Rent GPUs, buy your own, or use an API? A guide for teams building on AI

There are three ways to get the GPU compute an AI product needs, and the internet will happily argue that its favourite is the only sane choice. It is not that simple. The right answer is not about which technology is best — it is about how busy you can keep a machine, how much control you need over your model and data, and whether you would rather spend capital or spend monthly. Here is how we would think it through.

Option 1 — a pay-per-token API

You call someone else's endpoint and pay per token or per request. Nothing to run, nothing to patch, and you are live this afternoon.

Where it wins: prototyping, low or spiky volume, and any team that does not want to operate infrastructure. If you are still finding product-market fit, this is almost always where to start — you should not own a GPU to serve a hundred requests a day.

Where it hurts: cost becomes unpredictable and then large as volume grows, because you are paying a margin on every call forever. You are limited to the models on offer, you cannot fine-tune freely, and your data leaves your boundary on every request — which some regulated teams simply cannot allow.

Option 2 — buy your own GPUs

You purchase the hardware and rack it, in a colo or your own room. The GPUs are yours; so is everything around them.

Where it wins: heavy, always-on workloads with full control. If you will genuinely keep the cards busy for years, owning them can be the lowest cost per GPU-hour there is, and nobody can take the capacity away from you.

Where it hurts: the cheque. An eight-way Blackwell box is a six-figure capital outlay before you have paid for power, cooling, space and the people to run it — and modern GPUs draw enough power that "just put it in the office" is not a real plan. Then it depreciates whether you use it or not. Ownership only pays off at high, sustained utilisation over a long horizon; below that, you have bought an expensive space heater.

Option 3 — rent bare-metal nodes

You reserve dedicated physical GPUs for a term. No capital outlay, no datacentre to run — but unlike an API, the machine is yours alone for the duration, and you run your own stack on it. This is what FlexiCloud does with B300 nodes.

Where it wins: the large middle ground between "too big for an API" and "not ready to buy". You get dedicated hardware and a flat, predictable rate; your data stays on a machine and in a region you choose; and you keep full control of the OS, drivers, CUDA and orchestration without owning depreciating metal. It is opex, not capex, so it comes out of a budget you already have.

Where it hurts: you commit for a term, and you are responsible for your own software stack — we run the hardware, you run what is on it. If your demand is tiny or truly unpredictable, the commitment is the wrong shape and an API is cleaner.

A simple way to decide

Strip away the noise and it comes down to utilisation and control:

• Spiky, small, or still experimenting? Use an API. Pay only for what you use, and revisit when the bill starts to sting.
• Steady, heavy demand for a defined project or season, and you care where your data runs? Rent a node. You get dedicated hardware and a predictable cost without a capital purchase.
• High, sustained utilisation for years, with the capital and the team to run it? Buy. At that scale, owning wins — and renting first is a good way to prove the workload before you commit the capital.

The honest version of this: many teams move through these options rather than picking one forever — API to find the shape of the product, a rented node when training and inference become steady, and their own hardware only if and when the numbers clearly justify it.

Where we fit

FlexiCloud is the middle option done properly: dedicated bare-metal B300 nodes in India, the EU and the US, reserved from six months, priced on the length of your term. You run the software; we run the hardware. If a node is not the right call for where you are, we will say so — sometimes the right advice is "stay on the API for another quarter". If you want to work out which option your workload actually needs, see what a run would cost in our B300 cost breakdown, check the pricing page, or talk to an engineer.