GPU Rental Pricing and IaaS Offerings in Canada for AI Workloads

Overview of GPU Cloud Providers in Canada

Canadian Regions & Providers: A number of major cloud providers and specialized GPU cloud services offer resources in Canadian data centers. Key regions include AWS Canada (Central) (ca-central-1 in Montreal), Microsoft Azure Canada Central/East (Toronto and Quebec City), Google Cloud’s Montréal/Toronto regions, and OVHcloud (Beauharnois/Montreal). Additionally, specialized GPU cloud platforms like Lambda Labs, CoreWeave, RunPod, and Vast.ai provide GPU rentals; some have Canadian points-of-presence or community providers (e.g. RunPod’s CA-MTL region), while others are currently based in U.S./global data centers.

Target GPU Models: We focus on NVIDIA’s high-end accelerators – Tesla V100 (16 GB), A100 (40 GB and 80 GB variants), and H100 (80 GB) – as these are popular for deep learning training and inference. Below we compare on-demand hourly prices (in USD, with approximate CAD in parentheses) for these GPUs across providers, noting multi-GPU instance bundling, discount options, and data residency implications. We then discuss other considerations like OpenAI/xAI services and the idea of running traditional hosting on GPU hardware.

Pricing Comparison (On-Demand Hourly Rates per GPU)

The table below summarizes on-demand hourly pricing per GPU for V100, A100, and H100 on various platforms, focusing on Canadian regions when available. (Currency conversion is ~1 USD = 1.3 CAD for reference.)

 

Provider V100 16GB (per GPU) A100 40GB (per GPU) A100 80GB (per GPU) H100 80GB (per GPU) Notes
AWS (Canada) $3.37 USD/hr ($4.40 CAD) $4.71 USD/hr ($6.15 CAD) (in 8-GPU instance) N/A in ca-central-1 N/A in ca-central-1 P3 (V100) available 1–8 GPUs; A100 only in 8-GPU P4d (40GB). No H100 in CA yet.
Azure (Canada) Limited – (older V100 VMs not widely available in CA) $5.5 USD/hr ($7.2 CAD) (A100 80GB, per GPU) $5.5 USD/hr ($7.2 CAD)* Preview only (not GA in CA) Azure ND A100v4 VMs in CA (e.g. 1×A100 80GB ≈ $5.5/hr). H100 (NDv5) in preview (US regions).
Google Cloud (Canada) N/A (no V100 in CA; T4/P4 only) N/A (no A100 in CA regions) N/A N/A Montréal/Toronto GCP regions lack V100/A100/H100 (only T4/P4/L4 GPUs).
OVHcloud (Montreal) $0.88 USD/hr (~$1.15 CAD) (V100S 32GB) N/A in CA (A100 available in EU only) N/A in CA Not yet in CA (EU only) OVH public cloud offers V100/V100S in Canada at ~$0.88/hr. Newer A100, H100 currently only in OVH’s EU region (GRA).
Specialized GPU Clouds:         (Prices in USD; data centers mainly outside Canada)
Lambda (Cloud) $0.55/hr (V100) $1.29/hr (A100 40GB) $1.79/hr (A100 80GB) $2.99/hr (H100 SXM) Lambda’s on-demand prices (US regions) are much lower than hyperscalers (pay by minute). No Canadian region yet (data in US).
CoreWeave (Cloud) ~$0.60–$0.80/hr (V100, estimate) ~$2.46/hr (A100 80GB, PCIe) ~$2.70/hr (A100 80GB, SXM, in 8-GPU VM) ~$3–$4/hr (H100, estimate) CoreWeave (US-based) offers flexible GPU instances. E.g. 8×A100-80GB = $21.60/hr (~$2.70 per GPU). Canadian data center planned (Cohere/CoreWeave) but not live yet.
RunPod (Secure/Community) ~$0.70–$0.99/hr (V100, community) $1.64/hr secure / $1.19/hr community (A100 80GB) $1.89/hr / $1.39/hr (A100 80GB SXM) $2.39/hr / $1.99/hr (H100 PCIe); ~$2.99/$2.69 (H100 SXM) RunPod has Secure Cloud (owned infrastructure) and Community Cloud (peer providers). They support a Montreal region (for data residency) via community hosts. Prices are highly competitive (H100 ~$2–3/hr; A100 ~$1.2–1.9/hr).
Vast.ai (marketplace) ~$0.40–$0.80/hr (varies by host) ~$1.0–$2.0/hr (varies) ~$1.5–$2.5/hr (varies) ~$2–$4/hr (varies) Vast.ai brokers spare GPUs from many hosts. Prices can be very low (bid/spot instances). Canadian hosts exist but availability may be limited.

 

Notes: “N/A” indicates the GPU/instance is not offered in that region. AWS and Azure pricing for Canada is ~10–15% higher than their U.S. pricing pcr.cloud-mercato.com aws-pricing.com. AWS’s A100 (P4d) instances bundle 8 GPUs (must rent all 8, ~$37.69/hr in ca-central, i.e. ~$4.71 per GPU) aws-pricing.com. Azure’s A100 VMs in Canada can be as small as 1 GPU (Standard_NC24ads_v4) at ~USD $5.5/hr costcalc.cloudoptimo.com. OVH’s listed H100 ($2.99/hr) and A100 are in EU; these were not yet deployed in Canada as of early 2025 help.ovhcloud.com. Specialized providers (Lambda, CoreWeave, RunPod) offer much lower rates, but their infrastructure is primarily outside Canada (except RunPod’s community option). All prices above are on-demand Linux rates; long-term commitments or spot markets can yield further discounts (explained below).

 

On-Demand vs. Reserved & Spot Pricing

On-Demand Pricing: The rates above are standard pay-as-you-go prices. Public cloud providers charge a premium for on-demand flexibility. For example, AWS’s base on-demand price for 1×V100 in us-east-1 is ~$3.06/hour datacrunch.io datacrunch.io (about $3.37/hr in ca-central-1). Azure’s on-demand price for 1×A100 80GB in East US is ~$3.67/hr datacrunch.io (higher in Canada). These on-demand rates can be significantly undercut by other options:

  • Reserved/Savings Plans: AWS and Azure offer savings for committing to 1- or 3-year terms or purchasing Savings Plans. For instance, a 1-year reserved AWS p3 instance is ~30% cheaper than on-demand, and 3-year can be ~50+% cheaper advisor.cloudzero.com advisor.cloudzero.com. These require steady workloads to be cost-effective.

  • Spot Instances: Spot (AWS) or Low-Priority (Azure) VMs can be 70–80% cheaper than on-demand, at the cost of potential interruptions. For example, AWS p3.2xlarge (V100) spot was about $0.61/hr in us-east (vs $3.06 on-demand) instances.vantage.sh. Spot availability in smaller regions (like AWS Canada) can be limited – for AWS P4d (A100) in ca-central, spot was sometimes not offered aws-pricing.com. Azure spot for ND A100 may also yield ~75–85% savings if capacity is idle instances.vantage.sh.

  • Committed/Bulk Pricing (Specialty providers): Companies like Lambda and CoreWeave often negotiate custom deals for large or sustained use. Lambda lists a reserved rate for H100 (with minimum 32 GPUs commitment) by contacting sales lambda.ai. CoreWeave’s model allows customizing CPU/GPU counts; their pricing is linear and they bill by the minute, similar to on-demand but with volume discounts available on request.

Multi-GPU Bundling: It’s important to note how instance bundling affects price granularity. AWS P3 instances allow renting 1, 4, or 8 V100s (p3.2xlarge, 8xlarge, 16xlarge). A user can start with a single V100 on AWS. In contrast, AWS’s A100 P4d requires an 8×GPU block (p4d.24xlarge) – you pay $37.7/hr for the whole 8-GPU machine aws-pricing.com. Azure offers more flexibility: NC-series VMs let you choose 1, 2, 4, or 8 GPUs. For example, an NC24ads_v4 (1×A100) vs NC48 (2×A100) scale linearly ($1.19/hr per A100 on RunPod’s community cloud, or ~$3.67/hr per A100 on Azure East US) datacrunch.io runpod.io. Specialized providers like Lambda, CoreWeave, RunPod all allow requesting single GPU increments (and even fractional GPUs in some cases via MIG or multi-tenant sharing), which can benefit smaller workloads.

Dedicated/Bare-Metal vs. Virtual Instances

Dedicated/Bare-Metal Options: Some providers offer bare-metal servers with GPUs, which can be dedicated to one tenant (useful for maximum performance or specific drivers). For instance, OVHcloud has High-Grade Servers (HGR series) and AI Servers which are physical machines with 4× or 8× GPUs that customers rent monthly. These often have lower effective hourly costs but require monthly commitments. OVH’s HGR-STOR or HGR-AI lines can host GPUs like V100S; prices were on the order of a few thousand CAD per month (thus effective <$0.50/hr/GPU in some cases). Similarly, Oracle Cloud and others have bare-metal GPU servers (Oracle’s BM.GPU instances with V100/A100, though Oracle’s Toronto region may not yet offer those).

In the public cloud (AWS/Azure/GCP), standard GPU instances are virtualized but essentially give you dedicated access to the GPU device. AWS offers dedicated hosts or capacity reservations if strict single-tenancy is needed for compliance. Azure’s GPU VMs can be isolated via dedicated Azure VM host if required.

Data Residency & Sovereignty: To meet Canadian data residency needs, the compute and storage must reside in Canada. AWS, Azure, and GCP all allow specifying the region (with AWS/Azure explicitly guaranteeing data stays in region). AWS Canada and Azure Canada are suitable for sensitive data projects in regulated sectors, with compliance certifications. OVHcloud Canada is often chosen for its sovereign cloud positioning (OVH offers a “SecNumCloud” environment in France for EU sovereignty; in Canada, OVH’s data centers are used by government and compliant with local regulations). Using RunPod’s Montreal region could potentially keep data in Canada, but note that RunPod’s Community Cloud means you’re running on a third-party provider’s hardware (which could be an OVH server or another data center in Montreal). Due diligence is needed to ensure that specific provider guarantees data residency and security. Other specialized clouds (Lambda, CoreWeave) currently do not have Canadian data centers – data would travel to the US, which may violate residency requirements if that’s a concern.

OpenAI and x.ai GPU-Backed Services

OpenAI Services: OpenAI itself does not rent out GPUs directly as IaaS. Instead, they offer API access to AI models (GPT-3.5, GPT-4, DALL-E, etc.) and a hosted fine-tuning service. This is a hosted inference/training API model. The pricing is per token or per image generated, rather than per GPU-hour. For example, running GPT-4 queries via OpenAI’s API might cost ~$0.03–$0.06 per 1K tokens, which indirectly reflects OpenAI’s GPU compute costs but isn’t exposed as a GPU-hour rate. For Canadian users with AI workloads, OpenAI’s API can be a solution for inference (especially for NLP tasks), but it does not guarantee data residency in Canada. Data sent to OpenAI’s API is processed wherever OpenAI’s servers are (primarily US). If the question is whether OpenAI offers something like “GPU clusters for rent” – the answer is no, they only offer model access.

OpenAI’s fine-tuning service (for models like GPT-3) charges per 1,000 tokens trained; this could be seen as roughly equivalent to paying for GPU time. For instance, fine-tuning Ada might cost $0.0004 per 1K tokens, which could equate to single-digit dollars per hour of GPU time, but the comparison isn’t direct since OpenAI abstracts away the hardware. The key point is OpenAI’s offerings are AI-platform-level (model-as-a-service), not raw infrastructure. If a Canadian company’s needs can be met by using pretrained models via API (and data sensitivity is manageable), this can save the overhead of managing GPU infrastructure. However, for custom model training or where data can’t leave Canadian soil, OpenAI’s services may not be viable.

x.ai (Elon Musk’s AI initiative): As of 2025, xAI (sometimes stylized as “x.ai”) is an AI research company building its own large-scale AI models (Musk’s Grok model, and the massive “Colossus” GPU cluster siliconangle.com siliconangle.com). They are not a cloud provider and do not offer public GPU-backed services or APIs (beyond potentially a chatbot service on X/Twitter in the future). Their activity is focused on developing AI to compete with OpenAI, rather than renting out compute. Notably, xAI is investing heavily in GPUs (reportedly assembling a 100,000+ H100 GPU supercomputer) siliconangle.com, but this is for internal use. In summary, neither OpenAI nor x.ai provides Canadian-focused GPU infrastructure for rent – OpenAI offers high-level AI model access (with no Canadian region control), and x.ai offers nothing publicly at this time aside from research updates.

Using GPU Hardware for Traditional Hosting (VPS/Shared Hosting on GPU Clusters)

If a company like Xavi.app invests in a Canadian GPU cluster for AI, an intriguing idea is to double-purpose the hardware for other hosting services (virtual private servers, web hosting, email, etc.) when GPUs are underutilized. This could potentially increase ROI on the expensive hardware. Here we explore feasibility and profitability:

  • Technical Feasibility: Modern GPU servers are typically high-end machines with powerful CPUs (e.g. dual Xeons/EPYCs), large RAM, and fast networking – resources that can certainly run general-purpose workloads alongside AI jobs. It is technically feasible to partition servers such that the GPU is available to AI containers/VMs, while other containers/VMs use only CPU/RAM for tasks like web hosting. Using virtualization (VMware, KVM) or containerization (Docker/Kubernetes), one can allocate a portion of the machine to traditional services. For example, a server with 8 GPUs and 64 CPU cores could dedicate 4–8 cores to a few Linux VPS instances (hosting websites, databases, email), while leaving GPUs and remaining cores free for on-demand AI jobs. Technologies like NVIDIA MIG (Multi-Instance GPU) can even split a GPU into smaller slices, though MIG is more useful for running multiple smaller AI tasks concurrently and less relevant to non-GPU workloads. Essentially, a GPU server is a superset of a normal server – it can do anything a normal server does plus GPU work. Cloud providers like AWS allow running non-GPU work on GPU instances (you pay for the whole instance regardless), and some users indeed run mixed workloads if it’s efficient.

  • Performance & QoS Considerations: The main challenge is ensuring that running traditional hosting services on a GPU node doesn’t interfere with AI jobs (and vice versa). AI training jobs are typically intensive but mostly on GPU and disk I/O; web hosting is CPU/memory and I/O intensive but light compared to AI. There could be contention on CPU and I/O if not managed – e.g., if an AI job uses all CPU cores for data loading, your web server might slow down. Careful resource allocation (cgroup quotas, VM resource limits) is needed to protect the baseline performance of the hosting services. Another consideration is uptime and scheduling: AI jobs might be bursty – you may want to power down some services to free resources for a large training run, which wouldn’t be acceptable for a 24×7 website. To avoid this, you’d likely reserve certain resources permanently for the hosting side, and treat those as “not available” for AI scheduling. This sacrifices some potential GPU server capacity, but ensures the web/email services remain stable.

  • Profitability Analysis: GPU hardware and data center costs are high, and they are usually justified by the high revenue from AI workloads. For instance, renting one A100 can easily fetch ~$2–$5 USD/hour on-demand (≈ $50–$120 per day). In contrast, a typical cloud VPS for web hosting might be $20–$50 per month. Even if a single GPU server could host, say, 50 modest VPS instances (each paying $20/month), that’s $1,000/month total – which is far less than what even one GPU could earn if utilized ~50% of the time for AI. For example, an 8×A100 server might cost on the order of $150k capital; leasing those GPUs at $2/hr each could yield ~$12k/month if near full utilization, dwarfing the ~$1k/month from using it as a conventional VPS host. Thus, if AI demand exists, it’s far more profitable to use the hardware for AI rather than low-margin web hosting.

    However, if the GPUs would otherwise sit idle, any additional income helps. Integrating hosting could guarantee a baseline revenue to cover some costs (power, cooling, staff) during AI workload lulls. One strategy could be to run internal services or less critical client workloads on the cluster when AI jobs aren’t scheduled, and gracefully throttle or migrate them when GPUs spin up. Another angle is offering high-performance VPS for specialized customers – for example, “GPU-adjacent” cloud services (like a Jupyter notebook service with occasional GPU access, or data preprocessing servers) that complement AI training jobs.

  • Operational Complexity: Running a traditional hosting business on the side of an AI rental business adds complexity. You’d need to provide the support, SLAs, and security isolation expected in hosting. There’s a risk that a sudden AI client demand might tempt one to reclaim resources from the hosting side, harming those customers’ experience. Unless carefully automated or contractually managed (e.g. offering preemptible VPS at a discount to developers who don’t mind occasional stoppages), this could lead to reliability issues. Many cloud providers keep their general compute and specialized GPU clusters separate for this reason – to simplify resource management and ensure each service meets its SLA.

  • Real-World Examples: There aren’t many public examples of providers explicitly advertising mixed GPU + general hosting from the same machines, but it’s conceptually similar to how cloud providers utilize hardware: AWS might use the same underlying server model to offer a GPU instance type and a CPU instance type. In your own data center, you could likewise carve some machines or portions of machines for different purposes. For instance, if Xavi.app’s cluster has some nodes not in heavy AI use, those could permanently run ancillary services (company’s websites, etc., which effectively monetizes them internally). Another possibility is offering GPU-backed VDI or workstations for remote users (which uses the GPU for graphics when free, and otherwise the machine can crunch AI tasks).

Bottom Line: It is technically feasible to run VPS/web hosting on the same hardware as an AI GPU cluster using virtualization and careful scheduling. If GPUs are frequently idle, this can generate extra revenue and improve utilization. However, the profit per hardware-dollar from traditional hosting is much lower than from AI rentals. From a strategic perspective, one should ensure that adding hosting services doesn’t undercut the primary business of AI infrastructure. It might make sense if targeting specific clients who need both AI training environment and regular hosting in one package (niche scenario). Otherwise, maintaining focus on GPU rentals (which themselves can be sold in Canada at a premium due to residency needs) may be more impactful. A compromise could be to allocate a small portion of the cluster (or older GPU nodes) to offer Canada-based VPS or cloud storage as a value-add service, while keeping the majority of GPUs available for on-demand AI tasks that bring in higher revenue.

International Competitiveness and Strategic Insights

Although this report centers on Canadian infrastructure, it’s crucial to note the international competitiveness in GPU cloud pricing:

  • The big U.S. cloud providers (AWS, Azure, GCP) have far more capacity and often lower prices in their larger regions. A Canadian AI startup might be tempted to run workloads in us-east-1 (N. Virginia) or us-west-2 (Oregon) for an immediate ~10-15% cost savings on AWS, for example pcr.cloud-mercato.com. Similarly, Azure’s AI VM prices are somewhat lower in U.S. regions than in Canada. The trade-off is data leaving Canada and higher network latencies for Canadian end-users.

  • Specialized GPU clouds (Lambda, RunPod, etc.) demonstrate that prices under $2/hr per A100 are achievable lambda.ai runpod.io. This is well below the on-demand rates in Canada’s big clouds (which are ~$5–6/hr for an A100). Even factoring in currency and some overhead, there is a large gap. This suggests that if Xavi.app (or any Canadian provider) can source hardware and run it efficiently, they could price significantly below AWS/Azure in Canada and still profit. In fact, OVHcloud’s aggressive pricing (e.g. $0.88/hr for a V100S ovhcloud.com) shows a local provider can undercut U.S. clouds by a wide margin. The demand for Canadian sovereignty and lower latency could allow charging a bit more than U.S. market rates, but to be competitive, Canadian services should aim to narrow the price gap.

  • When marketing to Canadian enterprise and government clients, emphasize data residency, privacy, and support, as these can justify higher pricing than bare-bones international offerings. However, savvy AI developers (especially those paying out-of-pocket or startups on a budget) will know about the cheap options like Vast.ai or Lambda in the U.S. They may only choose a Canadian provider if required or if the provider offers turnkey value (e.g. managed solutions, easier procurement, or bundled services).

Strategic Recommendations for Xavi.app Leadership: Focus on the high-value GPU rental market in Canada by leveraging data residency and possibly hybrid offerings. Consider partnerships or reselling – for instance, reselling capacity on platforms like RunPod or Vast.ai could fill your cluster’s spare cycles (effectively treating them as spot instances for external users). Offering complementary services (like secure data storage in Canada, or an AI platform layer on top of raw GPUs) can differentiate from pure commodity GPU sellers. If adding traditional hosting, target high-performance computing adjacency (clients who need CPUs near their GPUs, or AI developers who also need to host an app or database in Canada). Keep in mind the price trends: new GPUs (H100, and upcoming NVIDIA models) often command premium prices initially; older GPUs (V100, P100) get commoditized. As of 2025, H100s are expensive (~$7/hr on Azure US datacrunch.io, ~$2.5–$3/hr on niche clouds), but that will fall. Stay agile in updating hardware and pricing models.

Finally, maintain a comparative edge by monitoring global rates: for example, if Lambda drops A100 prices further or if AWS introduces H100 in Canada at a certain price, adjust accordingly. The goal should be to convince Canadian customers that they can get world-class AI infrastructure at home – meaning cost-efficient, scalable, and compliant. This combination of competitive pricing, Canadian data protection, and service flexibility will position Xavi.app strongly against both the hyperscalers and the bargain GPU clouds in the U.S.

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