Most companies did not start life “in the cloud.” They started with physical buildings, physical servers, and physical hard drives. Over decades, this created enormous amounts of important data: customer records, video archives, financial ledgers, medical scans, engineering drawings. Moving all of that instantly into the cloud is like asking someone to teleport their entire house, furniture included, in one afternoon. It is expensive, risky, and the old house (the on-premises systems) usually still needs to keep working during the move.

AWS Storage Gateway is Amazon’s answer to this exact situation. It is a hybrid cloud storage service: “hybrid” meaning part of the system stays on the company’s own premises (in their office or data center) and part of it lives in AWS’s cloud, with the two halves talking to each other constantly and invisibly.

Formally, AWS Storage Gateway is a service that connects on-premises software appliances with cloud-based storage to provide seamless integration between an organization’s on-premises IT environment and AWS’s storage infrastructure. It uses standard storage protocols that servers and applications already understand — so nothing on the customer’s side has to be rewritten.

A handful of terms recur throughout this topic. Getting comfortable with them now makes everything downstream much easier to follow.

Every deployment of Storage Gateway is built from the same basic building blocks, arranged slightly differently depending on which “disguise” is needed.

flowchart LR
    subgraph OnPrem["On-Premises Data Center"]
        APP["Applications & Servers"]
        GW["Storage Gateway Appliance (VM / Hardware)"]
        CACHE[("Local Cache Disk")]
        BUF[("Upload Buffer Disk")]
        APP -->|NFS / SMB / iSCSI| GW
        GW --- CACHE
        GW --- BUF
    end
    subgraph AWSCloud["AWS Cloud"]
        S3[("Amazon S3")]
        EBS[("Amazon EBS Snapshots")]
        GLACIER[("S3 Glacier")]
        VTL[("Virtual Tape Library")]
    end
    GW -->|Encrypted Upload| S3
    GW -->|Volume Backups| EBS
    S3 -->|Lifecycle Rules| GLACIER
    GW -->|Tape Emulation| VTL
        

Reading the diagram left to right: applications on the company’s own servers never talk to AWS directly. They only ever talk to the local gateway appliance, using storage protocols they were already using. The gateway is the only component that actually reaches out across the internet to AWS, and it does so over an encrypted channel.

Every write and read passes through a predictable sequence of steps, regardless of which gateway type is in use. Understanding this sequence is the key to understanding performance, durability, and troubleshooting later on.

It helps to picture this step-by-step process the way a busy restaurant kitchen works. The application is the waiter taking an order (a write request). The upload buffer is the kitchen’s ticket rail — orders line up there the instant they arrive, so the waiter can immediately move on to the next table instead of standing around waiting. The chefs (the background upload process) then work through the ticket rail at a steady, sustainable pace, sending finished dishes (uploaded data) out to be served (durably stored in AWS). No customer ever notices the kitchen queue; they only notice that their order was taken quickly and their food eventually arrived correctly.

This separation between “accepting the write” and “durably storing the write” is precisely what allows Storage Gateway to feel fast even though its true home for the data is hundreds or thousands of miles away in an AWS Region. It is also why network quality still matters: if the kitchen’s chefs (the upload process) can never work through the ticket rail faster than new orders arrive, the buffer eventually fills up, and the whole system has to slow down new writes until it catches up. This is one of the most important operational realities to understand about any hybrid storage system, not just Storage Gateway specifically.

sequenceDiagram
    participant App as On-Prem Application
    participant GW as Storage Gateway
    participant Cache as Local Cache/Buffer
    participant S3 as Amazon S3 / EBS

    App->>GW: Write file / block / tape data
    GW->>Cache: Store locally (fast ack)
    GW-->>App: Write acknowledged
    GW->>S3: Asynchronous encrypted upload
    S3-->>GW: Durable storage confirmed
    App->>GW: Later, request same data
    GW->>Cache: Check local cache
    alt Cache hit
        Cache-->>App: Instant response
    else Cache miss
        GW->>S3: Fetch object
        S3-->>GW: Return data
        GW-->>App: Deliver data
    end
        

Over the long term, data does not have to sit in the same storage tier forever. S3 lifecycle rules can automatically move older File Gateway objects into cheaper, colder storage classes like S3 Glacier, and virtual tapes that have not been accessed for a while can be archived into deep-archive storage — all without the on-premises applications ever noticing.

Because reads and writes are cache-aware, performance is really governed by two numbers: how big the local cache is relative to the “working set” (the data actually used day to day), and how much upload bandwidth is available. A gateway sized with enough cache to hold a business’s active files will feel just as fast as local disk almost all the time.

On the cloud side, scalability is effectively AWS’s problem to solve, not the customer’s: S3 and EBS scale storage capacity automatically behind the scenes, so a company never has to plan a “disk expansion project” again.

It is worth being precise about what “scalable” means here, because it is easy to assume it means “infinitely fast” as well as “infinitely large.” Those are two different properties. Capacity scalability means a company will essentially never be told “sorry, the disk is full” again, because the backing store is a globally distributed object and block storage platform designed to absorb exabytes of data across many customers. Throughput scalability, on the other hand, is bounded by the size of the local cache and the speed of the network link between the office and AWS. A small branch office on a modest internet connection will comfortably store petabytes of cold archival data over time, but it will not turn that same modest connection into a suddenly blazing-fast pipe for constant, large, uncached reads. Understanding this distinction up front prevents a very common and very avoidable disappointment during rollout.

A useful mental model when planning capacity is to separate data into three buckets: hot data that is read or written constantly and should always fit comfortably inside the local cache; warm data that is accessed occasionally and can tolerate the small delay of a cache miss; and cold data that is rarely touched and is perfectly happy living exclusively in the cloud, possibly even in a cheaper archival storage tier. Sizing the gateway around this three-tier mental model, rather than simply guessing a round number for the cache disk, is what separates a smooth deployment from a frustrating one.

Because the durable copy of data lives in Amazon S3 or EBS, it inherits the extremely high durability those services provide — data is stored redundantly across multiple facilities. If the on-premises gateway appliance itself fails, it can typically be replaced or redeployed, and reconnected to the exact same cloud-side data, since the source of truth for uploaded data lives in AWS, not solely on the local box.

Reliability in a hybrid system like this has two separate dimensions that are easy to conflate: the reliability of the data itself, and the reliability of the path to reach that data. The data’s durability comes almost entirely from AWS’s storage layer, which spreads copies of every object across multiple independent facilities within a Region, so that the loss of a single disk, server rack, or even an entire building does not put the data at risk. The path to reach that data, however, runs through the on-premises gateway appliance and the internet connection between the office and AWS — and that path can absolutely fail on its own, even while the underlying data remains perfectly safe.

This is why organizations that treat reliability seriously often deploy a second, standby gateway appliance that can be activated quickly if the primary one fails, and why they pay attention to internet connection redundancy (for example, a backup internet line from a different provider). None of this changes the safety of the data sitting in AWS; it changes how quickly the business can get back to actually using that data after a local hardware or network failure.

Security is layered rather than relying on any single mechanism.

It is worth walking through why each of these layers exists rather than treating them as an arbitrary checklist. Encryption in transit protects data while it is physically traveling across a network that the company does not fully control — the public internet, in most cases — where a malicious party could otherwise intercept raw traffic. Encryption at rest protects data once it has arrived at its destination, guarding against a scenario where someone gains unauthorized access to the underlying storage hardware itself, which in a shared cloud environment is a meaningfully different threat model than in a private data center. Access control through IAM answers a completely different question: not “can this data be read if intercepted,” but “who is even allowed to ask for it in the first place.” And private connectivity options like Direct Connect exist for organizations whose compliance requirements or internal policies specifically prohibit sensitive data from ever touching the public internet, encrypted or not.

A beginner-friendly way to remember this is the idea of a locked delivery truck driving on a private road. Encryption in transit is the lock on the truck’s cargo doors. Encryption at rest is the locked warehouse the truck eventually parks inside. IAM is the guest list deciding who is allowed to open the warehouse door at all. And Direct Connect is choosing to build a private road for the truck instead of sending it down the public highway. Each protection covers a different point of weakness, which is exactly why real deployments use all four together rather than relying on any single one.

Amazon CloudWatch collects metrics such as cache hit percentage, upload buffer utilization, and throughput, so an operations team can see, in real time, whether the gateway is comfortably keeping up or straining against its cache and bandwidth limits. CloudWatch Alarms can then notify staff automatically before a small warning sign becomes a real outage.

Three metrics tend to matter more than all the others combined, and each answers a distinct operational question. Cache hit percentage answers “is my cache big enough” — a healthy, well-sized deployment should see this number stay consistently high, and a steady downward trend over weeks or months is usually the earliest warning sign that the working set of active data has outgrown the cache. Upload buffer percentage used answers “am I writing data faster than my internet connection can absorb it” — a buffer that regularly climbs toward full is a direct signal that either the internet link needs upgrading or the write pattern needs to be spread out over more of the day. Cloud bytes uploaded versus downloaded answers “how is this gateway actually being used day to day,” which is invaluable both for cost forecasting and for spotting unexpected usage patterns, such as an application unexpectedly re-reading cold archival data far more often than intended.

Treating these metrics as a dashboard to check only when something already feels slow is a missed opportunity. The far more effective habit, and the one experienced operations teams settle into, is reviewing the trend lines weekly, so that a cache that is quietly filling up gets a capacity upgrade scheduled calmly in advance, rather than an emergency one requested during a Friday afternoon slowdown.