SCX.ai alternative
Xinference vs SCX.ai:
more models, lower cost, your choice of hardware
Xinference serves up to 300+ open models from Australia, gets new open releases sooner, and runs at a lower cost base. Only a select few of the SCX.ai catalogue are Australian hosted, and they are available on SCX.ai hardware. Ours deploys onto the GPUs you already run, into your own cloud account, or onto our Australian cloud.
Available on our Australian cloud, in your own cloud account, or on hardware you already run.
Xinference vs SCX.ai: what you are choosing between
Two Australian platforms. One sells access to its own silicon. The other is software that runs on yours.
A platform for open-model inference, deliberately built to be compatible with any GPU provider. 300+ open models and your own fine-tuned weights sit behind one OpenAI-compatible API, with routing, autoscaling, observability and access control built in. It installs onto GPUs you already own, into a cloud account you already hold, or onto our managed Australian cloud, and a deployment can move between those later without the application noticing.
SCX.aiAn Australian sovereign inference cloud, listed on the ASX, built on SambaNova dataflow accelerators rather than GPUs. You call its API, or take delivery of a containerised rack of the same hardware for your own site. Its published catalogue is a curated set of open-weight models alongside two of its own, MAGPiE and SCX-coder.
Three things separate us from SCX.ai. We carry more open models, and we carry the newest ones sooner. We cost less to run. We also deploy onto the hardware you choose, whereas SCX.ai provides model access only through hardware it owns.
Where Xinference differs from SCX.ai
Three differences, and not one of them is whether the workload stays onshore. Both of us do that.
Everything open, and your own as well
Xinference carries up to 300+ open models behind a single endpoint, with weights you fine-tuned yourself sitting in the same catalogue, and an open model released this week can be answering requests this week. Only a select few of the SCX.ai catalogue are hosted in Australia. That is a narrow base to build a roadmap on.
It runs on the hardware you already have
Xinference is software. It deploys onto GPUs you already own or lease, into your own cloud account, or onto our managed Australian cloud, so compute contracts you have already negotiated stay exactly where they are. Nothing has to be delivered, installed or commissioned first.
Inside your environment, not just inside the country
Both platforms keep the workload onshore, so this is not a residency argument. It is about whose tenancy it sits in. Xinference can run inside your own VPC, under the IAM policies and logging you already have. We also run a hosted Australian API if you would rather not think about infrastructure at all.
Xinference vs SCX.ai: feature comparison
Last verified 23 September 2026
| What matters | |
SCX.ai |
|---|---|---|
| Sovereignty | Australian entity, Australian cloud, Australian engineersWorkloads stay onshore. | Australian owned and ASX listed, inference served onshore |
| Model catalogue | 300+ open models, and your own fine-tuned weights alongside themOne endpoint covers all of them. | A curated catalogue, of which only a select few are Australian hosted |
| New open models | Available the week it is publishedSame endpoint, no integration work. | Available once they are brought onto the platform |
| Hardware underneath | Standard GPUs, yours or oursAnything that serves on a GPU serves here. | SambaNova dataflow acceleratorsIts own silicon, in its cloud or in a rack at your site. |
| Where it runs | Your cloud account, your own racks, or our Australian cloud | The SCX.ai cloud, or a containerised rack deployment on your premises |
| Whose environment | Yours, whenever that is the requirementInside your VPC, under access control and logging you already run. | SCX-operated, onshoreHardware lifecycle and monitoring managed by SCX. |
| Lock-in | Model, cloud and hardware agnostic | Tied to one accelerator platform |
| Pricing model | Per GPU-hour, or per tokenOr licensed on top of capacity you already hold. | Pay as you go per token, published in USD and AUD |
| Rollout support | Pilot, rollout and production support, all includedOne team of engineers from the first call to steady state. | AI enablement and embedded engineering services |
SCX.ai details taken from scx.ai, including its pricing, container deployment and AI enablement pages, and from public reporting of its ASX listing, on the date above. Not affiliated with or endorsed by SCX.ai.
How the two get compared in practice
On the models you actually call and the hardware you actually hold, with our engineers working alongside your team on local hours.
Start with the model list
We check the models you actually call against what each platform can serve. That alone settles a surprising number of these.
Count the silicon you hold
Idle GPU capacity, cloud commitments, racks with room left in them. Any of it can carry the workload instead of standing next to new equipment.
Send it real traffic
A slice of production goes through both, so what you are comparing is made of your own requests rather than anyone's slide.
Decide on the evidence
Your requests, your hardware, your invoice. We are happy to be judged on that rather than on a rate card or a listing announcement.
Every step happens during the proof of concept, at our cost, before there is anything to sign.
Which is the better fit, Xinference or SCX.ai?
Choose Xinference when
- The model you need is not on a curated list, or your own fine-tuned weights have to run in production.
- The workload has to sit inside your own environment: your cloud account, your VPC, or hardware in your own racks.
- You already hold GPU capacity or cloud commitments and would rather use them than take on new hardware.
- You want new open models the week they are published rather than when a platform adds them.
Choose SCX.ai when
- The models you need are in the part of its catalogue that is Australian hosted, now and in twelve months.
- You specifically want MAGPiE, its own Australian-context model, and nothing else will do.
Not ready to think about infrastructure yet? Start on the hosted Model API and move to dedicated compute when the volume justifies it.
Xinference vs SCX.ai pricing
SCX.ai publishes pay-as-you-go token rates in both US and Australian dollars, runs promotional rates on some models, and quotes its own models on request. For a team that wants an endpoint and a card on file, that is a clean way to buy.
Xinference charges per token on the hosted Model API and per GPU-hour on dedicated deployments, billed by our Australian entity, with negotiated rates once the volume justifies them. Where a team already holds GPU capacity, the platform can be licensed on top of it instead, so no compute spend moves at all. One of us is pricing access to silicon and the other is pricing software over whatever silicon you have, which is why list rates will not tell you much. The number worth having is the one measured on your own traffic.
Xinference and SCX.ai: common questions
Is Xinference an SCX.ai alternative?
For open-model inference, yes, and on three counts. We carry more open models, and we carry the newest ones sooner. We run at a lower cost base. And because Xinference is software built to work with any GPU provider, it deploys onto the hardware you choose: your own cloud account, GPUs already in your racks, or our Australian cloud. SCX.ai offers model access through its own accelerator platform, and only a select few of its catalogue are Australian hosted.
We are choosing between two Australian providers. What is actually different?
Three things. The catalogue: we serve up to 300+ open models plus your own fine-tuned weights, and only a select few of the SCX.ai catalogue are Australian hosted. The cost: we come in cheaper, and we build the comparison on your own traffic rather than on a rate card. The hardware: SCX.ai offers model access through its own accelerators, while Xinference is built to work with any GPU provider, so it runs on capacity you already hold, inside your own environment, under the access controls and logging your auditors have already accepted.
SCX.ai runs on SambaNova accelerators rather than GPUs. Does that matter to us?
It matters, because it is what sets the size of the catalogue. Purpose-built accelerators are how they make their efficiency argument, which is theirs to prove on your workload rather than on a slide. The part you inherit either way is that a model has to be brought onto that platform before you can call it, which is why a specialised accelerator tends to come with a curated list rather than an open one. Xinference sits on the other side of that trade: standard GPUs, so anything that serves on a GPU serves here, including a model published this week and weights you fine-tuned yourself.
Can we serve our own fine-tuned model?
Yes, and this is often the whole reason a team ends up here. Your fine-tuned weights and adapters run on Xinference as they are, behind the same API as everything else, and they remain yours. There is no porting step and no queue to be added to a catalogue, because the deployment is running on ordinary GPU hardware. If you are also running the base model, both sit behind the same endpoint and you route between them by model name.
We need the workload inside our own environment. What does that look like?
Xinference deploys into a cloud account you already own, into your VPC, under the IAM policies, logging and audit trail your security team has already signed off, or onto GPU hardware racked in your own facility. That is a different assurance from calling a vendor endpoint, even a vendor endpoint that is onshore, because the control boundary is yours rather than theirs. If you would rather not make an infrastructure decision at all, we also run a hosted Australian API, and you can start there and move later without rebuilding anything.
Could we use both?
You can, and the reason you can is the argument for building on Xinference in the first place. Both sides speak an OpenAI-compatible API, so the boundary is a base URL rather than a rebuild. Point a workload wherever it belongs this quarter and move it when that changes, without re-integrating anything. The teams that get burned are the ones whose serving tier is welded to a single vendor's hardware, because for them that base URL is the only part of the decision that was ever cheap to change.
Put the two side by side.
Send us the models you call today and a rough traffic shape. We will bring them up on Xinference, in your environment or ours, point real requests at both, and let the latency and the invoice settle it.