CUDO Compute

CUDO Compute GPU pricing overview

CUDO Compute is a specialist GPU cloud provider headquartered in London, UK. CUDO Compute is a sustainability-focused GPU cloud providing H100 SXM, H100 PCIe, and A100 clusters powered by verified renewable energy across EU and US data centers. In 2025–2026 CUDO expanded its GPU catalog to include H200 instances, launched a carbon reporting dashboard that gives teams verifiable emissions data per workload, and strengthened its enterprise SLA offering with 99.9% uptime SLA and dedicated account management. CUDO is one of the few GPU cloud providers that can supply both the compute and auditable green energy certificates (RECs) in a single contract — a meaningful differentiator for enterprises with ESG reporting obligations. EU data residency and GDPR compliance are built in across all European regions. Billing is On-demand, Reserved with a minimum commitment of None. Available regions include EU-West, EU-North, US-East. GPU instances can be provisioned on-demand, making CUDO Compute suitable for both short-duration experiments and sustained production workloads.

CUDO Compute vs other GPU providers

CUDO Compute 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: Verified renewable energy with auditable RECs per workload; Carbon reporting dashboard — emissions data per job for ESG reporting; H200, H100 SXM, and A100 clusters with 99.9% uptime SLA. Use the side-by-side comparison tool above to see CUDO Compute pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all active listings alongside 91+ providers in a single sortable view.

Best use cases for CUDO Compute

CUDO Compute is best suited for: Enterprises with ESG reporting obligations needing verifiable green compute, EU-based AI teams requiring GDPR-compliant GPU infrastructure, Large-scale LLM training with sustainability requirements, Teams needing both compute and renewable energy certificates in one contract. Support tiers range from Standard → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. 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.

CUDO Compute billing model and cost structure

CUDO Compute uses On-demand, Reserved 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 CUDO Compute

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 CUDO Compute pricing data is collected

Prices shown are sourced from CUDO Compute'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.