Cloud Providers for AI and ML Workloads: 2026 Comparison

Gartner forecasts worldwide AI spending will reach $2.59 trillion in 2026. This is an approximately 47% jump year over year. AI infrastructure is the largest slice of that total, at over 45% of all AI spending.
Most teams choose between cloud providers for AI and ML workloads by lining up hourly GPU rates and picking the lowest number. Three months later, the invoice looks nothing like the estimate.
The hourly rate is the least useful number in this decision. This guide compares the five major hyperscalers on the costs that actually move your bill, using verified July 2026 pricing. Then it prices one real workload from start to finish.

Which cloud is right for AI and ML workloads?

The right cloud depends on whether you are training models or serving them, and on where your data is legally allowed to sit.

Cloud providers for AI and ML workloads compared

There is no hourly price column. The next two sections explain why.

Why AI and ML workloads need different infrastructure

A standard cloud VM is built to run an application. An AI workload is built to push enormous volumes of data through a processor as fast as possible. Three things change as a result.

GPU acceleration: Model training runs the same mathematical operation across millions of values at once. A CPU handles those in sequence. A GPU handles thousands in parallel, which is why training that takes weeks on CPUs takes hours on GPUs.

Interconnect between GPUs: Once a model is too large for a single GPU, the GPUs must exchange data constantly during training. If that link is slow, you pay for eight GPUs and get the throughput of four. This is why NVLink, InfiniBand, and RDMA appear on GPU cloud spec sheets and never on standard VM spec sheets.

Storage throughput: A training run reads its dataset over and over. If storage cannot feed the GPU fast enough, the GPU sits idle and bills you anyway.

Training workloads and inference workloads are different problems

Training is a burst. You need many GPUs for a short window, then you need none. What matters is provisioning speed, interconnect quality, and whether capacity is available when you want it.
Inference is constant. One endpoint serving user requests runs 730 hours a month, every month. What matters is cost per hour, latency to your users, and whether the endpoint scales down when traffic drops.
Most teams optimise their cloud choice for training, then discover inference is where the money actually goes. Price both before you commit.

How to compare cloud GPU pricing

Check these five things before you compare:

1. Price per GPU per hour: Divide the instance rate by the number of GPUs in it. An 8-GPU node at $80 an hour is $10 per GPU, not $80. This single step removes most of the confusion in published comparisons.
2. vCPUs included: Ask how many vCPUs come with the instance, and whether more can be added without moving to a larger GPU count. A GPU with too few vCPUs behind it runs below its rated throughput and still bills at the full rate.
3. RAM included: System RAM is separate from GPU memory, and both matter. Data loading, preprocessing, and checkpoint writes all run in system memory.
4. Storage included: Check the amount, the type, and whether it is local. Local NVMe feeds a GPU far faster than network-attached storage. Also confirm whether storage is bundled or metered separately per GB per month.
5. Network and egress: Confirm the bandwidth on the instance, the interconnect between GPUs if you are training across several, and the egress rate for data leaving the cloud. Egress is the line most teams forget entirely.

The costs that never appear in the hourly rate

Four things sit outside the rate and still show up on your bill:

1. Egress: Egress is the clearest example. Moving data out of a cloud typically runs $0.087 to $0.12 per GB, and it never shows up when you compare instance pricing. Training jobs that repeatedly pull datasets from object storage rack up these charges quietly, and by the time they land on the invoice, the run is already done.
2. Idle endpoints: A persistent inference endpoint bills whether or not it receives traffic. This is consistently the most underestimated line item on managed ML platforms.
3. Managed platform premium: SageMaker carries a 20 to 25% premium over standard EC2 pricing for the MLOps layer it adds. That premium may well be worth paying. It should not be a surprise.
4. Storage: Datasets, checkpoints, and model artefacts sit on disk between runs. Forgotten volumes from completed experiments are a standard finding on any large cloud bill.

Forex exposure on USD-billed infrastructure

Every hyperscaler bills Indian customers in USD. If the rupee moves 4% against the dollar in a quarter, your infrastructure cost moves 4%, and nobody on your team did anything.
For a team spending Rs. 5 lakh a month on GPUs, that is roughly Rs. 20,000 a quarter of variance you cannot forecast or control. An INR-denominated invoice removes that line from the problem entirely.

Cloud providers for AI and ML workloads

Below is the list of cloud providers for AI and ML workloads:

1. CloudPe

As an India-native option for AI and ML workloads, CloudPe runs NVIDIA H200 instances at a flat Rs. 300 per hour, with 16 vCPUs, 128 GB RAM, and 250 GB NVMe included, from a Tier-4 facility in Mumbai.
At current rates, that is roughly $3.60 per GPU-hour for an H200, against $11 to $13 for an H100 on hyperscaler on-demand list pricing. GPU instances start at Rs. 14,500 per month. There is no minimum GPU count and no credit system to decode.

Four CloudPe products map to AI and ML workloads:

1. GPU Cloud covers both sides of the problem. The H200’s 141 GB of HBM3e memory handles long-context inference and fine-tuning that will not fit on an 80 GB H100. The RTX Pro 6000 with 96 GB of Blackwell-generation memory covers rendering, generative media, and AI content workloads.
2. Kubernetes-as-a-Service runs containerised inference at scale without your team maintaining the control plane.
3. VMs starting at Rs. 930 per month cover the data pipeline, API layer, and application services surrounding the model. Most of an AI system’s workload is not GPU-bound.
4. VPC and private networking give regulated teams an isolated environment, which matters for BFSI and healthcare deployments under RBI and DPDP obligations.
   The infrastructure behind it: data centres in Mumbai, Pune, Delhi, and Bengaluru, a 99.9% uptime SLA, support resolution under 2 hours, and 20 years of infrastructure operations. Billing is in INR, so there is no forex line in your forecast.

The infrastructure behind it: data centres in Mumbai, Pune, Delhi, and Bengaluru, a 99.9% uptime SLA, support resolution under 2 hours, and 20 years of infrastructure operations. Billing is in INR, so there is no forex line in your forecast.

2. Amazon Web Services

AWS offers the widest set of managed AI services of any provider. SageMaker covers training, tuning, and deployment. Bedrock provides API access to third-party models including Anthropic, Meta Llama, and Mistral. Trainium and Inferentia are AWS’s own chips, built to cut training and inference costs against standard GPU instances.

Where it wins: teams already running on AWS, and anyone who needs a managed service at every stage of the ML pipeline rather than building one.
Where it costs you: on-demand H100 pricing sits in the $11 to $13 per GPU-hour band, the pricing model is genuinely complicated, and the SageMaker premium sits on top of everything.

3. Microsoft Azure

Azure’s position is the Microsoft environment. Azure AI Foundry and Azure ML handle model deployment, and Azure OpenAI Service is the main route for enterprises that want OpenAI models under an enterprise agreement.

Where it wins: organisations already on Microsoft 365 and Active Directory, internal copilot-style tools, and regulated deployments needing Azure-native audit logging.
Where it costs you: the ND H100 v5 shape lists at $12.29 per hour, among the highest on-demand rates of any major provider. GPU capacity can also be constrained without a reservation.

4. Google Cloud

Vertex AI brings training, tuning, deployment, and a model catalogue into one platform. Google’s TPUs are the only widely available alternative to NVIDIA silicon at scale, and Vertex AI connects directly to BigQuery for teams whose training data already lives there.

Where it wins: large-scale training, data-heavy ML pipelines, and teams that want TPU access alongside GPUs.
Where it costs you: onboarding is steep for anyone not already on Google Cloud, and egress on repeated reads from Cloud Storage is a real cost during long training runs.

5. Oracle Cloud Infrastructure

OCI competes on architecture rather than headline price. It offers bare metal GPU shapes, which removes virtualisation overhead, and RDMA cluster networking for distributed training. Its H100 list rate of roughly $10 per GPU-hour is below AWS and Azure but not dramatically so. The real differentiator is that Oracle dropped egress fees globally in February 2026.

Where it wins: HPC-style workloads, distributed training that needs RDMA, and any workload that moves large volumes of data out of the cloud.
Where it costs you: OCI sells H100 in 8-GPU bare metal nodes with no smaller SKU, so entry cost is high. The managed ML tooling is also less developed than SageMaker or Vertex AI, meaning you build more of the pipeline yourself.

6. IBM Cloud

IBM’s AI position runs through watsonx and its hybrid cloud architecture. The pitch targets enterprises running AI workloads across on-premises systems and cloud together, under strong governance.

Where it wins: regulated industries, hybrid deployments, and organisations with existing IBM infrastructure.
Where it costs you: the smallest GPU footprint of the five, and a single-zone region in Chennai for Indian customers, which limits in-country redundancy options.

What an AI workload actually costs

Comparisons stop at the hourly rate. Bills do not. Here is one workload priced end to end.

• The scenario: fine-tune a 7 billion parameter model, then serve it in production for one month.
• Fine-tuning: 8 GPUs for 40 hours, so 320 GPU-hours
• Inference: 1 GPU endpoint running 730 hours
• Storage: 500 GB for dataset, checkpoints, and artefacts
• Egress: 500 GB of responses leaving the cloud

Note: Rates verified July 2026. Confirm live & forex rates before budgeting.

The point of the exercise: training is a one-time 320 GPU-hours. Inference is 730 GPU-hours every single month and never stops.
By the end of quarter one, the inference endpoint has consumed nearly seven times the compute of the training run everyone spent weeks planning. Price your inference endpoint first. Then price training.

Data residency and compliance for AI workloads in India

Where your GPU sits is a legal question before it is a technical one. Here are some most discussed data residency regulations that are talked about the most:

• The DPDP Act governs personal data handling in India.
• RBI requires regulated entities to store payment system data within the country.
• SEBI applies its own localisation expectations to market participants.

If your training data or inference logs contain regulated personal data, the region your workload runs in forms part of your compliance position.
The market has already moved. Investment in Indian data centres increased to $235 billion in 2025 compared to 2024.

Indian enterprises are also further ahead on deployment than most global comparisons assume. Deloitte’s 2026 State of AI research found 40% of Indian respondents report significant or full AI usage, against a global average near 28%. Demand for compliant local compute is not theoretical.

Do you need GPU infrastructure at all?

Ask this before you compare a single provider:

• A hosted inference API is almost certainly cheaper if you’re calling an off-the-shelf model a few thousand times a day. You pay per token, run no infrastructure, and your cost scales to zero when nobody is using the product.
• Owning GPU capacity starts to make sense when one of three things is true:
  – Your request volume is high and steady enough that per-token pricing exceeds the cost of a dedicated endpoint.
  – You’re running a fine-tuned or open-weight model that no API serves.
  – Your data cannot leave your environment for regulatory reasons.
• If none of those apply yet, use an API and revisit in two quarters. Buying GPU capacity you don’t need is the most expensive mistake in this category.

Conclusion

There is no single best cloud for AI and ML. It all depends on your workload, your data rules, and your budget.
Train on the provider that has the accelerator and interconnect you need. Serve on the provider that gives you the lowest steady-state cost and the right latency to your users. These two do not have to be the same provider.
Price your actual workload before committing to anyone. No spec sheet tells you what month three looks like on the invoice.