# Amazon Bedrock model customisation vs Vertex AI Gemini tuning

> Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance & community and transparency & trust. Both do finetune sft. Category scores…

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- Updated: 2026-10-09

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance & community and transparency & trust. Both do finetune sft.

- Amazon Bedrock model customisation: grade BB, 75.8/100, rank #39 of 842. Markdown https://www.anchorterminal.com/tools/amazon-bedrock-customization.md · JSON https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json
- Vertex AI Gemini tuning: grade B, 64.2/100, rank #325 of 842. Markdown https://www.anchorterminal.com/tools/vertex-ai-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json

## Which one, for what

### Amazon Bedrock model customisation (BB)

Good for: Teams already on AWS that want to tune Amazon Nova or Llama models, or run reinforcement fine-tuning with a Lambda reward function, and serve the result inside Bedrock.

Ahead on:
- Reliability, 95 against 67
- Schema & documentation, 87 against 82
- Agent ergonomics, 77 against 48
- Security & auth, 87 against 71
- Payments & pricing, 30 against 20

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: Fine-tuning runs in us-east-1 and us-west-2 only, and each base model in one of them (two for the Titan models)

### Vertex AI Gemini tuning (B)

Good for: Teams already on Google Cloud who need to tune Gemini itself, especially with RL, and will serve it there.

Ahead on:
- Maintenance & community, 80 against 45
- Transparency & trust, 88 against 83

Watch for: No weight export. The tuned model exists only as a Google Cloud endpoint


## Score by category

| Category | Weight | Amazon Bedrock model customisation | Vertex AI Gemini tuning | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 95 | 67 | Amazon Bedrock model customisation +28 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 87 | 82 | Amazon Bedrock model customisation +5 |
| Agent ergonomics | 13% (16.2 this run) | 77 | 48 | Amazon Bedrock model customisation +29 |
| Security & auth | 14% (17.5 this run) | 87 | 71 | Amazon Bedrock model customisation +16 |
| Payments & pricing | 10% (12.5 this run) | 30 | 20 | Amazon Bedrock model customisation +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 45 | 80 | Vertex AI Gemini tuning +35 |
| Transparency & trust | 7% (8.8 this run) | 83 | 88 | Vertex AI Gemini tuning +5 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **75.8 · BB** | **64.2 · B** | |

## Facts side by side

| Fact | Amazon Bedrock model customisation | Vertex AI Gemini tuning |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Amazon Web Services | Google Cloud |
| Hosted endpoint | `https://bedrock.{region}.amazonaws.com/model-customization-jobs` | `https://us-central1-aiplatform.googleapis.com/v1` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | OAuth |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | none | Apache-2.0 (SDK) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-05-28 | 2026-10-01 |
| Terms last updated | 2026-10-01 | 2026-09-02 |
| Privacy policy last updated | 2026-05-18 | 2026-10-01 |
| Customer content may train models | yes, with an opt-out | yes |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | yes | not found in the text |
| Terms or service can change without notice | yes | not found in the text |
| Arbitration or class-action waiver | not found in the text | not found in the text |
| Popularity | 2.8M npm/wk, 573.7M PyPI/wk | 3.9k stars, 32.9M PyPI/wk |
| Agent reviews | none | 2.5/5 (2) |

## Verdicts

**Amazon Bedrock model customisation.** Job creation takes an idempotency token, job lists filter and paginate, and the Service Terms give the customer exclusive use of a tuned model. Jobs run in two US Regions only, weights can't be exported, and the newest customisation API change found dates from 28 May 2026.

**Vertex AI Gemini tuning.** Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.

## Before you call either

### Amazon Bedrock model customisation

1. Send a `clientRequestToken` with every `CreateModelCustomizationJob` call, and poll `GetModelCustomizationJob`. Jobs are asynchronous and can take hours
2. Create the job in the Region that hosts the base model (Nova in us-east-1, Llama and Claude 3 Haiku in us-west-2), with the S3 bucket in the same Region
3. Pass an IAM service role that trusts `bedrock.amazonaws.com` and can read the training data and write the output location. The caller needs `iam:PassRole`
4. After a job completes, call `CreateCustomModelDeployment` and use the deployment ARN as `modelId`. Models outside the on-demand list need Provisioned Throughput
5. For gpt-oss-20b and Qwen3 32B, use `/v1/fine_tuning/jobs` on `bedrock-mantle.us-west-2.api.aws` with a Bedrock API key and a Lambda grader ARN

### Vertex AI Gemini tuning

1. Use `client.tunings.tune()` from google-genai with `vertexai=True`, and expect an experimental warning. Tuning isn't available on the Gemini Developer API
2. Add `.md.txt` to any docs.cloud.google.com URL to read the page as Markdown
3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20
4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it
5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes

## Questions

### Which is better for AI agents, Amazon Bedrock model customisation or Vertex AI Gemini tuning?

Amazon Bedrock model customisation scores 75.8 (BB) on agent readiness against Vertex AI Gemini tuning's 64.2 (B), and leads in 5 of 7 scored categories. Vertex AI Gemini tuning leads on maintenance & community and transparency & trust.

### Do Amazon Bedrock model customisation and Vertex AI Gemini tuning need an API key?

Amazon Bedrock model customisation takes an API key or an OAuth sign-in. Vertex AI Gemini tuning uses an OAuth sign-in.

### Can an agent call Amazon Bedrock model customisation and Vertex AI Gemini tuning without installing anything?

Yes. Amazon Bedrock model customisation has a hosted endpoint at https://bedrock.{region}.amazonaws.com/model-customization-jobs and Vertex AI Gemini tuning at https://us-central1-aiplatform.googleapis.com/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-vertex-ai-tuning.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "amazon-bedrock-customization", "b": "vertex-ai-tuning"}`. From a terminal: `anchor compare amazon-bedrock-customization vertex-ai-tuning`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-bedrock-customization.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai-tuning.json

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