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Fine-tuning compared, for Australian teams.

What you get from Amazon Bedrock, Google Cloud and Microsoft Foundry managed fine-tuning today: where training runs, whether you can download the weights, and what it costs to keep a model online.

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Choose a provider to compare

Each comparison is checked against the provider’s documentation and price lists, most recently on 13 October 2026.

At a glance

The questions that decide where a model gets trained, answered for every provider.

Managed fine-tuning for a team in Australia, as documented on 13 October 2026.
QuestionMatilda TrainAmazon BedrockGoogle CloudMicrosoft Foundry
Where training runsIn AustraliaUS regions only: us-east-1 for Nova, us-west-2 for Llama, Claude 3 Haiku, Qwen3 and gpt-ossGemini: us-central1 or europe-west4. Open models: US, EU, Singapore or JakartaFrom Australia East, Global or Developer training. Microsoft says neither guarantees data residency; regional training is US and EU only
Download the weightsYes, every checkpointNo documented way to download a customised modelNot for tuned Gemini. Tuned open models, yesNo documented way to download a fine-tuned OpenAI model
Keeping the model onlineNothing to pay. It’s a file you own.Most custom models need Provisioned Throughput: US$13.08 to US$24.00 per model unit-hour for a custom Llama 3.1 70BTuned Gemini 3 costs 1.5× the base model per token. Tuned open models need an hourly endpointUS$1.70 an hour per deployed fine-tune, with or without traffic: about US$1,241 a month
Newest training GPUs in AustraliaAMD MI355X and NVIDIA B300H100 on EC2 in Sydney; H200 only through Capacity BlocksH100, in one zone each in Sydney and Melbourne; no TPUsGB200, H200, H100 and MI300X VMs; H100 lists at A$25.39 per GPU-hour
Help porting your codeA Maincode data scientist, includedPaid AWS Support plansPaid Customer Care plansPaid Azure support plans

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

Comparison questions

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.
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.
AMD or NVIDIA: does my code need to change?
Most PyTorch and Hugging Face training code runs on MI355X as it is. Code that depends on CUDA-only kernels can run on B300 instead, or a Maincode data scientist can help port it when you start.

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.

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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.