When to Use On-Demand vs. Reserved GPU Rentals

By Dylan Condensa

GPUs are the backbone of modern AI projects, powering everything from model training to large-scale data processing. If you've been around this space for a while, you'll know that there are two types of cloud GPU instances: on-demand GPUs and reserved GPUs. Each has its own set of advantages and disadvantages depending on where you're at in your development cycle. Choosing the right one for you is a decision that directly impacts your costs, performance, and overall efficiency.

This guide breaks down the scenarios where on-demand or reserved GPU instances make the most sense for AI workloads.

Understanding On-Demand and Reserved GPU Instances

Before diving into use cases, itā€™s essential to understand the basics of on-demand and reserved GPU instances.

  1. On-Demand GPU Instances: On-demand GPUs are rented on a pay-as-you-go basis, providing the flexibility to spin up resources instantly without prior commitment. They are ideal for unpredictable workloads or short-term tasks where resource needs fluctuate.
  2. Reserved GPU Instances: Reserved GPUs are pre-booked for a specific durationā€”ranging from weeks to months. These are suited for planned, long-term projects that require consistent computational power.

When to Use On-Demand GPU Instances

On-demand GPU instances shine in scenarios requiring flexibility and agility. Below are common situations where they are the optimal choice:

1. Experimentation and Prototyping

During the early stages of AI model development, experimentation is key. Data scientists and researchers may frequently adjust hyperparameters, test new algorithms, or experiment with datasets. On-demand GPUs allow quick iteration without long-term financial commitment.

  • Example Use Case: A startup testing a new recommendation engine on different types of neural networks.

2. Handling Spikes in Demand

Businesses with fluctuating computational needs, such as those running seasonal campaigns or processing unpredictable datasets, benefit greatly from on-demand GPUs. They offer scalability to handle sudden workloads without overprovisioning.

  • Example Use Case: E-commerce companies running AI models for holiday-specific demand forecasting.

3. Short-Term Projects

For projects with tight deadlines but short durations, committing to long-term GPU reservations can lead to overpayment. On-demand GPUs ensure developers pay only for the time they use.

  • Example Use Case: A university team using AI to analyze a single data-driven research project over a three-week period.

4. Avoiding Overcommitment

Organizations still gauging the scope of their AI projects may find reserved instances risky. On-demand GPUs allow incremental investment as project requirements evolve.

  • Example Use Case: A consultancy working on a clientā€™s proof-of-concept AI model.

When to Use Reserved GPU Instances

Reserved GPUs are a cost-efficient solution for predictable, sustained workloads. Hereā€™s when they are most advantageous:

1. Large-Scale Model Training

Training large AI models, such as GPT-style transformers or computer vision architectures, often requires days or even weeks of consistent GPU usage. Reserved GPUs provide significant cost savings while guaranteeing resource availability.

  • Example Use Case: A company training a generative AI model on terabytes of data.

2. Production Deployments

Once an AI model is ready for production, it typically runs consistently to handle inference workloads. Reserved GPUs ensure stable performance for serving real-time predictions or batch processing.

  • Example Use Case: A financial institution running fraud detection algorithms 24/7 on production data streams.

3. Predictable Long-Term Workloads

Organizations with steady computational needs can capitalize on discounts provided by reserved GPU instances. These workloads often include model retraining cycles or large-scale data preprocessing.

  • Example Use Case: A social media platform retraining recommendation algorithms every month with new user interaction data.

4. Advanced Reservations for Deadlines

Certain projects, like competitive Kaggle challenges or enterprise deadlines, demand guaranteed GPU availability. Reserved GPUs eliminate the risk of resource scarcity, which is common during peak usage periods across cloud providers.

  • Example Use Case: A biotech company simulating protein folding for an upcoming drug discovery deadline.

Comparing GPU Providers: AWS, Azure, GCP, Lambda, Vultr, and Crusoe

When choosing between on-demand and reserved GPUs, itā€™s equally important to evaluate the cloud providers offering these services. Each provider has unique pricing models, features, and strengths, which can impact your decision.

1. AWS (Amazon Web Services)

  • On-Demand GPU Instances: AWS offers a range of GPU instances (e.g., P4, G4dn) through its Elastic Compute Cloud (EC2) service. On-demand instances are priced higher but provide instant availability for short-term tasks.
  • Reserved GPU Instances: AWS offers Reserved Instances (RIs) for its GPU-equipped EC2 instances, such as the G4, G5, P3, and P4 series. Customers can achieve significant cost savings by committing to a one- or three-year term.
  • Strengths:
    • Vast global infrastructure.
    • Extensive ecosystem of AI and ML tools (e.g., SageMaker).
    • Flexible pricing options.

2. Azure (Microsoft Azure)

  • On-Demand GPU Instances: Azure provides GPU-enabled VMs under its NV and NC series. On-demand pricing is suitable for businesses exploring AI model development.
  • Reserved GPU Instances: Reserved Virtual Machine Instances (RIs) are available for GPU-enabled VMs. Customers committing to one- or three-year terms can reduce costs significantly compared to on-demand pricing.
  • Strengths:
    • Integration with Microsoft tools like Office 365 and Power BI.
    • Robust hybrid cloud solutions.
    • Focus on compliance for enterprises.

3. GCP (Google Cloud Platform)

  • On-Demand GPU Instances: Google Cloud offers GPUs like NVIDIA T4, V100, and A100 for on-demand usage, with competitive pricing and fast deployment.
  • Reserved GPU Instances: GCP offers Committed Use Contracts for GPU instances. These provide discounted rates in exchange for one- or three-year commitments, reducing costs significantly.
  • Strengths:
    • Strong AI/ML services, including TensorFlow and Vertex AI.
    • Customizable instance configurations.
    • Competitive spot instance pricing.

4. Lambda

  • On-Demand GPU Instances: Lambda Labs specializes in high-performance GPU rentals, particularly for AI and ML workloads. Their pricing is straightforward, with no extra fees for data egress.
  • Reserved GPU Instances: Lambda offers reserved instances featuring GPUs like NVIDIA H100 and A100. Committing to longer terms (up to three years) provides significant discounts.
  • Strengths:
    • Hardware optimized for deep learning.
    • Transparent pricing.
    • Excellent customer support tailored for AI developers.

5. Vultr

  • On-Demand GPU Instances: Vultr provides affordable GPU instances with NVIDIA A100 cards for on-demand usage. Their pricing is competitive for small to medium workloads.
  • Reserved GPU Instances: Vultr offers subscription-based pricing for monthly reserved GPU instances, providing cost savings compared to hourly rates.
  • Strengths:
    • Straightforward pricing with hourly billing.
    • Rapid instance deployment.
    • Great for small and medium sized workloads.

6. Crusoe

  • On-Demand GPU Instances: Crusoe uses excess energy from data centers and natural gas flaring to power GPUs, offering a sustainable approach to cloud computing.
  • Reserved GPU Instances: Offers reserved instances with substantial discounts for long-term commitments, aligning with the companyā€™s sustainability goals.
  • Strengths:
    • Environmentally friendly operations.
    • Competitive pricing.
    • Focus on reducing carbon footprint.

Introducing Shadeform: Simplifying GPU Rentals and Reservations

Shadeform is a powerful platform designed to simplify the process of renting and reserving GPUs for AI and machine learning workloads. Whether you need on-demand GPUs for experimentation or reserved GPUs for large-scale training, Shadeform brings the offerings of leading cloud providersā€” AWS, Azure, Lambda, Vultr, Crusoe, and 10+ moreā€”into a single, easy-to-use interface.

With Shadeform, you can compare prices, availability, and features across all these providers in real time. It eliminates the hassle of jumping between platforms to find the best fit for your needs, saving you time and ensuring cost efficiency.

How Shadeform Works

  1. Unified Comparison: Instantly see GPU pricing and availability from top providers in one place, making it easy to identify the most cost-effective or powerful option for your project.
  2. Simple Launching: Deploy GPU instances directly through Shadeform without needing to navigate individual provider interfaces.
  3. Flexible Reservations: Reserve GPUs from your preferred provider for long-term workloads, locking in competitive rates while ensuring availability.

Why Choose Shadeform?

  • Streamlined Workflow: Manage both on-demand and reserved GPU instances without switching between platforms.
  • Cost Savings: Compare real-time pricing to ensure youā€™re always getting the best deal.
  • Flexibility: Quickly adapt to changing project requirements by transitioning between providers or instance types.
  • Provider Choice: Access GPUs from leading cloud providers with diverse offerings to suit any AI workload.
  • Ease of Use: A user-friendly interface and seamless API integration simplify even the most complex workflows.

Whether you're a startup experimenting with new models or an enterprise scaling AI deployments, Shadeform ensures you have the tools to make smarter, faster, and more cost-effective decisions about GPU resources.

Start exploring Shadeform today and experience the easiest way to compare, launch, and reserve GPUs across multiple providers for your AI projects.

What Did We Learn?

Deciding between on-demand and reserved GPU instances boils down to your projectā€™s specific needs. On-demand instances provide the flexibility required for experimentation and short-term tasks, while reserved instances offer cost savings and stability for long-term, predictable workloads.

Shadeform brings hundreds of offerings for both on-demand and reserved instances under one roof, empowering AI developers to select the best option for them without having to navigate multiple interfaces. With real-time price comparisons, scalability, and a unified interface, Shadeform ensures that your AI workflows remain optimized, cost-effective, and hassle-free.

Ready to simplify your GPU resource management? Sign up for free and discover how Shadeform can transform your AI development process.


Relevant Blogs

The Ultimate Guide to Evaluating and Renting Cloud GPUs

How to Rent GPU Instances and Clusters on Shadeform

The Top 5 Clouds for Renting GPUs

Terms of UseContactDocs
Ā© Shadeform, Inc