4090 à 0,20 €/heure et 5090 à 0,40 €/heure. Pas d'enchères, pas de gadgets.

Updated May 2026: This article was originally published when Hivenet Compute launched lower introductory GPU prices. We have rewritten it because both our pricing and the GPU cloud market have changed. Current Hivenet Compute rates used in this article are €0.40/hour for RTX 4090 and €0.75/hour for RTX 5090. GPU prices move often, so always check the live Compute console before making a buying decision.

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The cheapest GPU cloud is not always the provider with the lowest number on a pricing page.

For AI workloads, “cheap” can mean several different things. It can mean the lowest batch price. It can mean the lowest marketplace bid. It can mean a stable on-demand price your finance team can forecast. It can mean no egress fees, per-second billing, or a GPU that is actually available in the region where your data needs to stay.

That distinction matters. RTX 4090 and RTX 5090 instances sit in a useful middle ground for AI teams. They are not H100 or H200 replacements for every workload. They are not the right choice for frontier-scale training or workloads that require ECC memory, NVLink, MIG, or formal data-center GPU certification. But for inference, fine-tuning, notebooks, image generation, rendering, and production prototypes, they can offer strong performance at a much lower hourly cost than traditional data-center GPUs.

What changed since the original article

The original version of this post compared Hivenet’s launch pricing against public GPU cloud providers in October 2025. Those launch prices were:

That changes the claim we can honestly make.

At the old price, Hivenet could argue that its 4090 and 5090 rates were unusually low among predictable on-demand providers. At the current price, the better claim is narrower and more useful:

Hivenet Compute offers competitive fixed on-demand RTX 4090 and RTX 5090 pricing, with per-second billing, no egress fees, EUR pricing, and regional control.

That is different from saying “the cheapest GPU on the internet.” It is also more defensible.

The current comparison

Here is the practical market picture. Treat this as a snapshot, not a permanent price list.

TensorDock’s public dashboard lists RTX 5090 from $0.56/hr and RTX 4090 from $0.27/hr. Salad lists batch-priority RTX 5090 at $0.294/hr and RTX 4090 at $0.204/hr. RunPod’s RTX 5090 model page advertises RTX 5090 GPUs from $0.99/hr. Vast.ai describes real-time marketplace pricing set by supply and demand across its GPU inventory.    

That table tells the real story. If you only need the lowest sticker and your workload can handle batch scheduling, marketplace listings, or changing availability, Hivenet will not always be the cheapest choice. If you want predictable on-demand access, per-second billing, no egress fees, and a European-first posture, the comparison changes.

What the RTX 4090 is good for

The RTX 4090 is still the practical entry point for many AI workloads.

It has 24 GB of VRAM, enough for a large set of 7B and 13B-class open-weight models, LoRA and QLoRA fine-tuning, Stable Diffusion and Flux workflows, computer vision experiments, notebooks, and smaller inference services. NVIDIA’s own RTX 4090 page lists it as an Ada Lovelace GPU with 24 GB of G6X memory.

On Hivenet Compute, the RTX 4090 is currently €0.40/hr. That works out to about €0.0167 per GB-VRAM-hour.

That number is not the whole story. VRAM-hour does not capture memory bandwidth, model architecture, quantization, batching, CPU pairing, storage, or operational overhead. But it is a useful first-pass budgeting tool.

Choose an RTX 4090 when you want a low-cost GPU you control, and your model or workload fits comfortably inside 24 GB of VRAM.

Good fits include:

What the RTX 5090 is good for

The RTX 5090 is the stronger option when you need more memory headroom, more throughput, and a newer architecture.

NVIDIA lists the RTX 5090 as a Blackwell GPU with 32 GB of GDDR7 memory. NVIDIA’s marketplace listing for the Founders Edition gives 32 GB GDDR7 and 21,760 CUDA cores.  

On Hivenet Compute, the RTX 5090 is currently €0.75/hr. That works out to about €0.0234 per GB-VRAM-hour.

That is higher than the 4090 on a raw VRAM-hour basis, but the 5090 is not only buying you more VRAM. It gives you Blackwell, 32 GB GDDR7, stronger memory bandwidth, and better fit for heavier small and mid-sized AI workloads.

Choose an RTX 5090 when your workload benefits from the 32 GB VRAM ceiling, stronger throughput, or FP8/FP4-era model serving.

Good fits include:

The important caveat: do not sell the RTX 5090 as a universal H100 replacement. It is not. H100-class GPUs still matter for workloads that need 80 GB HBM, ECC, MIG, NVLink, large low-latency 70B serving, or enterprise compliance requirements. The 5090 wins where the workload fits the card.

The honest Hivenet position

Hivenet Compute is not always the lowest hourly price on the market.

That sentence should stay in the article, because readers will check. Salad, TensorDock, Vast.ai listings, and other marketplace or batch providers can show lower sticker rates. Some will be better for a specific workload. Some will be cheaper if you are patient, flexible, or running batch jobs that can tolerate interruptions.

The Hivenet Compute claim is different:

Hivenet Compute is for teams that want fixed on-demand RTX 4090 and RTX 5090 GPU access, per-second billing, no egress fees, regional control, and no marketplace bidding.

That is the buyer Hivenet should speak to in this post.

Not the person trying to shave the last cent off a batch job.

The buyer is the researcher, developer, AI startup, or team lead who wants to run a workload now, keep control of the stack, know what the hour costs, and avoid rebuilding the deployment every time the cheapest listing disappears.

Compute or Inference?

This article is about Hivenet Compute.

That means raw GPU access. You get the GPU instance. You run your own stack: vLLM, TGI, SGLang, llama.cpp, PyTorch, ComfyUI, Jupyter, or whatever your workload needs.

That is the right product when you want control.

It is not the same thing as a managed inference API. If you want to replace OpenAI, Anthropic, or Gemini calls by changing a base URL and sending requests to a managed endpoint, that is an Inference product question, not a Compute question.

The distinction matters because it changes the work the customer has to do. Compute gives you a GPU and control. Inference gives you a managed endpoint. Mixing those in one pricing claim makes the article less useful and creates the wrong expectation.

How to choose the cheapest useful option

Use this decision rule.

Choose a batch provider when the job can wait, restart, or tolerate scheduling constraints. Training experiments, rendering batches, and non-urgent generation jobs can often live here.

Choose a marketplace when you are comfortable checking listings, comparing host reliability, and accepting price or availability changes. This can be the cheapest route for technical users with time to manage the tradeoffs.

Choose Hivenet Compute when you want a fixed on-demand GPU price, per-second billing, no egress fees, and control of the stack. It is the cleaner choice when operational time matters more than chasing the lowest temporary listing.

Choose a data-center GPU provider when the workload needs H100/H200-class memory, ECC, MIG, NVLink, very large model serving, or strict procurement requirements.

CFO math: what the current Hivenet prices mean

At the current Hivenet Compute rates:

For a full 8-GPU host, the simple hourly math is:

Do not use these numbers as a benchmark claim. They are pricing math. Real cost per token, image, training run, or inference request depends on the model, precision, batch size, context length, framework, storage, and network path.

But pricing math still helps. It gives teams a clean starting point before they run their own benchmark.

Bottom line

If you want the absolute lowest sticker price, you will often find it in batch pricing or live marketplaces.

If you want predictable on-demand RTX 4090 or RTX 5090 access, Hivenet Compute is a strong option: fixed EUR pricing, per-second billing, no egress fees, and GPU instances you can use without bidding for capacity.

That is the better claim. It is less flashy than “cheapest,” but it is more useful. It also survives a reader opening three competitor pricing pages in the next tab.

For AI work, the cheapest GPU is not the one with the smallest hourly number. It is the one that lets you finish the job with the fewest surprises.

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