Cloud remains a powerful foundation for scale, analytics, and collaboration, but relying on it as the only platform for critical workloads introduces avoidable risk.

Recent data points highlight this:

• All three major hyperscalers — AWS, Google Cloud, and Microsoft Azure — experienced notable outages in 2025.
• Industry data shows they had around 100 service outages between them in the 12 months from August 2024 to August 2025.
• Over 75% of Fortune 500 companies rely on AWS, and more than 55% on Microsoft Azure.
• Worldwide end-user spending on public cloud services was expected to reach $723.4 billion in 2025, with double-digit growth across segments.

This level of concentration means that when a major provider has a problem, the impact can ripple across interconnected systems and partners. Even a short outage can lead to:

• Lost sales and revenue
• Productivity loss and recovery costs
• Longer-term damage to customer trust and brand reputation

It’s also important to be realistic about service levels: 100% uptime simply doesn’t exist in practice, even for the largest providers. Any cloud platform can experience downtime lasting from minutes to many hours.

For organisations whose IT has become more distributed, with growing volumes of data generated and used at the edge (for example in retail stores, hospitals, or manufacturing plants), waiting for a centralised cloud service to recover is often not an option. These environments typically demand continuous local uptime.

This is why many digital businesses are rethinking a cloud-only mindset and moving toward hybrid models that combine cloud with edge computing. The goal isn’t to abandon cloud, but to avoid making it a single point of failure for essential workloads.

Edge computing helps you reimagine resilience by keeping essential services running locally, even when cloud access is disrupted.

Here’s how it strengthens a cloud strategy rather than competing with it:

1. Local continuity when cloud is unavailable
   Decentralised edge infrastructure allows key applications and data to run and be stored close to where they’re needed. If there’s a cloud outage, an internet issue, or local disruption to cloud access, edge systems can continue operating independently until connectivity is restored.
2. Reduced dependency on a single provider
   With around 100 outages across AWS, Azure, and Google Cloud in a recent 12-month period, depending solely on any one of them for mission-critical workloads is risky. Edge architectures give you an additional layer of control and resilience, so a hyperscaler incident doesn’t automatically mean downtime for your frontline operations.
3. Better fit for always-on edge environments
   Sectors like retail, healthcare, and manufacturing often need continuous uptime at distributed sites. For these locations, “waiting for the cloud to come back” is not realistic. Edge computing lets these sites keep running core processes locally while still syncing with the cloud for analytics, reporting, and collaboration.
4. Complementary roles for cloud and edge
   A practical hybrid model typically looks like this:
   • Cloud for large-scale compute, analytics, collaboration, and (in AI scenarios) model training and centralised storage.
   • Edge for local processing, storage, and real-time decision-making, including running lightweight AI models close to the data source.
   This combination helps you maintain service levels locally while still benefiting from the scale and intelligence of the cloud.

In short, edge computing doesn’t replace cloud. It reinforces it, giving you a more resilient, hybrid infrastructure that is better able to absorb outages and minimise disruption to critical business workloads.

A practical hybrid strategy blends cloud and on-premises edge computing in a way that fits your specific business requirements, rather than following a one-size-fits-all template.

Key elements include:

1. Deliberate design, not default choices
   Instead of assuming “cloud-first” or “cloud-only” is always best, you identify which workloads:
   
   • Must keep running locally during a cloud or network outage
   • Benefit from low latency and local processing
   • Can safely rely on centralised cloud services
   You then design an architecture that places each workload where it makes the most sense.
2. Cloud-enabled, not cloud-dependent edge
   On-premises edge infrastructure is integrated with the cloud but not fully dependent on it. If the cloud or internet connection fails, edge systems can still operate, store data, and support critical processes, then sync back when connectivity returns.
3. Close collaboration with service providers
   Rolling out hybrid infrastructure usually involves working closely with technology and service partners to align on:
• Cloud and storage design
• Resilience and recovery objectives
• Operational management of many smaller sites
4. Use of modern edge hardware and management tools
   The edge market is maturing quickly. The global edge computing market is estimated to grow from $28.5 billion in 2026 to $263.8 billion in 2035, at a 28% compound annual growth rate. Hardware innovation is delivering data centre-level performance in more compact, lower-cost formats, and management tools are making it easier to run and monitor distributed sites.
5. Support for AI at both cloud and edge
   In AI deployments, a hybrid model is particularly effective:
   • Cloud handles large-scale model training, storage, and central analytics.
   • Edge runs lightweight models for faster, local decision-making where data is generated.
   This approach helps you get value from AI in real time while still leveraging the cloud for heavy lifting.

Overall, a cloud–edge hybrid strategy is about reshaping your architecture so that critical operations continue even if the cloud is out. It’s not a rejection of cloud, but a more measured way to embrace it while minimising disruption and protecting business continuity.