A hybrid cloud is an IT environment that combines on-premises infrastructure with one or more public cloud services, connected in a way that allows data and workloads to move between them. Neither side is fully independent: the value of hybrid cloud comes from the integration between on-premises systems and cloud services, not from running them in parallel in isolation.

Why Hybrid Cloud Exists

The motivation for hybrid cloud is not primarily technical. Organizations that have hybrid cloud environments usually have them because of constraints that pure cloud migration cannot easily resolve:

Data gravity - Large datasets are expensive and slow to move. A media company with 500 terabytes of video on-premises will not migrate that to S3 in a reasonable timeframe without significant disruption and cost. The data stays where it is, and the architecture adapts to meet it there.

Existing investments - Enterprise storage, networking, and compute infrastructure has useful life remaining. On-premises systems that work well do not need to be replaced to enable cloud AI capabilities.

Compliance and data residency - Regulated industries (healthcare, financial services, government) often have requirements about where data can reside and who can access it. Hybrid cloud lets organizations keep sensitive data on-premises while using cloud services for processing that meets compliance requirements.

Latency requirements - Some workloads cannot tolerate the round-trip latency of a public cloud endpoint. Manufacturing systems, real-time control systems, and certain financial systems need compute close to the data source.

Hybrid Cloud for AI Workloads

AI workloads have a specific hybrid cloud challenge: the data that AI models need to learn from and process often lives on-premises (historical records, video archives, sensor data), while the best AI tools and most powerful models live in the cloud.

The hybrid cloud answer to this challenge is to move compute to the data rather than moving all the data to the compute. This takes several forms:

  • Running inference on-premises against models that were trained in the cloud
  • Using cloud AI services that can process data stored in on-premises-connected storage
  • Replicating only the data needed for a specific analysis job, running it, and keeping results in sync

AWS Hybrid Cloud Options

AWS provides several services designed for hybrid environments:

Amazon FSx for NetApp ONTAP - A managed file system that runs NetApp ONTAP in AWS. On-premises ONTAP systems replicate to FSxN using SnapMirror, creating a bridge that preserves existing NFS and SMB access patterns while enabling AWS integration. FSxN supports automatic tiering to S3, which acts as the handoff point into cloud AI services. This is particularly effective for media and data-intensive workloads where moving entire archives to S3 is impractical.

AWS Outposts - AWS-managed hardware that runs AWS infrastructure on-premises or in a colocation facility. Applications run the same AWS APIs, SDKs, and services they would in a cloud region. Outposts is suited for workloads that require AWS compute close to on-premises data sources, or that cannot move to a cloud region due to latency or compliance constraints.

AWS Local Zones - AWS infrastructure deployed in metropolitan areas outside of standard AWS regions. Local Zones reduce round-trip latency for end users in those locations and support workloads with strict latency requirements. They are not on-premises - they are AWS-operated - but they reduce the distance between users and compute.

AWS Direct Connect - Dedicated private network connections from on-premises data centers to AWS. Direct Connect provides consistent bandwidth and lower latency than internet connectivity, which matters for workloads that continuously move data between on-premises and cloud.

Hybrid Cloud is About People, Not Just Architecture

Linda Mohamed’s perspective, developed through AI solution work with enterprise clients: hybrid cloud is rarely a purely technical decision. The organizations that succeed with it treat it as a people and change management challenge as much as an infrastructure one.

Storage teams know their on-premises systems. Moving to a hybrid model changes their tools, their mental models, and sometimes their roles. Treating hybrid cloud as “just an architecture pattern” and skipping the change management work leads to environments that are technically connected but organizationally fragmented - nobody owns the boundary between on-premises and cloud, problems fall through the gaps, and the promised efficiency gains do not materialize.

The better framing is to start with the users of the system: what do they need, where are they, and what constraints do they operate under? The hybrid architecture should serve those users. The technology follows from that, not the other way around.

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