Green AI Cloud

Green AI Cloud GPU pricing overview

Green AI Cloud is a specialist GPU cloud provider headquartered in Europe. Green AI Cloud offers H100 and A100 GPU instances powered by 100% renewable energy across European data centers, targeting organizations that need ESG-compliant AI compute for LLM training, fine-tuning, and inference workloads. On-demand billing and a sustainability-first mission make it a responsible choice for enterprises with carbon reduction commitments. A strong option for EU-based AI teams that want verifiably green GPU cloud infrastructure without sacrificing performance. Billing is On-demand with a minimum commitment of None. Available regions include EU. GPU instances can be provisioned on-demand, making Green AI Cloud suitable for both short-duration experiments and sustained production workloads.

Green AI Cloud vs other GPU providers

Green AI Cloud competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: 100% renewable energy; Carbon-neutral compute; European presence. Use the side-by-side comparison tool above to see Green AI Cloud pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 3 Green AI Cloud listings alongside 91+ providers in a single sortable view.

Best use cases for Green AI Cloud

Green AI Cloud is best suited for: Sustainability-focused teams, ESG-compliant AI, European workloads. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 3 active GPU listings on Green AI Cloud, covering H100 80GB, A100 80GB, RTX A6000. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Green AI Cloud billing model and cost structure

Green AI Cloud uses On-demand pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Green AI Cloud

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Green AI Cloud pricing data is collected

Prices shown are sourced from Green AI Cloud's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.