Spot vs On-Demand VMs: What’s the Difference?

Your GPU workload is ready. But are you about to overspend for performance you don’t need or risk everything to save a few bucks? If you’ve found yourself stuck choosing between spot and on-demand GPU VMs, stay here. Understanding the difference between these two can help you stretch your budget without compromising on performance or reliability.

No matter if you’re training large AI models, running batch jobs or scaling up for a product launch, this blog will help you decide which VM type is right for your use case.

What are On-Demand VMs?

On-demand VMs give you instant access to GPU resources whenever you need them, for as long as you need them. There’s no upfront commitment, no reservations and no waiting. You spin up the VM in the cloud, use it and pay only for the time it's running.

What are Spot VMs?

Spot VMs offer the same powerful GPUs at a lower price by using spare capacity. But they can be taken back (terminated) by the provider at any moment, without warning. That’s because these VMs only run as long as there’s spare capacity available.

Differences: Spot vs On-Demand VMs

Here are the major differences between Spot vs on-demand VMs:

When to Use On-Demand VMs

Even though the price is higher, the peace of mind and reliability often outweigh the cost of on-demand VMs. You should choose on-demand GPU VMs when:

• Your workload is mission-critical: Apps or services that need consistent uptime should always run on reliable, persistent cloud GPU VMs.
• You need guaranteed resources: If your project timeline is tight or customers are involved, interruptions are not an option.
• Your data needs to be preserved: On-demand VMs support data persistence and snapshots, ideal for workloads that must retain state.
• You’re running real-time inference: On-demand VMs ensure uninterrupted access to GPUs for latency-sensitive operations.

When to Use Spot VMs

Spot GPU VMs are a great way to save significantly on compute costs but only when your workload can afford the tradeoffs. Here’s when you should consider them:

• Non-critical or interruptible: Great for tasks like automated testing, background services or temporary development environments.
• Batch processing: Jobs that can be rerun without consequence such as image or video rendering or simulations.
• AI Experimentation: Spot VMs can accelerate model training and experimentation at a fraction of the cost. Just make sure to experiment with AI workflows where you can manually save checkpoints and resume later if interrupted.
• Scaling compute-heavy tasks: Use spot instances in parallel with on-demand VMs to scale your GPU-intensive jobs without blowing your budget.

Limitations of On-Demand VMs

While on-demand VMs are dependable, there may be some minor limitations such as:

• GPU Availability: During periods of high demand, certain GPU VM flavours may be temporarily out of stock. You can reserve the GPUs as it ensures priority access even during peak demand. 
• Higher cost: You'll pay more than you would for Spot VMs but with on-demand access, you get guaranteed availability while the VM is running, persistent data storage and full control, including features like hibernation, snapshots and boot-from-volume. 

Limitations of Spot VMs

Before you opt for the lower price, it’s important to understand what you’re giving up. At Hyperstack, spot VMs come with the following limitations:

• No termination notice: Your VM can be shut down at any time if the capacity is reclaimed. Be ready to face that “no warning shock” and prepare already.
• No hibernation or snapshot support: Spot VMs are ephemeral by nature. You can’t pause and resume them later.
• No boot-from-volume: You must start from a base image every time. Custom configurations need to be reapplied manually.
• No data persistence: All data stored locally on a spot VM will be permanently lost if the instance is reclaimed. You are responsible for saving important data to persistent external storage or implementing checkpointing to prevent loss.
• Standard public IP pricing: Unlike some on-demand setups, spot VMs don’t come with discounted IP charges.
• Limited availability: Not all GPU VM flavours are eligible for spot pricing. You’ll see supported options in the UI and our weekly product updates here.

GPU Pricing Comparison: On-Demand vs Spot VMs

You can get the same GPU performance at a 20% lower cost on Hyperstack:

Which One Should You Choose?

The answer depends on your workload needs, so for:

• Critical or production-ready: Go with On-Demand GPU VMs.
• Experimental, repeatable or batch jobs: Spot GPU VMs could save you serious money.

Many teams even use a hybrid approach, running their stable infrastructure on on-demand VMs while offloading compute-intensive experiments to spot VMs. This gives you the flexibility of cost control without sacrificing reliability where it counts.

Final Thoughts

Your choice between Spot and On-Demand VMs is not just about pricing. It’s about workload type, tolerance for interruptions and long-term efficiency. By understanding the pros, cons and ideal use cases of each, you can choose an option that’s both cost-effective and performance-optimised.

Explore your spot and on-demand GPU VM options with Hyperstack today and run smarter, not just faster.

FAQs

What are Spot VMs?

Spot VMs use spare GPU capacity at lower prices but can be terminated anytime without notice, making them ideal for flexible tasks.

What are On-Demand VMs?

 On-Demand VMs provide guaranteed GPU access with full control, persistent storage, and support for features like hibernation and snapshots.

What’s the difference between Spot and On-Demand VMs?

Spot VMs are cheaper but can be interrupted any time. On-Demand VMs cost more but ensure consistent uptime and data persistence.

When should I use Spot VMs?

You can use Spot VMs for non-critical, interruptible workloads like AI training, batch processing jobs or scaling experiments affordably.

When should I use On-Demand VMs?

Choose On-Demand VMs for production workloads, real-time inference, or anything that requires reliability, uptime and long-term data storage.

What happens to data on Spot VMs?

Data on Spot VMs is not persistent and will be lost upon termination. Always save to external storage or use checkpointing.

Can Spot VMs be paused or hibernated?

No, Spot VMs are ephemeral. They don’t support hibernation, snapshots or boot-from-volume.