Exoscale

Exoscale GPU pricing overview

Exoscale is a specialist GPU cloud provider headquartered in Lausanne, Switzerland. Exoscale is a Swiss cloud provider offering A100 and L40S GPU instances with strict Swiss privacy law and GDPR compliance, making it a strong on-demand GPU cloud option for regulated industries requiring Swiss data sovereignty. Per-minute billing with no minimum commitment provides cost-efficient access to GPU compute for AI inference and training workloads in Swiss and EU data centers. A strong choice for financial services, healthcare, and other regulated sectors that need GPU compute within Swiss jurisdiction. Billing is On-demand (per-minute) with a minimum commitment of None. Available regions include EU-Central, EU-West. GPU instances can be provisioned on-demand, making Exoscale suitable for both short-duration experiments and sustained production workloads.

Exoscale vs other GPU providers

Exoscale 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: Swiss data sovereignty; GDPR + Swiss privacy law; Regulated industry focus. Use the side-by-side comparison tool above to see Exoscale pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 Exoscale listings alongside 91+ providers in a single sortable view.

Best use cases for Exoscale

Exoscale is best suited for: Swiss/EU regulated workloads, GDPR-critical AI, Financial services AI. Support tiers range from Basic → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on Exoscale, covering A100 80GB, A100 40GB, L40S, A10G. 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.

Exoscale billing model and cost structure

Exoscale uses On-demand (per-minute) 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 Exoscale

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 Exoscale pricing data is collected

Prices shown are sourced from Exoscale'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.