Vast.ai vs AWS EC2 for GPU Cloud Services: A Detailed Analysis

Cloud GPUs are everywhere, powering everything from deep learning to gaming to complex simulations. While Amazon Web Services (AWS) has been a longtime leader in cloud computing with its EC2 instances, a newer player, Vast.ai, has been making waves by offering GPU rental at competitive prices.

With Vast.ai, users access a decentralized GPU marketplace designed to give them the best bang for their buck. AWS, on the other hand, provides a range of configurable, scalable instances backed by Amazon’s robust infrastructure. Both have distinct advantages, from Vast.ai’s real-time bidding system and customizable security levels to AWS’s deep integration with other Amazon services and trusted global presence.

This guide will walk you through the strengths, limitations, and unique features of both Vast.ai and AWS, so you can decide which best fits your GPU cloud computing needs.

Affiliate Disclosure

We are committed to being transparent with our audience. When you purchase via our affiliate links, we may receive a commission at no extra cost to you. These commissions support our ability to deliver independent and high-quality content. We only endorse products and services that we have personally used or carefully researched, ensuring they provide real value to our readers.

Choose from a wide range of NVIDIA and AMD GPUs at CUDO Compute, ensuring cost-effective performance for every workload. Sign up now!

For a deeper understanding of CUDO Compute, take a look at this informative video:

GPU Model Availability and Specifications

AWS GPU Instances

AWS offers several GPU instance families under EC2, including G3, G4, G5, G6, P2, P3, P4, and P5 instances, each featuring different GPU models:

G3 Instances:

• Equipped with NVIDIA Tesla M60 GPUs with 8 GiB of GPU memory, suitable for graphics-intensive tasks like 3D rendering and video encoding.
• Available with up to 4 GPUs (e.g., g3.16xlarge) and vCPU options up to 64, with hourly Linux prices starting at $0.75 for single GPU setups.

G4 Instances:

• G4dn and G4ad instances offer different GPU options (NVIDIA T4 and AMD Radeon Pro V520 GPUs respectively) ideal for machine learning inference and small-scale training.
• G4ad delivers up to 45% better price performance for graphics applications, with instances priced from $0.379/hr.

G5 Instances:

• Feature NVIDIA A10G GPUs with up to 24 GiB memory, useful for graphics-intensive applications and machine learning inference.
• Supports up to 8 GPUs and 192 vCPUs on multi-GPU instances like g5.48xlarge, starting at $1.006/hr.

G6 Instances:

• Incorporate NVIDIA L4 Tensor Core GPUs with 24 GB per GPU, optimized for deep learning inference.
• Single GPU instances are available from $0.805/hr.

P-Series Instances (P2, P3, P4, P5):

• High-performance instances with NVIDIA K80, V100, A100, and H100 GPUs, designed for heavy ML training and HPC applications.
• For instance, the P4d.24xlarge with 8 NVIDIA A100 GPUs and 320 GB GPU memory costs $32.77/hr, ideal for high throughput ML model training.

Vast.ai GPU Options

Vast.ai provides a wide selection of GPUs from RTX 3070 to high-end options like NVIDIA A100 SXM4 and H100 SXM, available on-demand at flexible prices:

RTX Series:

• Ranges from RTX 3070 ($0.05/hr) to RTX 4090 ($0.15-$0.40/hr), catering to various needs from lightweight tasks to high-end graphics rendering.

A-Series GPUs:

• Models like the A100 SXM4 ($0.73-$1.61/hr) and A100 PCIE ($0.14/hr) are suitable for heavy AI workloads and ML training.

H100 SXM:

• This GPU model, priced between $2.53 and $3.34/hr, is ideal for demanding deep learning and LLM applications, making it a competitor to AWS’s P4 and P5 instances.

Additional Options:

• Vast.ai also offers GPUs like L40S ($0.67/hr) and RTX A4000 ($0.05-$0.12/hr), providing more flexibility for users with varying GPU requirements.

Pricing Structure and Cost-Effectiveness

AWS has standardized pricing with on-demand, reserved, and spot instances. Prices vary by instance type and reserved commitment with up to 70% savings on long-term reservations. On-demand instances have predictable pricing and reserved options help reduce costs for long-term users.

Vast.ai operates on a marketplace model with hourly pricing that varies by provider and GPU type. Pricing is by GPU usage so it’s more affordable for users that need short-term or sporadic GPU access. For example, the A100 SXM4 is $0.73/hr which is way cheaper than AWS’s similar instances.

Save significantly with competitive pricing on powerful models like H100, A100, and V100, tailored for budget-friendly high-performance. Sign up now!

Performance and Suitability for Specific Workloads

Graphics-Intensive Applications

Graphics-intensive applications like gaming, 3D rendering, and video encoding require powerful GPUs to handle complex visual calculations smoothly and efficiently. AWS and Vast.ai are both competitive in offering solutions for these compute-intensive workloads but approach this from different angles to cater to different budget and performance requirements.

AWS G3 and G4 Instances

AWS offers a range of GPU instances for high-performance graphics needs, the G3 and G4 series. The AWS G3 instances come with NVIDIA Tesla M60 GPUs to deliver performance for applications that rely on GPU acceleration.

With Tesla M60 GPUs these instances are great for stable frame rates, high-quality rendering, and efficient handling of tasks like 3D modeling, video processing, and streaming applications. The G3 instances are particularly useful for gaming developers and digital content creators who need stable and responsive graphics processing.

In addition to G3, AWS also offers G4ad instances powered by AMD Radeon GPUs, a more cost-effective alternative to NVIDIA-based offerings. G4ad instances are good for applications that require moderate graphical power but need to stay within budget. Their pricing is generally more affordable so good for streaming lower-intensity games or rendering tasks that don’t require top-tier GPUs.

By using AMD Radeon Pro V520 GPUs these instances provide performance for real-time rendering, virtual workstations, and high-definition video processing while being lower cost than NVIDIA-based solutions. So G4ad instances are a good option for businesses that need to balance performance and budget for their graphics-heavy applications.

Vast.ai: Lower-Cost RTX Series Options

Vast.ai takes a different approach by offering high-performance GPUs at a lower cost. Vast.ai users can access GPUs from the NVIDIA RTX series including the RTX 3090 and RTX 4090 models.

These GPUs are consumer-grade but deliver a lot of power for many graphics workloads. They’re good for game streaming, VR applications, and moderate-level rendering tasks that can benefit from fast real-time processing without the overhead of enterprise-level costs.

Vast.ai’s approach of renting GPU power at competitive prices is an affordable entry point for users who need strong GPU support without committing to a long-term solution. The result is a budget-friendly option for short-term projects, burst workloads, and scenarios that need high graphics performance but don’t need sustained 24/7 GPU usage.

For users or small businesses looking for budget-friendly options for high-quality rendering and streaming Vast.ai’s RTX options are a good alternative. With a choice of GPU and pricing users can get the best price for performance.

CUDO Compute offers easy, on-demand access to high-performance GPUs, perfect for AI, ML, and HPC workloads of all scales. Sign up now!

Machine Learning and AI

HPC, AI, and deep learning tasks require a lot of compute power, speed, and scale. Both AWS and Vast.ai have solutions for data scientists, ML engineers, and AI researchers but they are very different in scale, cost, and flexibility.

AWS P-Series Instances

AWS’s P-Series instances (P4d and P5) are designed for large-scale machine learning and HPC workloads. They come with powerful NVIDIA A100 and H100 GPUs optimized for high throughput training and deep learning models.

P4d instances have up to 8 A100 GPUs with NVIDIA InfiniBand network infrastructure for high-speed inter-GPU communication and data transfer. P5 instances take this further with H100 GPUs to push the performance boundaries for large model training including compute-intensive tasks like NLP, image recognition, and reinforcement learning.

AWS also has a feature called UltraClusters which allows users to deploy massive clusters of GPUs that can scale up to thousands or even tens of thousands of GPUs to support highly complex ML models.

This level of scale is perfect for enterprises, scientific research, and organizations running large AI model experiments. UltraClusters on AWS allows you to deploy massive computational resources quickly so you can train AI models at scale with big time and cost savings.

Vast.ai: A100 and H100 on Demand

If you need the power of high-performance GPUs like A100 and H100 with decent disk space on your Operating Systems but want a more flexible and cost-effective option, Vast.ai has those too, with a focus on affordability and short-term rental.

A100 SXM4 and H100 SXM on Vast.ai have similar compute power as AWS P4 and P5 series but you can rent them for lower cost and shorter duration. This is perfect for AI researchers, startups, and independent ML practitioners who need high-performance GPUs but don’t need the constant availability or scale that AWS UltraClusters provide.

For example, a machine learning researcher who only needs GPU power for model testing or short-duration experiments can use Vast.ai to reduce costs while maintaining high computing power. With on-demand access to A100 and H100 GPUs, you get access to the latest technology without long-term financial commitments. This flexibility can lower the barrier for smaller organizations or teams working on AI projects to get state-of-the-art ML infrastructure.

Network Bandwidth and Storage Capabilities

Network bandwidth and storage are key in high-performance computing (HPC) and distributed machine learning where fast data transfer and storage matters. AWS and Vast.ai have different networking and storage solutions for different workloads and users.

AWS

AWS has high network bandwidth on its larger instances, great for data-intensive distributed computing workloads. P3 instances have up to 100 Gbps, perfect for large-scale applications like genomics, financial modeling, and deep learning. For even higher network requirements, P4 instances have 400 Gbps networking and 1.6 TB NVMe SSD storage, great for projects with high data transfer needs like distributed machine learning and real-time data analytics.

AWS’s Elastic Fabric Adapter (EFA) technology helps with data sharing between GPUs, especially in P5 instances used for large AI model training. EFA provides low latency, and high bandwidth connections between GPUs, making it great for enterprises running complex AI and HPC workloads where fast communication between GPUs is key to scaling across clusters.

Vast.ai

Vast.ai also has high-performance networking options for distributed computing. It has networked GPUs with options like InfiniBand GPU Direct RDMA which supports up to 1.6 Tbps throughput on A100 NVLINK clusters.

This allows users to do distributed training with high-speed inter-GPU communication, matching AWS’s network performance at a lower cost. Vast.ai is perfect for users who need high bandwidth and performance in distributed setup but want a more cost-effective, short term solution vs AWS’s long-term, large-scale infrastructure.

Scale your infrastructure seamlessly with CUDO’s flexible GPU options, from short-term rentals to enterprise-level reservations. Sign up now!

Customization and Scalability Options

AWS and Vast.ai provide robust customization and scalability options, catering to a wide range of user needs, from startups to enterprise-grade HPC and ML workloads.

AWS

AWS has many instance configurations across multiple GPU families so you can tailor resources to your specific workload. G4, G5, P3, and P4 instances allow you to control GPU types, CPU, memory, and storage. This flexibility is for simple applications and complex data-intensive workloads.

AWS’s UltraCluster option takes scalability to the next level, allowing you to scale across thousands of GPUs for massive distributed workloads in genomics, artificial intelligence research, and scientific simulations. This setup supports complex high-volume workflows with low latency networking and integration with AWS’s Elastic Fabric Adapter (EFA) for best performance.

Vast.ai

Vast.ai has a highly flexible pay-as-you-go model, where you can mix and match GPUs according to your workload and budget. You can select specific GPU types like RTX or A100 and custom CPU and memory configurations, a cost-effective alternative to the fixed instance options many cloud providers offer.

Vast.ai’s Docker ecosystem allows you to deploy containerized workloads, so you can manage and scale applications more easily. This is especially useful for users who need flexibility, one-time projects, or fluctuating workloads that need short-term GPU rentals without long-term commitments.

Ease of Access and Usability

AWS and Vast.ai provide different approaches to usability, each with its strengths based on the user’s needs and experience level.

AWS

AWS has a full console that integrates deeply with its massive cloud, giving you access to deployment, monitoring, and security tools. If you’re already familiar with AWS, this consistency across services is a big win, so you can manage your GPU instances alongside other AWS resources in one place.

The console is highly customizable, from custom scaling policies to advanced network configurations, perfect for users managing complex, multi-faceted workloads.

AWS also has documentation, tutorials, and customer support to help new users navigate its many features and deploy resources with confidence. Plus AWS has managed services and automation tools like Elastic Load Balancing (ELB) and Auto Scaling which can simplify things at scale but add complexity to some tasks.

Vast.ai

Vast.ai is all about simplicity and speed, with a minimalist interface even for those with no experience in cloud computing. The marketplace allows users to browse and rent GPU resources by type and price, making it perfect for short-term projects or users who don’t want to commit to long-term instances.

Vast.ai’s Docker ecosystem has preconfigured software environments that make setting up ML and GPU workloads easy even for users with limited technical knowledge. But fewer automation and resource management features than AWS, IBM Cloud, and Google Cloud Platform.

CUDO Compute simplifies AI deployment with a user-friendly interface, detailed tutorials, and Docker container support for seamless integration. Sign up now!

Security and Compliance

In cloud computing, security, and compliance are essential, especially for industries handling sensitive data, such as healthcare, finance, and government sectors. AWS and Vast.ai offer security features and varying levels of compliance to meet these demands, albeit at different scales and depths.

AWS

AWS has one of the most secure infrastructures in the cloud. With over 10 years of experience, they have implemented many security protocols including end-to-end encryption, multi-factor authentication (MFA), identity and access management (IAM), and continuous monitoring for suspicious activity. AWS also has a long list of compliance certifications including ISO 27001, SOC 1/2/3, HIPAA, PCI-DSS, GDPR FedRAMP, etc.

With this many certifications, AWS is the go-to choice for organizations with strict regulatory requirements and legal obligations. Also, AWS encryption is built into many services by default so data is secure in transit and at rest and customer-controlled keys using the AWS Key Management Service (KMS). AWS security tools like GuardDuty and Inspector help detect vulnerabilities and enforce organizational policies.

Vast.ai

Vast.ai may not have as many security certifications as AWS but has prioritized user security, especially with bare-metal GPU rentals through ISO-certified Tier 2-4 data centers. These data centers have high levels of physical security and server isolation so users can have dedicated hardware without a shared environment which can reduce exposure risks.

Vast.ai doesn’t cover as much compliance as AWS but is a good choice for users who need reliable security with unlimited SSH keys in a controlled environment without specific regulatory requirements like HIPAA or GDPR.

For projects that require flexible yet secure infrastructure without extensive compliance certifications, Vast.ai is a more cost-effective alternative to public clouds and can be deployed within a few minutes. However, for organizations that need advanced compliance, AWS’s many certifications may be more comforting for data-sensitive applications.

Vast.ai vs AWS EC2: Which one should you go for?

AWS and Vast.ai have their own strengths for cloud GPU services, for different users and use cases. Vast.ai is great for affordability, flexibility, and short term rentals, for users who want cost-effective access to powerful GPUs without long-term commitments.

AWS is great for enterprise-grade scalability, compliance, and integration with the broader cloud, for complex workloads and organizations with high regulatory requirements. Ultimately it comes down to budget, workload complexity, and level of infrastructure integration.

Benefit from enterprise-grade GPU clusters at CUDO Compute, with high bandwidth and memory for intensive, data-driven tasks.