AI development requires powerful GPU resources and cloud providers are making it easier than ever to access high-performance compute without heavy upfront investment. Whether you’re training large-scale models, fine-tuning LLMs or deploying inference at scale, choosing the right GPU provider is the best way to optimise cost efficiency, performance, and scalability.
Below, we compare some of the best cloud GPU providers for AI in 2025 with their features, pricing and ideal use cases.
1. AWS
Amazon Web Services (AWS) offers a wide selection of cloud GPU instances through its EC2 family. With GPUs like NVIDIA A100, H100, V100 and T4, AWS provides scalable options tailored for both training and inference. Their ecosystem is deeply integrated with SageMaker for machine learning workflows.
Features of AWS
The key features of AWS include :
- Wide global data centre presence for low-latency performance.
- Advanced networking with Elastic Fabric Adapter (EFA) for high-performance distributed training.
- Support for mixed-precision training with NVIDIA Tensor Cores.
- Deep integration with AWS services like S3, DynamoDB, and Lambda for end-to-end AI pipelines.
- Enterprise-grade security, compliance, and identity management with IAM.
Pricing Model of AWS
AWS offers different kinds of pricing models, so you can choose according to your workload needs :
- Offers on-demand, spot and reserved pricing models, giving flexibility depending on workload needs.
- Spot Instances can save up to 70–80% for training workloads that can tolerate interruptions.
- Reserved Instances provide lower long-term rates for enterprises running continuous AI training.
- On-demand pricing allows quick scaling for inference workloads without upfront commitments.
- GPU pricing starts at around $0.65/hr for NVIDIA T4 and can go above $32/hr for A100s, suiting everything from small-scale inference to large LLM training.
Who Should Use AWS
You should use AWS if you are :
- Training large-scale LLMs with A100/H100 clusters.
- Real-time inference and model deployment with T4 or V100.
- Enterprise-grade ML pipelines with SageMaker.
- Scalable AI for industries like healthcare, finance and autonomous vehicles.
2. Microsoft Azure
Microsoft Azure delivers GPU resources via its NC, ND and NV-series virtual machines. Azure is especially strong in enterprise AI, offering tools like Azure Machine Learning, seamless integration with Microsoft 365 and compatibility with popular frameworks like PyTorch and TensorFlow. Azure is often used by enterprises already using Microsoft services, as integration with Active Directory, Office 365 and Power BI creates an end-to-end solution.
Features of Microsoft Azure
The key features of Microsoft Azure include :
- Wide range of GPU VMs optimised for different workloads like inference, training and visualisation.
- NVIDIA H100 and A100 availability for demanding AI training.
- Advanced networking options with InfiniBand for distributed workloads.
- Strong hybrid and multi-cloud support through Azure Arc.
- Enterprise compliance and security are aligned with Microsoft’s ecosystem.
Pricing Model of Microsoft Azure
Microsoft Azure has flexible pricing models that make it easier for you to choose for your AI workload :
- Flexible pay-as-you-go and reserved capacity pricing make it useful for varying AI project scales.
- Supports low-priority VMs (similar to AWS spot) at significant discounts for non-critical training.
- Reserved plans are best for enterprises training models like GPT or Llama at scale.
- Offers dedicated VM families (like ND-series for AI/ML), ensuring predictable costs.
- GPU pricing starts from about $0.90/hr for NVIDIA T4 and scales up to $30+/hr for A100/H100.
Who Should Use Microsoft Azure
You should use Microsoft Azure if you are working on :
- AI research with advanced GPU clusters.
- Enterprises building AI pipelines integrated with Microsoft products.
- High-performance workloads in finance, healthcare and engineering.
- Hybrid AI deployment across on-premise and cloud.
3. Google Cloud
Google Cloud offers some of the most cost-effective and performance-optimised GPU options in the market. Through its Compute Engine, you can access GPUs like T4, V100, A100 and H100. Google Cloud also provides TPUs (Tensor Processing Units) for deep learning workloads, giving it a unique advantage for AI research.
Features of Google Cloud
The key features of Google Cloud include :
- Access to NVIDIA A100 80GB and H100 Tensor Core GPUs for LLMs.
- TPUs for highly parallelised AI tasks.
- Deep integration with Vertex AI, Google’s managed ML platform.
- Per-second billing, ensuring cost efficiency.
- High-performance networking with 100 Gbps InfiniBand.
Pricing Model of Google Cloud
Google Cloud caters to different audiences and offers a flexible pricing model for GPUs :
- Pricing flexibility with on-demand, committed use discounts, and spot VMs.
- Sustained-use discounts automatically apply for long-running AI workloads.
- Spot VMs can reduce training costs by up to 60%.
- Specialised machine families for AI ensure cost-efficient GPU use.
- GPU pricing starts at $0.35/hr for NVIDIA T4 and goes up to $30/hr+ for A100s.
Who Should Use Google Cloud
You should use Google Cloud if you are working on :
- Cost-efficient large AI model training
- Scalable inference for SaaS AI applications.
- End-to-end ML workflows with Vertex AI.
4. Lambda Labs
Lambda Labs is a GPU cloud provider focused on AI and machine learning workloads. Unlike hyperscalers, Lambda offers AI-specific infrastructure with transparent pricing and minimal vendor lock-in. Its GPUs include the latest A100s, H100s and RTX 6000 Ada, providing a balance of performance and cost. Lambda is popular among AI startups, research labs and developers who value a GPU-first approach without paying the premium of hyperscalers.
Features of Lambda Labs
The key features of Lambda Labs include :
- Dedicated bare-metal GPU servers for max performance.
- Cloud clusters with NVLink and InfiniBand for distributed training.
- Transparent hourly and monthly pricing.
- Pre-installed deep learning frameworks (PyTorch, TensorFlow, JAX).
- Hybrid deployments with Lambda Echelon (dedicated clusters).
Pricing Model of Lambda Labs
Lambda Labs offer transparent pricing for cloud GPUs for AI :
- Simple hourly GPU pricing with no hidden costs.
- Discounts are available for long-term rentals, making it a good choice for training larger AI models.
- Transparent, developer-friendly pricing aimed at startups and researchers.
- Pricing flexibility across cloud and on-prem options, helping hybrid AI projects.
- GPU pricing starts at $0.50/hr for NVIDIA RTX 4090 and scales up to $2–$3/hr for A100s.
Who Should Use Lambda Labs
You should use Lambda Labs if you are working on :
- Cost-effective LLM training.
- Fine-tuning AI models on A100 or H100.
- Startups running AI inference at scale.
- Research teams that need bare-metal GPU performance.
5. Paperspace
Paperspace, acquired by DigitalOcean is a developer-friendly GPU cloud provider known for ease of use and affordability. Its platform includes Gradient, which provides a simple environment for model training and deployment. Paperspace is mostly used by startups, individual developers and small teams due to its simplicity and low entry cost.
Features of Paperspace
The key features of Paperspace include :
- Affordable GPU instances starting under $0.50/hour.
- Gradient notebooks for ML development.
- Pre-installed deep learning frameworks.
- Team collaboration and model versioning tools.
- Flexibility to run workloads from Jupyter notebooks to production-scale inference.
Pricing Model of Paperspace
Paperspace offers a flexible pay-per-use pricing across multiple GPU types. Other pricing includes :
- Different Gradient tools determine your limits. You get charged for storage overages ($0.29/GB) and for compute usage.
- Good for smaller AI teams due to no minimum commitment.
- Auto-shutdown features reduce unused compute charges, perfect for experiments.
- GPU pricing starts at $0.40/hr for NVIDIA T4, scaling up to $3/hr+ for A100s.
Who Should Use Paperspace
You should use Paperspace if you :
- Want affordable AI training and inference.
- Working on educational and research projects.
- Are small team experimenting with LLM fine-tuning.
- Are moving from prototyping to production.
6. Vast.ai
Vast.ai is a decentralised GPU marketplace that allows users to rent compute from providers around the globe. Its biggest advantage lies in pricing, often significantly cheaper than hyperscalers. GPUs range from consumer cards like RTX 3090 to enterprise GPUs like A100 and H100.
Features of Vast.ai
The key features of Vast.ai include :
- Decentralised marketplace with global availability.
- Extremely competitive pricing with up to 80% savings.
- Support for a wide range of GPUs, including consumer-grade cards.
- Transparency in performance, pricing and reliability metrics.
- Flexible contracts for short- or long-term usage.
Pricing Model of Vast AI
Vast.ai operates on a marketplace pricing model, where GPU providers compete to offer the lowest prices :
- Prices can be 30–70% cheaper than traditional providers for AI workloads.
- You can pay only for the actual runtime, avoiding idle charges.
- Great for researchers and startups needing budget-friendly training GPUs.
- GPU pricing starts as low as $0.20/hr for RTX 3090s, with A100s around $1.50–$2.50/hr.
Who Should Use Vast.ai
You should use Vast.ai if you are :
- Running low-cost AI model training and inference.
- Researchers who need to run short experiments.
- Startups testing workloads before scaling to enterprise clouds.
- Developers who need access to diverse GPU options at low prices.
7. CoreWeave
CoreWeave has grown rapidly as a GPU-native cloud platform built for AI and HPC. Its strong suit is performance and scalability. From startups to enterprises, CoreWeave caters to businesses of all scales.
Features of CoreWeave
The key features of CoreWeave include :
- Specialised for GPU compute with A100, H100, L40s and RTX 6000 Ada GPUs.
- Ultra-fast networking with 100 Gbps+ interconnects.
- Massive GPU scale designed for AI training at the enterprise level.
- Kubernetes-native orchestration for AI workflows.
- Reserved capacity for guaranteed GPU availability.
- Partnership ecosystem with AI-first companies for optimised workloads.
Pricing Model of CoreWeave
CoreWeave offers a highly competitive pricing model, including :
- Offers spot pricing for lower-cost training jobs that can handle interruptions.
- Transparent cost breakdowns with billing per second, maximising efficiency.
- Optimised for AI, rendering, and simulations, ensuring high performance per dollar.
- GPU pricing starts at $0.45/hr for NVIDIA A4000 and goes up to $3/hr+ for A100s.
Who Should Use CoreWeave
You should use CoreWeave if you are :
- Running large-scale LLM training requiring hundreds of GPUs.
- Deploying high-performance inference workloads at scale.
- Building enterprise AI and HPC projects requiring ultra-fast networking.
FAQs
1. Which are the best cloud GPU providers for AI?
Google Cloud, AWS, Paperspace and CoreWeave are some of the best cloud GPU providers for AI.
2. Which cloud provider is cheapest for AI GPUs?
Vast.ai offers the lowest pricing, starting at $0.20/hr, making it ideal for researchers and startups.
3. Which provider is best for large-scale LLM training?
AWS, Google Cloud, and CoreWeave are best, offering A100 and H100 clusters with high networking performance for distributed training.
4. Do all providers support pay-as-you-go pricing?
Yes, but AWS, Azure and Google also provide reserved discounts, while Vast.ai and Lambda Labs focus on hourly rentals.
5. Which platform is easiest for beginners?
Paperspace is beginner-friendly with Gradient notebooks, pre-configured ML tools, and affordable GPU pricing starting at just $0.40/hr.