Spot vs On-Demand Instances: Which Saves You More?
When managing cloud costs, the choice between spot and on-demand instances is all about balancing cost savings with reliability. Here's the quick takeaway:
- Spot Instances: Offer up to 90% savings by using surplus capacity. Ideal for tasks that can handle interruptions, like batch processing or testing. However, they can be reclaimed with as little as 2 minutes’ notice, making them less reliable.
- On-Demand Instances: Provide guaranteed availability with fixed pricing. Perfect for critical workloads needing consistent uptime but are more expensive - up to 90% higher costs than spot instances.
Quick Comparison
| Feature | Spot Instances | On-Demand Instances |
|---|---|---|
| Cost | Up to 90% cheaper | Standard published rates |
| Reliability | Prone to interruptions | Guaranteed availability |
| Use Cases | Batch jobs, testing | Critical apps, production |
| Pricing Model | Dynamic, market-driven | Fixed, predictable |
| Notice for Reclamation | 30–120 seconds | Not applicable |
For UK businesses, the best approach often involves mixing both types. Use spot instances for flexible workloads and on-demand for critical tasks. This hybrid strategy helps reduce costs without risking performance. Read on to learn how to optimise your cloud expenses.
Section 6: On-Demand vs. Spot Instances
Pricing Structures and Cost Comparison
Let’s take a closer look at how pricing works for spot and on-demand instances, and how these differences impact monthly costs and budgeting strategies.
How Spot Instance Pricing Works
Spot instance pricing relies on a dynamic, market-driven system where costs shift based on real-time supply and demand for unused cloud resources. Unlike older auction-based systems, today’s spot pricing is determined by the provider’s immediate capacity availability rather than user bids [6]. Factors like regional capacity, instance popularity, seasonal demand, and hardware updates all influence pricing [6]. For workloads that demand heavy computing power, costs can spike during peak usage hours.
The main draw of spot instances is the potential for huge savings. Microsoft Azure highlights this by stating:
With Spot Virtual Machines, provision unused Azure compute capacity at deep discounts of up to 90 percent compared to pay as you go prices [4].
Similarly, Google Cloud offers discounts ranging from 60% to 91% off standard on-demand rates for most machine types and GPUs. These prices are updated roughly every 30 days [5]. However, the trade-off for these savings is pricing unpredictability. Spot prices can change without warning, and instances may be interrupted with little notice if the capacity is needed elsewhere. This volatility makes budgeting harder, but for workloads that can handle interruptions, the cost reduction can be significant.
How On-Demand Instance Pricing Works
On-demand instances follow a fixed pricing model, offering stable and predictable costs. You pay a set hourly or per-second rate (with a minimum charge of 60 seconds) for the exact time your instances are active [2][7]. There’s no bidding, no fluctuating rates, and no concerns about capacity availability. The charges are straightforward - based entirely on your usage, with rates that remain unchanged regardless of demand or time of day.
This consistency makes on-demand pricing ideal for businesses that prioritise guaranteed availability and predictable expenses. While it’s typically more expensive than spot pricing, the stability it provides is a key advantage for organisations that need reliable performance and straightforward budgeting.
Cost Comparison Summary
When comparing the two models, spot instances can deliver savings of 60–90%, which can result in significant monthly cost reductions, especially for workloads that run continuously. For example, CAST AI reported achieving a 90% cost reduction by implementing a Spot Instance policy [3].
However, spot prices can vary throughout the month. During periods of high demand, prices may climb closer to on-demand rates, while low-demand periods can offer even greater discounts. This variability means that careful monitoring is essential. To fully capitalise on the cost advantages, businesses need to design flexible, fault-tolerant applications that can handle interruptions while maintaining reliable service.
Pros and Cons
Weighing the benefits and drawbacks of spot and on-demand instances is key to optimising your cloud infrastructure. Each option brings unique strengths and limitations that can shape your operational efficiency and budget.
Spot Instances: Pros and Cons
Spot instances are a go-to option for cutting costs, but they come with a trade-off: reliability. The cost savings can be dramatic. For instance, an AWS m5.xlarge instance costs around £0.15 per hour on-demand, while the spot price can plummet to just £0.03 per hour. Over 100 hours, that’s a difference of £15.36 for on-demand versus £2.72 for spot pricing - a massive 82% saving [8].
But there’s a catch. Spot instances can be reclaimed by providers with as little as two minutes’ notice, and their availability depends on fluctuating supply and demand [9][10]. This makes them a poor fit for applications that demand uninterrupted uptime or are critical to business operations.
| Pros | Cons |
|---|---|
| Save up to 90% compared to on-demand pricing | Risk of interruptions with minimal notice (2 minutes) |
| Ideal for scaling variable workloads | Availability can be unpredictable due to market demand |
| Flexible, market-driven pricing | No uptime guarantees (no SLAs) |
| Pay only for actual usage | Unsuitable for critical or stateful applications |
Spot instances shine for workloads that can handle interruptions, but when reliability is non-negotiable, on-demand instances take the lead.
On-Demand Instances: Pros and Cons
On-demand instances are all about reliability and predictability, making them the clear choice for applications where downtime isn’t an option - even if it means paying a premium.
The standout advantage is guaranteed availability. Unlike spot instances, on-demand options are not at risk of being reclaimed by the provider. They’re also backed by service level agreements (SLAs), ensuring steady performance for workloads that can’t afford interruptions [10].
However, this reliability comes at a price. On-demand instances can cost up to 90% more than spot instances, which can significantly inflate operational expenses for large-scale projects [8][9][10][11]. For businesses with substantial compute needs, these higher costs can add up to thousands of pounds each month.
| Pros | Cons |
|---|---|
| Guaranteed availability with no interruptions | Higher costs - up to 90% more expensive than spot pricing |
| Fixed, predictable pricing for easier budgeting | Less cost-efficient for scaling during peak periods |
| Backed by service level agreements (SLAs) | Not ideal for workloads that aren’t mission-critical |
| Suitable for a wide range of applications | Increased costs for long-running tasks |
The choice between spot and on-demand instances often boils down to the nature of the workload. For example, a UK data analytics company running non-urgent overnight batch jobs might opt for spot instances, reaping up to 90% savings since interruptions won’t disrupt their operations. On the other hand, a UK online retailer would likely stick with on-demand instances to guarantee uninterrupted service during events like Black Friday, where downtime could result in major revenue losses [8][11]. This balancing act between cost efficiency and reliability is at the heart of smart cloud infrastructure planning.
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Best Use Cases for Each Instance Type
Selecting the right instance type boils down to understanding your workload needs and balancing costs against reliability. Spot and on-demand instances each serve distinct purposes, and knowing when to use which can significantly impact your cloud spending.
When to Use Spot Instances
Spot instances shine in scenarios where cost savings outweigh the need for guaranteed uptime. These are best suited for tasks that can handle interruptions without major setbacks.
Data analysis and batch processing: These workloads often run during off-peak hours and can resume from checkpoints, making them ideal candidates for spot instances. The cost savings can be substantial [12].
Development and testing environments: Non-time-sensitive tasks like CI/CD pipelines can benefit from spot instances. If an interruption occurs, tests can simply be rerun without much hassle [14].
Containerised workloads and microservices: These are well-suited for spot instances, especially when distributed across multiple availability zones. Their decentralised nature ensures that interruptions have minimal impact on the overall system [13].
High-performance computing (HPC): Tasks like scientific simulations, rendering, and complex computations that can checkpoint progress are excellent candidates. They can pause and resume without losing significant work, making the cost savings worthwhile [13].
According to KodeKloud:
Spot Instances are a cost-effective choice for fault-tolerant and flexible applications.[14]
For example, one nOps customer ramped up their spot instance usage from 12.5% to 50.5% over three months, saving £66,991 monthly while running production workloads [15].
The key is ensuring your applications are stateless, fault-tolerant, or flexible enough to handle the unpredictability of spot instances [13]. When interruptions are not an option, on-demand instances are the better choice.
When to Use On-Demand Instances
On-demand instances provide the stability and reliability required for business-critical applications. They are indispensable when uninterrupted performance is a must.
Customer-facing applications: Platforms like e-commerce sites, online banking systems, and customer portals depend on consistent uptime. Downtime could lead to lost revenue and damage to user trust, making the extra cost of on-demand instances justifiable.
Production environments: Core systems like databases, authentication services, and payment processing require guaranteed availability to meet strict SLA requirements [16].
Applications with unpredictable traffic: Sudden demand spikes, such as those during viral events or breaking news, require immediate capacity. On-demand instances ensure availability without the risk of resource shortages.
Short-term, time-sensitive workloads: Tasks like emergency computations or temporary projects that cannot tolerate interruptions are better suited for on-demand pricing [16].
Compliance-critical workloads: Industries like finance, healthcare, or regulatory reporting often mandate consistent performance and guaranteed uptime, which only on-demand instances can provide.
Another advantage of on-demand instances is their full lifecycle control. You can start, stop, hibernate, reboot, or terminate them as needed, paying per second with no long-term commitments. This combination of flexibility and reliability makes them indispensable for workloads where even brief interruptions could cause significant issues [16].
Ultimately, the choice comes down to your tolerance for interruptions versus your need for cost efficiency. For workloads that can withstand occasional disruptions, spot instances offer substantial savings. However, for applications where reliability is non-negotiable, on-demand instances are worth the investment.
Cost Reduction Methods and Best Practices
Cutting down on cloud expenses requires more than just opting for the cheapest solution. For UK businesses, achieving meaningful savings involves strategic planning, adopting proven methods, and selecting the right mix of instance types to suit their needs.
Using Spot Instances Effectively
Spot instances can be a game-changer for cloud cost savings, but using them wisely is key. Diversify workloads across different instance types, sizes, and availability zones to minimise the risk of interruptions and ensure access to lower-cost capacity. Think of it as not putting all your eggs in one basket - spreading the load reduces potential disruptions.
One smart approach is attribute-based instance selection. Here, you define your specific needs for CPU, memory, and storage, and let the system automatically choose and launch the most suitable instances. Additionally, spot placement scores can guide you to the best times and locations for launching instances, helping to avoid interruptions. Pair this with a price-capacity-optimised strategy, which prioritises maximising capacity at the lowest cost, and you’re on the right track.
Automation plays a vital role in managing spot instances smoothly. By deploying automation tools, you can adjust instances as prices fluctuate. These tools should also include autoscaling features, allowing a seamless fallback to on-demand instances during interruptions, ensuring your workloads keep running without a hitch.
Keeping an eye on pricing trends across cloud regions is another critical step. Provider APIs can help you monitor these trends and make informed choices. For instance, recent data shows significant differences in price changes: Azure spot VM prices jumped by 108% from 2022 to 2023, AWS saw a 21% increase, while Google Cloud Platform (GCP) prices actually dropped by nearly 26% [18]. Knowing these trends can help you choose the most cost-effective regions and timings for your workloads.
Running spot instances during off-peak hours or in regions with higher capacity can also lead to savings. If your workload requirements allow, consider using older-generation instances, which often have better availability [19].
Once you’ve optimised your spot instance strategy, combining it with on-demand instances can further enhance cost efficiency.
Combining Spot and On-Demand Instances
A hybrid approach that blends spot and on-demand instances offers the best of both worlds: cost savings from spot instances and the reliability of on-demand options. This strategy ensures flexibility without locking into long-term contracts while maintaining operational stability [1].
To make this work, divide workloads based on their criticality and tolerance for interruptions. Use spot instances for tasks that can handle interruptions - like data analytics, temporary CI/CD jobs, or checkpoint-enabled applications. On the other hand, reserve on-demand instances for workloads that demand consistent performance or have unpredictable requirements [1].
For workloads running on spot instances, implementing checkpointing mechanisms can be a game-changer. These allow applications to restart from specific points if interrupted, making spot instances usable for a broader range of tasks [17]. Orchestration tools can also help by automatically balancing workloads between spot and on-demand instances, depending on price and availability [1].
How Hokstad Consulting Can Help
While these methods can significantly reduce costs, implementing them effectively can be challenging due to the complexity of cloud pricing and the technical expertise needed. That’s where Hokstad Consulting steps in. Specialising in cloud cost optimisation, they help UK businesses cut cloud expenses by 30–50% through tailored strategies.
Their cloud cost audits identify immediate savings opportunities by analysing your current instance usage and recommending optimal configurations. They also offer DevOps transformation services, such as automated CI/CD pipelines and monitoring solutions, which align seamlessly with hybrid spot and on-demand strategies.
Hokstad Consulting’s migration services ensure that transitioning to more cost-effective architectures happens without downtime. Their expertise spans public, private, and hybrid cloud environments, and they design solutions tailored to your business needs and compliance requirements.
What sets them apart is their flexible engagement model, including a No Savings, No Fee
option. Under this arrangement, their fees are capped as a percentage of the savings they achieve, ensuring their success aligns with your goals. It’s a risk-free way to optimise your cloud spending.
Beyond initial savings, they provide ongoing support, covering performance optimisation, security audits, and infrastructure monitoring. Their custom development and automation capabilities ensure you maximise the benefits of both spot and on-demand instance strategies, while maintaining the reliability your business demands.
As cloud pricing continues to shift, having expert guidance becomes increasingly valuable. For example, the price difference within the same instance family can vary by as much as 980% [18]. This highlights the importance of professional cost engineering in keeping your cloud spending competitive and efficient.
Conclusion
Choosing between spot and on-demand instances boils down to balancing two key factors: cost savings and reliability. Spot instances can slash costs by as much as 90% compared to on-demand pricing, making them a tempting option for organisations looking to reduce expenses.
That said, these savings come with trade-offs in reliability. Spot instances are perfect for tasks like batch processing, data analysis, or development environments where interruptions are acceptable. On the other hand, on-demand instances are the go-to choice for critical applications that demand consistent uptime.
The key lies in striking the right balance. Adopting a hybrid approach - leveraging both instance types based on the importance of the workload - allows UK businesses to keep cloud costs in check without compromising operational stability. With the ever-changing nature of cloud pricing, seeking expert advice can be invaluable. By using automation tools, checkpointing systems, and distributing workloads across multiple availability zones, organisations can make the most of spot instances while minimising potential disruptions [11].
FAQs
How can I manage the unpredictability of spot instance pricing to save costs effectively?
To tackle the unpredictable nature of spot instance pricing while cutting costs, begin by examining historical pricing trends. This will give you a clearer picture of how prices fluctuate over time. Pair this insight with automation tools that can handle resource allocation dynamically - think auto-scaling or rebalancing across different instance types and availability zones.
Another smart move is to adjust your workload scheduling. For tasks that aren't time-sensitive, aim to run them during off-peak periods or when spot prices drop. Setting your bid prices close to On-Demand rates is another way to reduce the risk of interruptions while still reaping considerable savings. By combining these strategies, you can strike a balance between cost efficiency and operational reliability.
How can I make my applications resilient to interruptions when using spot instances?
To make sure your applications can handle the occasional interruptions that come with spot instances, it's essential to focus on creating fault-tolerant architectures. This means using auto-scaling to adjust capacity as needed and building in redundancy to reduce the impact of any disruptions.
Another smart approach is to diversify the types of instances you use. Relying on just one type can increase your risks during capacity shortages, so mixing it up helps spread that risk. You might also want to implement auto-recovery mechanisms and take advantage of managed services that support various instance types to boost your application's resilience.
By weaving these strategies into your design, you can enjoy the cost benefits of spot instances without sacrificing reliability or performance.
How can I choose the right mix of spot and on-demand instances for my business?
Choosing the right mix of spot instances and on-demand instances comes down to your workload needs and budget priorities. Spot instances offer savings of up to 90% compared to on-demand pricing, making them a great choice for non-essential tasks like batch processing, data analysis, or testing environments that can handle interruptions. Meanwhile, on-demand instances are the better option for critical applications that demand consistent, uninterrupted performance.
For many businesses, a hybrid strategy strikes the perfect balance. By running essential workloads on on-demand instances and using spot instances for less critical tasks, you can manage costs without compromising reliability. To make the most of this approach, take a close look at how predictable or flexible your workloads are and adjust accordingly to find the most budget-friendly solution for your needs.