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Google Cloud tunes Gemini in the US or EU.
We train in Australia.

On Google Cloud’s Gemini Enterprise Agent Platform, formerly Vertex AI, Gemini tuning runs in us-central1 or europe-west4 and tuned Gemini is served from US or EU endpoints. Here you train open models onshore from Matilda Code or any MCP-capable coding agent or IDE, and keep every checkpoint.

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Matilda Train and Google Cloud, side by side

Where training runs, which models you can tune, whether you get the weights and what keeps a model online.

Checked against each provider’s documentation and price lists on 13 October 2026

FeatureMatilda TrainHosted in AustraliaGoogle CloudGemini Enterprise Agent Platform, formerly Vertex AI
Where training runsIn AustraliaGemini: us-central1 or europe-west4. Open models: US, EU, Singapore or Jakarta
Where the tuned model is servedWherever you choose to run itTuned Gemini is served from US or EU multi-region endpoints
Download the weightsYes, every checkpointNot for tuned Gemini. Tuned open models, yes
When the base model retiresYour weights keep workingTuned models can’t move to a newer Gemini, and stop working when their base model retires
Keeping the model onlineNothing to pay. It’s a file you own.Tuned Gemini 3 costs 1.5× the base model per token. Tuned open models need an hourly endpoint
Newest training GPUs in AustraliaAMD MI355X and NVIDIA B300H100, in one zone each in Sydney and Melbourne; no TPUs
Help porting your codeA Maincode data scientist, includedPaid Customer Care plans

When to stay put. If you need a tuned Gemini and will only ever serve it on Google Cloud, use Google’s tuning. Choose Matilda Train when you want to own the model, keep training in Australia, or train something their menus don’t offer.

One 70B fine-tune, priced in Australia

The GPU-hour price is half the story.
The other half is how long the run takes.

Cost of one run, Australian dollarsMLPerf Llama 2 70B fine-tune · 8 GPUs · on-demand prices excluding GST

Matilda TrainMI355X · 10.18 min

A$11.54

AWS SydneyH100 · 27.54 min

A$47.06

Google Cloud SydneyH100 · 27.54 min

A$64.43

Azure Australia EastH100 · 27.54 min

A$93.23

Vendor-submitted MLPerf Training results, times each provider’s on-demand GPU-hour price in its Australian region. Your workload will differ.

2.7× faster

than the best published 8× H100 result: 10.18 against 27.54 minutes

4× cheaper

per run than AWS Sydney on demand: A$11.54 against A$47.06, excluding GST

From a prompt to your own weights, in three steps

No console to learn and no cluster to run. If you can ask your coding agent for a change, you can ask it for a model.

  1. Step 01

    Connect your agent

    Add one remote MCP server to Matilda Code, or any MCP-capable coding agent or IDE, then approve the connection in your browser. Any client that supports remote MCP over HTTP with OAuth works.

    MCP endpoint · HTTP + OAuth
    https://train.matilda.com/mcp/research
  2. Step 02

    Ask for the model you want

    Describe the run in plain language. Your agent stages the data, checks the request against your limits with research_validate, then submits a durable job that keeps running if you close the laptop.

    Example prompt
    Fine-tune Qwen3-8B on data/tickets.jsonl. Hold out 5% for eval, use 8 GPUs, and stop when eval loss stops improving.
  3. Step 03

    Take the weights

    Checkpoints and adapters land in your workspace. Download them through your agent, SSH or the HTTP API, and serve them anywhere you like.

    Your workspace
    runs/4f19a2/
├─ model/
│  ├─ config.json
│  └─ model-00001-of-00004.safetensors
├─ metrics.jsonl
└─ run-record.json

Other comparisons

The same questions, answered for the other hyperscalers. See them all at a glance.

Moving from Google Cloud

More detail, examples and troubleshooting are in the researcher documentation.

How is this different from Bedrock, Google Cloud or Foundry fine-tuning?
Those managed services tune a fixed list of models, mostly in US or EU regions, and document no way to download a tuned Gemini, GPT-4.1 or Bedrock custom model. Matilda Train trains any open model in Australia and gives you the weights. If you specifically need a tuned Gemini or GPT-4.1, use their service. Compare each provider.
Do I own the model?
Yes. Checkpoints, adapters and merged weights are written to your workspace as standard files. Download them whenever you like and serve them anywhere. There’s no fee to keep them. The workspace guide covers downloads.
Which models can I train?
Any open-weight model your code can load, including the Qwen, Llama, Gemma, gpt-oss, Mistral and DeepSeek families. Use LoRA or QLoRA for quick adaptation, or full-parameter training when you need it. With 288 GB per GPU, larger models fit on fewer GPUs.
Which coding agents work?
Any client that supports remote MCP over HTTP with OAuth, including Matilda Code. You can start a client with read-only access and approve more permissions when a task needs them. The connection guide walks through it.
Where does my data go?
It stays on Australian infrastructure operated by Maincode. Each run is isolated, with its own identity and filesystem and no outbound internet, so training data can’t be sent elsewhere during a run. Other users, including people in your organisation, can’t see your files.

Bring your training.
Own the model.

Create your account, add compute credits and connect your coding agent. You pay by the GPU-hour, and every checkpoint is yours to download.

Sign up Read the getting started guide

Amazon Bedrock, Google Cloud, Gemini Enterprise Agent Platform, Vertex AI, Gemini, Microsoft Foundry, Azure and other product names are trademarks of their owners, used here to identify and compare.