Best AI/ML and HPC Cloud Providers in 2026: GPUs, Pricing, and Comparison

Russia's cloud market reached 226.9 billion rubles in the IaaS and PaaS segment in 2025, posting nearly 37% year-over-year growth. The hottest segment was high-performance computing and GPU cloud for neural networks: according to ComNews, business demand for cloud GPU rental could grow by another 50% in 2026. As Selectel's AI vertical director Alexander Tugov noted (ComNews, February 2026), the company's monthly GPU cloud server revenue tripled in the first nine months of 2025, while MTS AI moved all of its AI model training and inference to the cloud, saving over one billion rubles in on-premise infrastructure investment.

The reasons behind the boom are clear: training even a single Llama 3 70B-scale model requires a cluster of dozens of GPUs running for weeks. Buying your own server with eight H100s means roughly $300,000 in capital expenditure — plus an engineer, electricity, and delivery lead times that can stretch to months due to the global hardware shortage. Cloud GPU for ML solves this differently: you get access to compute in hours, scale resources on demand, and pay only for actual usage time.

However, choosing a GPU cloud is far more complex than picking a regular hosting plan. A price list showing a per-GPU-hour rate doesn't reflect the true cost of training a model — you also need to factor in dataset storage, outbound traffic, checkpoint backups, and the cost of downtime if the SLA is breached. That's exactly why we put together this cloud ranking, comparing ten Russian providers not by marketing claims but by concrete parameters: actual GPU models, total cost of ownership, reliability, security, and the ecosystem available to ML teams.

Types of GPU Clouds: From IaaS to Managed ML Platforms

Before diving into the ranking, it's important to understand what categories of AI cloud services exist on the Russian market and how they differ.

GPU Server Rental (IaaS)

The classic model: the provider allocates a virtual machine or physical server with one or more GPUs. You get full control over the environment — install CUDA, frameworks, configure networking. This is the approach taken by Cloud4Y, Selectel, Timeweb Cloud, K2 Cloud, and Immers Cloud. It's a good fit for teams that want to work with their own stack without vendor lock-in and are willing to invest time in administration.

Managed ML Platforms (PaaS)

The provider delivers a ready-made environment for training and deploying models: JupyterHub, MLflow, pipelines, automated deployment. Yandex DataSphere, VK Cloud ML Platform, and Cloud.ru ML Space operate in this segment. The barrier to entry is lower, but you're tied to a specific provider's ecosystem.

GPU Clusters for HPC (Bare Metal and Dedicated)

Physical servers with high-speed inter-node connectivity — InfiniBand or NVLink — for distributed training of large models. Cloud.ru's Christofari supercomputer, Selectel's HPC clusters, and T1 Cloud's dedicated GPU servers all operate in this segment.

What approach does Cloud4Y take? The company has historically been strong in the IaaS and Dedicated GPU segment. This isn't a technological gap — it's a deliberate strategy: you get top-quality hardware and infrastructure, and you choose your ML frameworks yourself. At the same time, Cloud4Y is developing its own LLM platform for fine-tuning language models and an ML platform for model training, covering core PaaS needs without building a heavy proprietary ecosystem.

Methodology: Criteria and Weights

Each provider is evaluated across six criteria. The final score is a weighted sum of ratings from one to five.

Performance and TCO each carry 25% because for AI cloud services the main concerns are model training speed and the total cost of that training. Scalability gets 15%: it answers a real-world question — will there be enough GPUs when you need them? Ecosystem gets 10% because ML teams often bring their own stack. Security gets 10% in the overall ranking, but for fintech and public sector use cases this criterion becomes a deal-breaker.

Ranking Participants: GPU Cloud Provider Profiles

Cloud4Y GPU — A100 Cloud with a Predictable Budget

Category: IaaS GPU + Dedicated GPU. In the market since 2009.

Cloud4Y offers GPU servers for machine learning and AI workloads featuring NVIDIA A100, RTX A5000, GeForce RTX 4090, and Tesla V100 cards. ML configurations start at 18,341 rubles per month (before VAT) with hourly billing available (details at cloud4y.ru/cloud-hosting/artificial-intelligence-and-machine-learning). The key differentiator from most competitors is fixed-rate pricing with no traffic charges for select products, making your monthly bill fully predictable.

Data centers are located in Moscow and Novosibirsk, as well as in the Netherlands, Germany, and Turkey. Among Russian GPU cloud providers, this is a unique capability: if your business operates simultaneously in Russia and the EU, you don't need two separate providers. The SLA is 99.982% based on Tier III data centers, and in 2026 Cloud4Y has two Tier IV data centers under construction.

For regulated industries, Cloud4Y offers a GovCloud service certified up to Protection Class 1 (K1) under FSTEC Order No. 17 and up to Personal Data Protection Level 1 (UZ-1). The provider supplies a compliance certificate and a threat model excerpt, significantly simplifying the customer-side certification process. For AI projects in healthcare, finance, and the public sector — where data falls under strict regulation — this is a turnkey solution that lets you start training models on sensitive data without months of paperwork.

How Cloud4Y addresses reliability

In reviews of any cloud provider, you'll find mentions of outages — that's an unavoidable reality of the infrastructure business. What matters more is actual uptime performance and how quickly the provider restores service. Cloud4Y delivers an SLA of 99.982% — that's less than an hour and a half of allowable downtime per year. For comparison, most competitors offer an SLA of 99.95%, which permits up to 4.4 hours of downtime. Monitoring runs 24/7, and the investment in Tier IV infrastructure is aimed at pushing SLA above 99.995%.

Transparent billing instead of hidden fees

One common remark about Cloud4Y is that backups and antivirus protection are billed separately. In the context of GPU clouds, this is actually an advantage, not a drawback. When training neural networks, you save model checkpoints to S3 storage — not through standard VM backup mechanisms. Storing a multi-gigabyte checkpoint in object storage at a fraction of a ruble per gigabyte is significantly cheaper than overpaying for "included" backups whose cost is baked into the GPU-hour price at competitors. Cloud4Y's formula: pay only for what you actually use.

An interface built for engineers, not for marketing screenshots

Cloud4Y's control panel is geared toward experienced engineers, and newcomers will indeed need some time to get up to speed. But the target audience for a GPU cloud is DevOps engineers and ML specialists, for whom APIs and CLI matter far more than slick dashboards. Cloud4Y supports a Terraform provider and Kubernetes integration, and most teams manage GPU servers via SSH and automation scripts rather than a web interface. That said, in 2026 the company is updating the control panel based on customer feedback.

Strengths: fixed-rate pricing, data centers in Russia and Europe, GovCloud for regulated industries, LLM and ML platforms, 10-day free trial.

Limitations: narrower managed service ecosystem compared to Yandex Cloud; interface geared toward experienced users; scaling requires planning.

Best suited for: mid-sized and large businesses with predictable budgets; public sector and regulated industries; international projects requiring data centers in both Russia and the EU.

Yandex Cloud — ML Platforms and a Data Science Ecosystem

Category: Managed ML Platform + IaaS GPU.

Yandex Cloud is the technology leader on the Russian market in terms of ecosystem breadth: over 100 services, DataSphere for managed ML, Kubernetes integration, and Data Proc. Available GPU models include A100 with 80 GB of memory and T4. Since January 2026, the GPU cost on the V4 platform is 1,016.90 rubles per hour (VAT included) with per-second billing by units.

DataSphere provides a JupyterLab environment with the ability to switch between configurations right inside a notebook — from CPU to a GPU cluster. Up to 10 GB of storage per project is free; anything beyond that is billed separately. New customers receive a starter bonus of 4,000 rubles valid for 60 days.

Strengths: 100+ Yandex Cloud services, DataSphere, CDN, ML marketplace, extensive documentation.

Limitations: complex unit-based pricing, high GPU-hour cost (over 1,000 rubles/hour), outbound traffic charges significantly inflate the final bill, strong vendor lock-in when using DataSphere.

Best suited for: ML teams already in the Yandex ecosystem, Data Lake projects, AI startups leveraging the starter bonus.

VK Cloud — ML Platform for Enterprise AI

Category: Managed ML Platform + IaaS GPU. Data centers: 3 (Moscow ×2, Kazakhstan).

VK Cloud offers Tesla V100, A100 (40 GB and 80 GB variants) GPU accelerators, and since 2024, NVIDIA L4 on the Ada Lovelace architecture. Pricing is not publicly disclosed: quotes are provided through a personal account manager with a choice of annual, monthly, or hourly billing. The ML Platform includes AutoML and pre-configured environments for model training. The platform is listed in the Russian software registry and complies with FZ-152 requirements.

Strengths: ML Platform with AutoML, broad VK ecosystem, generous bonuses for new customers.

Limitations: opaque pricing (available only on request), limited GPU fleet, PaaS focus reduces flexibility for custom setups.

Selectel — H100 Rental for HPC and High-Performance Computing

Category: IaaS GPU + Dedicated + HPC. In the market since 2008, 31,000+ clients.

Selectel offers the widest selection of GPUs among Russian providers: from Tesla T4 and A2 to A100, H100, and RTX 4090. A single server can house up to eight H100s, and servers can be combined into clusters with up to 100 Gbps networking. Ready-made configurations launch in two minutes; custom builds take one to five days. Traffic is unlimited at 1 Gbps or 300 TB per month. According to ComNews citing the company's AI vertical director, monthly GPU cloud server revenue tripled in the first nine months of 2025, and the company announced a 10-billion-ruble investment in this direction through 2031 (Selectel Tech Day 2025).

For high-performance computing, Selectel offers HPC clusters based on H100 with up to 67 teraflops of peak FP64 performance (SXM version; the PCIe version delivers roughly 51 teraflops) and fourth-generation Tensor Cores optimized for training large language models.

Strengths: 6 proprietary Tier III data centers, the most comprehensive certification package (FZ-152 UZ-1, FSTEC, PCI DSS, ISO 27001), free DDoS protection, private networking up to 25 Gbps.

Limitations: all data centers are in Russia — no international locations, pricing higher than some competitors, no managed ML platform.

Cloud.ru — The Christofari Supercomputer for Large-Scale Neural Network Training

Category: ML Platform + IaaS + HPC. Supercomputers: Christofari and Christofari Neo.

Cloud.ru is the only Russian provider offering access to supercomputer-class resources: Christofari built on V100 GPUs with 6.67 petaflops of performance, and Christofari Neo built on A100 GPUs with 11.95 petaflops — over 1,700 GPUs in total. The ML Space platform enables distributed training across 1,000+ GPUs — the only cloud service of its kind in Russia.

Billing follows a pay-as-you-go model; standard VMs are provisioned in roughly fifteen minutes (per the Tproger review; GPU configurations may differ), while custom setups may take up to one business day. GPU rates were updated effective January 1, 2026. In addition to virtual machines, dedicated servers are available with a minimum rental term of one month.

Strengths: Russia's largest GPU cluster, ML Space with a full ML development lifecycle, 80+ IaaS/PaaS/ML services.

Limitations: strong ecosystem lock-in (vendor lock-in), complex pricing, oriented toward the large enterprise segment.

MTS Cloud — Telecom Integration for AI Cloud Services

Category: IaaS GPU. Operator: MTS Web Services.

MTS Cloud (MWS) is the cloud division of Russia's largest telecom operator. MTS AI moved all of its AI model training and inference to the cloud, saving over one billion rubles in on-premise infrastructure investment. According to MWS Cloud's own analytics, the Russian IaaS and PaaS market reached 226.9 billion rubles in 2025.

Strengths: telecom infrastructure, integration with MTS services, FZ-152 compliance.

Limitations: limited publicly available GPU fleet, fewer ML services compared to Yandex and Cloud.ru.

Timeweb Cloud GPU — Quick Start for Developers and Startups

Category: IaaS GPU. Data centers: Moscow, Saint Petersburg, Novosibirsk.

Timeweb Cloud offers GPU instances with NVIDIA T4 and A100 starting at 50 rubles per hour with hourly billing (per the EasyLinkLife review, March 2026). Pre-built images with PyTorch and TensorFlow, S3 storage integration, and 99.98% SLA. The main advantage is simplicity: you can spin up a working environment for model training in minutes, and Russian-language support is available 24/7 via chat, Telegram, and phone.

Strengths: the lowest barrier to entry, straightforward pricing with no calculators needed, live support.

Limitations: no FSTEC or FSB certifications — not suitable for government contracts, limited selection of GPU models, no HPC clusters.

T1 Cloud — H100 Rental for the Public Sector and Large Enterprises

Category: IaaS GPU + Dedicated. Configurations from 1 to 8 GPUs per virtual machine.

T1 Cloud offers servers with NVIDIA A100 and H100, and has announced plans to adopt B200, which will enable LLM training at twice the speed of H100. The H100 in HPC configuration (SXM) delivers up to 67 teraflops of peak FP64 performance — three times faster than the A100 (the PCIe version provides roughly 51 teraflops; the exact specification depends on the server configuration). Billing follows a pay-as-you-go model, SLA is 99.95%, and support is available 24/7.

Strengths: H100 with InfiniBand, B200 roadmap, certified infrastructure for the public sector.

Limitations: less publicly available pricing information, oriented toward large enterprises and government.

K2 Cloud — The Broadest GPU Fleet Among Russian Providers

Category: IaaS GPU. GPUs: H100, A100, L40S, L4, T4.

K2 Cloud stands out with the most diverse set of GPU models on the Russian market: from budget T4s for inference to H100s for large-scale training, and L40S — a versatile card for both inference and graphics — available at a 50% discount. Billing is hourly or with 3-, 6-, or 12-month commitments at up to 25% off. vCPU, RAM, and disk configurations are tailored individually.

The company has launched a public Bug Bounty program with rewards up to 500,000 rubles, and the K2 Cloud backup service took first place in the CNewsMarket 2026 BaaS provider ranking.

Strengths: 5 GPU types in a single cloud, flexible commitments, Bug Bounty program, CNewsMarket #1 in BaaS.

Limitations: fewer managed ML services, IaaS-focused.

Immers Cloud and HPC Park — A Niche Cloud for Neural Networks with MIG Virtualization

Category: IaaS GPU + Containers. GPUs: A100, H100, H100 NVL, B5000 (Blackwell).

Immers Cloud and the associated HPC Park platform are niche providers targeting researchers and small ML teams. The key feature is Multi-Instance GPU (MIG) support, which allows splitting an A100 into seven independent instances for running different tasks in parallel on a single accelerator. HPC Park offers containers with 1/7 of an A100 (up to 28 units) or full A100s (up to four), along with up to two terabytes of storage. New customers can receive a grant of 30,000 rubles or more for up to one month of testing.

Strengths: MIG virtualization, Blackwell B5000 support, testing grant.

Limitations: niche provider, limited scale, researcher-oriented.

Summary Table: GPU Models, Pricing, and SLA

Prices were collected from official provider websites in February–March 2026. Exchange rate: 1 USD ≈ 88 rubles.

* Price before VAT. Actual cost depends on vCPU, RAM, and disk configuration. Billing models vary: monthly, hourly, per-second.

** Cloud4Y: starting price for an ML configuration. Cost depends on GPU model, vCPU, and RAM. Details: cloud4y.ru/cloud-hosting/artificial-intelligence-and-machine-learning

*** K2 Cloud: up to 25% discount with a 3–12 month commitment; L40S at 50% off as a promotional offer.

AI and ML Cloud Ranking: Final Scores

Final Score Breakdown

Leader — Selectel (4.20). Top reliability, the broadest GPU fleet (T4 through H100), the most comprehensive certification package (including PCI DSS and ISO 27001), HPC clusters. Falls behind on TCO — pricing is higher than some competitors — but for businesses with strict regulatory requirements and a need for high-performance computing, this is the optimal choice.

Second place — Cloud.ru (4.15). The technology leader in GPU cluster scale thanks to the Christofari supercomputers. ML Space delivers a full ML development lifecycle. However, strong ecosystem lock-in and an orientation toward the large enterprise segment narrow the target audience.

Third place — Yandex Cloud (3.85). The leader in ecosystem breadth: DataSphere, 100+ services, the best documentation. But the GPU-hour cost (over 1,000 rubles) and the difficulty of predicting the final bill bring the score down. The best choice for those already working within the Yandex ecosystem.

Fourth place — Cloud4Y (3.75). The strongest on the price-to-security ratio. Fixed-rate pricing with no traffic charges is a key advantage for budget planning. A unique offering among Russian providers — data centers in both Russia and Europe simultaneously. GovCloud certification up to K1 and UZ-1 is available for the public sector. Cloud4Y trails the leaders in GPU fleet scale and ML ecosystem functionality, but for teams that prioritize stable hardware and a predictable bill over dozens of managed services, Cloud4Y gets the job done.

Fifth–sixth place — Timeweb Cloud and T1 Cloud (3.55). Timeweb wins on TCO and ease of entry but falls short on security and scale. T1 Cloud is a strong player with H100s and B200 plans, oriented toward the public sector and large enterprises.

Hidden Costs: How a 50-Ruble GPU Hour Turns Into a Six-Figure Bill

The price list shows the cost per GPU hour, but the actual bill for training a model is made up of a whole set of components that pricing pages prefer not to mention.

TCO Calculation Scenario

Training a mid-sized language model: 4 × A100 80GB, 720 hours of continuous operation per month, 2 TB of data on NVMe storage, 500 GB of outbound traffic for downloading checkpoints.

* Cloud4Y: cost depends on GPU model and configuration. ML configurations start at 18,341 rubles/mo. Calculate the cost for your workload: cloud4y.ru/cloud-hosting/artificial-intelligence-and-machine-learning

** Timeweb: 4 × A100 × 50 rubles/hr × 720 hrs = 144,000 rubles. However, a 4-GPU configuration may not be available.

*** Cloud4Y: traffic is included in the price for select products.

Note on Yandex Cloud: the 1,016.90 rubles/hr rate applies to the GPU V4 platform (effective 01/23/2026). The exact GPU model corresponding to this platform should be verified — it may be H100-class rather than A100. For A100 on earlier platforms, the cost may be lower.

The key takeaway from this table: direct price comparisons between providers are misleading without accounting for the specific configuration — each provider's minimum rate refers to a different GPU model and resource bundle. Cloud4Y's fixed monthly rates ensure budget predictability for sustained workloads. Timeweb is attractive for short experiments thanks to its low hourly rate. Yandex Cloud's per-second unit-based billing offers maximum flexibility, but the final bill is difficult to forecast in advance. Our recommendation: request a quote for your specific configuration from two or three providers and compare TCO, not price lists.

A separate category of hidden costs is migration. If you decide to move 200 terabytes of data to another provider, outbound traffic charges alone can run into tens of thousands of rubles. This factor — vendor lock-in at the data level — should be considered when choosing a platform with a three-to-five-year horizon.

GPU Cloud Scalability: How Many Cards Can You Get, and How Fast?

One of the key criticisms of mid-sized providers is limited resource availability during peak demand. That's a fair point, but it needs context.

Ask yourself: how many GPUs do you actually need? According to market reviews, the average enterprise GPU cloud request is for 4 to 16 cards. Projects requiring 256 or more GPUs simultaneously represent a tiny fraction of the market (Cloud.ru's Christofari and Selectel's HPC clusters serve exactly that segment).

Cloud4Y guarantees resource availability for standard configurations of 1 to 8 GPUs. For predictable workloads, dedicated pools with fixed reservations are available. Scaling requires planning — it's not "push a button and get it in a second" like the hyperscalers, but for 90% of ML tasks, it's more than enough. Selectel and K2 Cloud offer faster provisioning thanks to a larger pool of pre-built configurations (as fast as two minutes at Selectel). Yandex Cloud and Cloud.ru win on absolute scale — hundreds of GPUs for distributed training.

More critical than scale is the number and geography of availability zones. Selectel has six proprietary data centers; Cloud4Y has locations in both Russia and Europe; VK Cloud has two sites in Moscow and one in Kazakhstan. For workloads that need multi-region coverage across Russia and the EU simultaneously, Cloud4Y remains the only option among Russian providers.

Reliability and SLA: How GPU Clouds Handle Incidents

Reliability deserves a separate deep dive because for GPU workloads the cost of downtime is far higher than for conventional hosting. If model training is interrupted halfway through and the last checkpoint was four hours ago, you lose not just time but money for the GPU hours already consumed.

Cloud4Y delivers one of the highest SLA figures on the Russian market — 99.982%, which allows for less than an hour and a half of downtime per year. For mission-critical AI workloads, this means minimal risk of losing compute resources. The investment in Tier IV infrastructure is aimed at raising the bar even further.

Incidents happen to every provider without exception — both Yandex Cloud and Selectel have had publicly reported outages. What matters isn't zero probability of an incident (that's unattainable) but detection speed, transparency in client communication, and time to recovery. Our recommendation for any GPU project: save checkpoints every one to two hours and use fault-tolerant pipelines with automatic training restart.

GPU Cloud Security: Certifications and Technologies

For AI projects in finance, healthcare, and the public sector, security is a deal-breaker. However, it's important to distinguish between two levels: paper certifications (FSTEC, FSB, PCI DSS) and actual technological safeguards.

Certifications: Who Needs What

Cloud4Y and Selectel offer the most comprehensive set of Russian certifications. Both companies have certified segments for hosting government information systems. Cloud4Y additionally provides GovCloud with certification up to Protection Class 1 — a critical requirement for organizations working with SMEV, EGISZ, and ESIA.

Technologies: What Stands Behind the Certificates

A certificate confirms; technology implements. GPU clouds face specific threats: data leakage from GPU memory between tenants (GPU memory is not automatically cleared when switching between clients), data interception during inter-node transfers within a cluster, and unauthorized access to checkpoints and datasets.

What to look for when choosing a provider: encryption of data at rest and in transit (TLS 1.2 and above), GPU tenant isolation (MIG virtualization, vGPU, or physical separation), network-level DDoS protection, access monitoring and auditing with SIEM integration, and Bug Bounty programs as an indicator of a proactive security posture (Cloud4Y and K2 Cloud run such programs).

The ML Platform Ecosystem: Managed Services vs. Freedom of Choice

The gap between providers in ML ecosystem functionality is one of the most visible. Yandex Cloud with DataSphere and Cloud.ru with ML Space offer a full cycle from data preparation to model deployment. Cloud4Y, Selectel, and K2 Cloud bet on IaaS with Kubernetes — you choose MLflow, Kubeflow, or any other tool yourself.

This isn't a technology gap — it's a difference in philosophy. Managed platforms lower the barrier to entry: no need to configure a cluster, install drivers, or set up distributed training. But they create dependency: a pipeline built for DataSphere isn't easy to migrate to another provider. Cloud4Y's IaaS approach gives you full freedom — you use standard tools (PyTorch, HuggingFace, vLLM) that work identically on any infrastructure.

Cloud4Y complements its IaaS approach with a proprietary LLM platform for fine-tuning language models and an ML platform for model training. This isn't a competitor to DataSphere in terms of features, but it's a sufficient baseline PaaS for teams that need an out-of-the-box tool without deep vendor lock-in.

GPU Cloud Support: Response Speed and Expertise

For GPU workloads, support quality is more critical than for conventional hosting: if a CUDA driver doesn't work with your version of PyTorch or an error occurs during distributed training, a first-line support team with templated answers is useless. You need engineers who understand GPU infrastructure.

Cloud4Y provides 24/7 support, and the company acknowledges that response times on standard requests haven't always been optimal historically. For GPU clients, a dedicated account manager and a priority support line are assigned, reducing response times. The 10-day trial period lets you evaluate support quality on real tasks before signing a contract.

Selectel offers free technical support with availability on weekends and holidays. Timeweb Cloud stands out with live Russian-language support via Telegram — for developers, this is often the deciding factor. Yandex Cloud provides different support tiers depending on the subscription level. Cloud.ru accompanies large clients with dedicated architectural consulting.

Who Should Choose What: GPU Cloud Scenario Matrix

Conclusions and Recommendations

GPU cloud is a complex product where resource availability and support matter more than a low hourly rate. A rate that looks like a bargain at first glance can turn into a hefty bill once you factor in traffic and downtime.

Evaluate TCO. Fixed-rate pricing (for example, Cloud4Y) saves 20–40% compared to per-minute billing for sustained workloads.

For 90% of use cases, a guaranteed allocation of 4–16 GPUs matters more than the theoretical ability to rent thousands. Prioritize predictability over scale.

Security requires a symbiosis: an FSTEC license and GPU memory encryption. For regulated industries, you need both (as offered by Cloud4Y and Selectel).

Test on real workloads. Cloud4Y gives you 10 days free, Yandex Cloud offers a 4,000-ruble bonus, and Timeweb provides 3 days. Compare inference speed and training times.

Plan for 3–5 years ahead. Migrating terabytes of data costs time and money. Evaluate the cost of exit before signing the contract.