The Ultimate Guide to AI/ML Hosting and Choosing the Best Platform

The Ultimate Guide to AI/ML Hosting: GPUs, VRAM, and Choosing the Best Platform (AWS vs. Google vs. HuggingFace)

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Hosting is one of the most important decisions when building AI/ML applications. Unlike normal websites, AI workloads involve running large models, serving real-time predictions, processing huge datasets, or even training LLMs. Most production-level AI apps—like those running on large language models (LLMs) or agentic AI—demand security, high performance, and scalability. This requires heavy computation, high-performance GPUs, fast storage, and enterprise-grade scalability and security.

As a beginner, while exploring hosting options for AI applications, there are a few key parameters that stand out as essential:

High-Performance Computing

Most AI/ML applications rely heavily on GPUs (Graphics Processing Units) because they are designed for parallel processing.

CPU vs GPU

Many AI apps rely on parallel processing for complex calculations, with high-performance models being trained and inferred simultaneously. Real-time tasks like detecting objects in autonomous vehicles, image processing, or natural language processing are much more efficient on GPUs than on CPUs. The GPU could do 10x+ operations faster than a CPU. CPUs are optimized for serial tasks, which work well for general-purpose computing, but when it comes to AI, GPUs are a better fit. They handle the volume and parallel nature of AI tasks—whether training large models or running inferences—much more efficiently.

High V-RAM memory

VRAM, Video Random access memory is a memory storage of GPU that stores tensors, activations, and model weights. Lack of sufficient VRAM will lead to performance issues. AI applications with production workload should ensure they have enough VRAM to run the application. VRAM capacity and GPU compute performance are both critical. VRAM determines the maximum model size and batch size you can run, while GPU performance determines the speed of computation. You need enough of both for optimal performance.

For example:

• Small models → 8GB–16GB VRAM works
• Medium models → 24GB–48GB
• Heavy models (Llama, Vision models, diffusion) → 80GB–120GB+

Fast storage

Unlike traditional web applications, AI/ML applications may require faster storage options. High throughput and low latency are essential for backing GPUs during real time processing. This can be provided by NVMe storage.

NVMe is Non volatile Memory Express, an interface protocol for flash based solid state drives connected to the system’s bus. So it felicitated high speed of data transfer and massive parallelism allowing execution of thousands of parallel command queues.

Preconfigured Machine learning frameworks

Most of the hosting service providers have preconfigured ML frameworks which have optimised the process of deployment and serving ML models.

Most of the standard hosting service providers use pre-configured frameworks like

• TensorFlow
• Scikit-learn
• PyTorch
• JAX

This helps in faster runtime of ML tasks and optimised inference of deep learning models.

Service providers for AI /ML applications

The key providers include:

• HuggingFace
• Azure Machine Learning
• Lambda Labs Cloud
• RunPod
• AWS EC2 P5 Instances
• Google Cloud Vertex AI
• Paperspace

HuggingFace

HuggingFace can be used to host ML applications which includes, chatbots, translators, object detection and multimodal modals. It offers multiple hosting options, such as:

• Inference Endpoints
• Managed Deployments
• Spaces (for demos)

HuggingFace helps in scalability in production and ensures security features like SSO, malware scanning..,etc. HuggingFace hosting is GDPR compliant and customers can decide where to store the data - models, datasets.

It is suitable for deploying ML API faster and inbuilt features allow single click deployment for transformers. It has pricing layers based on requirements.

Azure Machine learning

Azure Machine learning is the best choice if you are already in the Microsoft ecosystem. It handles AI workload management pretty well and promises enterprise grade security and can be used across regulated industries.

Azure Machine learning hosting supports Role based access controls, network isolation and audit trails to meet GDPR, HIPAA, and ISO/IEC 27001.

AWS EC2 P5 Instances

AWS EC2 is one of the broadest platforms available for matching the needs of the workload of the application. P5 instances of EC2 run on NVIDIA’s latest GPUs, NVIDIA H100. It is suitable for large scale LLMs applications.

AWS supports various standards like HIPAA, GDPR, NIST 800-17. The autoscaling feature of AWS also meets standards of PCI DSS Level 1 for data security compliance ensuring secure payment processing / client credit card data.

Google Cloud Vertex AI

Google Cloud Vertex AI is an enterprise security hosting service which can handle end to end ML platforms. Data can be trained, model registry and deployment can be done in the unified platform which is scalable in production. Various tiers of pricing available depending on the number of successful requests processed and type of data and other services like grounding of data with google search / Google Maps and Gemini latest models.

Vertex AI meets Data residency compliance, HIPAA compliance and EU AI Act. Vertex also has a feature called Model Armor, which protects the models from prompt injection.

Lambda Labs Cloud

Lambda Labs Cloud deploys supercomputers for training and inference. AI models can be deployed in GPU instances of NVIDIA GB200, HGX B200, H100 best for handling heavy workloads focussing on AI compute. Lambda Labs is a HIPAA Compliant GPU cloud and the company is SOC Type 2 compliant.

DigitalOcean

DigitalOcean is one of the best choices for hosting ML applications as it is powered with high end GPU like, NVIDIA A100 and cloud, databases options. Ideal for side projects, solopreneurs projects. Many students and learners prefer it as it extends support to Jupyter Notebook IDE.

Digital Ocean is compliant to SOC 1, SOC 2, PCI-DSS, ISO/IEC 27001:2013 Certification and EU-U.S. and Swiss-U.S. Privacy Shield Certification.

Summary: Best Provider by Audience / Usecase

Audience / Usecase	Best suited for
Students and learners	DigitalOcean
Budget friendly	LambaLabs
Enterprise ML / LLM training	AWS / Google
Deploying API and AI products	HuggingFace
High end research	Azure / Google Cloud.

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Factors to consider when Choosing AI/ML Hosting

I see that there is an opinion that LambaLabs is expensive, but the fact is it depends on the requirements of the projects.

• The dataset of the project needs to be estimated as small datasets need not require overpowered GPUs.
• When there is a large dataset involved, multiple GPUs are recommended.
• Need to check the bandwidth charges incurred.
• Estimate the storage needs for checkpoints depending on the type of the datasets handled in the project. These could be the hidden costs involved.

Hidden Cost: Checkpoint Storage

Large models generate massive checkpoints:

• Llama checkpoints → 50GB–120GB
• Diffusion training → 100GB–500GB+

Portability and Scalability

• Is there any vendor lock-in period involved. Portability, export options, proprietary tool restrictions are some of the key parameters that need to be analysed.
• Should assess the ability to autoscale as the project scope might out grow over time. Maximum memory, GPUs, storage, endpoints need to be evaluated.

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