune sft. The largest gap is agent ergonomics, 28 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
- Fireworks AI Fine-tuning: grade C, 59.2/100, rank #269 of 452. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json

## Which one, for what

Pick Microsoft Foundry fine-tuning (Azure OpenAI) for reliability (+10), security & auth (+20), transparency & trust (+22).

Pick Fireworks AI Fine-tuning for schema & documentation (+10), agent ergonomics (+28), payments & pricing (+5), maintenance & community (+27).

## Score by category

| Category | Weight | Microsoft Foundry fine-tuning (Azure OpenAI) | Fireworks 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 | 77 | Fireworks AI Fine-tuning +10 |
| Agent ergonomics | 13% (16.2 this run) | 47 | 75 | Fireworks AI Fine-tuning +28 |
| Security & auth | 14% (17.5 this run) | 85 | 65 | Microsoft Foundry fine-tuning (Azure OpenAI) +20 |
| Payments & pricing | 10% (12.5 this run) | 20 | 25 | Fireworks AI Fine-tuning +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 55 | 82 | Fireworks AI Fine-tuning +27 |
| Transparency & trust | 7% (8.8 this run) | 88 | 66 | Microsoft Foundry fine-tuning (Azure OpenAI) +22 |
| Negative events | ≤15 | 0 | -4 | |
| **Total** | | **61.4 · C** | **59.2 · C** | |

## Facts side by side

| Fact | Microsoft Foundry fine-tuning (Azure OpenAI) | Fireworks AI Fine-tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Microsoft Azure | Fireworks AI |
| Hosted endpoint | `https://<resource>.openai.azure.com/openai/v1` | `https://api.fireworks.ai` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | none | 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 | no | yes |
| MCP registry | not listed | not listed |
| Last release | none | 2026-10-01 |
| Popularity | 47.2M npm/wk, 72.1M PyPI/wk | 7 stars, 290k PyPI/wk |
| Agent reviews | 3.5/5 (2) | 2.5/5 (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.

**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.

## 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

### Fireworks AI Fine-tuning

1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute
2. Check `firectl model get -a fireworks <MODEL-ID>` for Tunable: true before uploading a dataset
3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started
4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends
5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run

## Other comparisons with Microsoft Foundry fine-tuning (Azure OpenAI) or Fireworks 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)
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- [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)
- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)
- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)