By Dan Reger, Senior Director, Product Marketing, Cloud
Why cloud bursting and extension strategies succeed or fail based on how data is accessed and managed
When IT demand increases, many organizations start looking at ways to migrate more applications to the cloud. They need a way to add cloud resources to their on-premises environment as needs expand. But adding compute is only part of the equation – those resources are only useful if unstructured data can be accessed where and when it’s needed.
Let’s explore and differentiate cloud bursting from extending to cloud and how data being accessed, placed, and managed across environments shapes both approaches to expanding your infrastructure.
These two approaches serve different goals and demand different preparation.
Cloud bursting can be your emergency relief valve for compute. The goal is temporary, peak capacity. It is inherently reactive. For example, as your workload demand hits 90%, automated systems can immediately start spinning up compute instances in the cloud to absorb any overflow.
This can help you respond to rapidly changing and often unpredictable needs. Because this burst might only last an hour, a week, or a single Black Friday, the underlying financial model for that extra capacity must be pay-as-you-go (PAYGO) to keep costs in check. Then those compute instances can be spun down automatically just as quickly as they were spun up. While you pay a premium for that minute-by-minute flexibility, this is offset by the savings of not running that extra capacity most of the time, when your workload isn’t peaking.
Extending to the cloud, conversely, has a longer-term, strategic goal. This isn’t about handling next Tuesday’s peak traffic, it’s about proactively building a permanent set of additional resources for the regular growth of your business. The goal is a seamless hybrid architecture where specific workloads live permanently in the cloud, often utilizing the same management and policies as your physical data center.
This is proactive work, often moving an entire application tier once and leaving it in the cloud for an extended period. Because the usage is more predictable and consistent, you typically lock in lower, long-term committed-use rates rather than choosing a PAYGO model.
What About AI?
You might assume that AI would make it a good candidate for cloud bursting. However, the modern realities of AI infrastructure in the cloud often break the cloud bursting model.
First, the scale and cost of resources needed, typically hundreds of GPUs or even thousands for training workloads, are usually only available through a long-term commitment. Hyperscale cloud providers generally will not offer massive GPU clusters for a few hours or a few days on a PAYGO basis. These resources are in too high a demand; the provider wants a multi-year commitment, which is exactly the opposite of the temporary bursting model.
However, for those needing short-term GPU capacity without a multi-year contract, specialized “neocloud” providers are emerging to fill that niche. Some provide smaller GPU clusters of around 100 GPUs on a PAYGO basis.
Second, the massive data sets needed to train large language models, and the tightly coupled nature of many AI tasks makes bursting such workloads to the cloud incompatible with the “immediate” requirements of bursting scenarios. By the time you move or duplicate everything your workload needs, your peak demand may have passed.
This isn’t to say AI can’t use the cloud, of course, but it is more likely to use the extended cloud model, especially for training tasks. You permanently allocate a specific GPU cluster in the cloud, integrating it with your long-term infrastructure. In effect, AI turns hybrid cloud from a burst problem into a data placement and access problem.
Data: The Essential Catalyst to Maximize Hybrid Compute
Regardless of whether you are trying to handle a five-minute compute spike (bursting) or build a permanent five-year hybrid compute cluster (extending), the true bottleneck is often data—specifically how it is accessed, placed, and managed across environments.
This is why a powerful data platform that optimizes data access across both cloud and on-premises environments is not just useful, it’s essential for success. The challenge isn’t where compute runs, but whether data can be accessed without friction across environments.
In a cloud bursting scenario, your application can spin up in the cloud in seconds, but it cannot function until it can access your data, which may remain on premises.
A modern data platform orchestrates data placement and access across environments, allowing on-premises data to be used by cloud compute nodes without requiring bulk data migration. It solves the “data gravity” problem that often paralyzes cloud bursting attempts.
Traditional approaches assume data must move to where compute runs. Modern architectures make data accessible where it already exists. That requires a fundamentally different approach to how data is managed across on-premises and cloud environments..
The Hammerspace Data Platform assimilates metadata, decoupling the file system from the underlying storage layer to create a single unified global namespace that spans storage systems, storage tiers, data centers, and clouds. Silos are eliminated, data services are centralized, and data orchestration becomes a non-disruptive background operation.
For example, Hammerspace metadata servers in the cloud can give your compute instances unified file system access to both cloud storage and on-premises storage without the need to migrate that data to the cloud. The orchestration layer will bring in only the data required by the workload or your policies. Tiered data lifecycle rules can also move cold data to cheaper cloud storage, or move active data to faster storage, completely transparently to users and applications.
A unified data platform truly excels with an extend-to-cloud strategy. When you are adding a bathroom to your home, you need a single, unified blueprint. Your data shouldn’t just exist in both locations; it should be a single resource pool, available everywhere.
Whether you’re building to burst or extend, your compute strategy, whether for AI or simple web apps, is ultimately tied to your data. Investing in a data layer that can actively orchestrate placement and management enables a hybrid cloud infrastructure that adapt to any workload, time horizon, or workload peak.
Learn More
- Hybrid Cloud Use Case page
- Press Release: CRN Recognizes Hammerspace for AI Training and Inferencing Performance on 2026 Cloud 100 List
- Tech Field Day article: Accelerate cloud and AI workloads with the Hammerspace Data Platform
- Blog Post:Global Energy Technology Company NOV Chooses Hammerspace for Cloud-Based Animation and Render Pipeline
