Three managed platforms dominate the conversation when a team decides to move compute closer to users or machines: AWS Wavelength, Azure Private MEC, and Google Distributed Cloud Edge. In a slide deck they look interchangeable. In production they behave nothing alike. One lives inside a carrier's 5G network, one is built around private cellular on your own campus, and one ships Google-managed hardware to a location you choose.

Choosing between them is less about raw speed and more about where your devices already are, and who controls the radio they talk to.

Deployment Models Come First

Latency figures are easy to quote and hard to compare. The deployment model tells you more, because it determines which devices can even reach your workload.

AWS Wavelength

Wavelength places AWS compute and storage inside a telecom operator's 5G network, in specific metros with specific carriers such as Verizon, Vodafone, and KDDI. You deploy the same way you deploy to a normal region — VPCs, subnets, familiar APIs — but the subnet sits in a carrier zone. Traffic from devices on that carrier's 5G network reaches your application without leaving the mobile network.

The catch is the word that. A phone on Wi-Fi, a device on another carrier, or a laptop on a wired connection does not get the shortcut. Wavelength is a targeted tool for mobile subscribers, not a general-purpose edge presence.

Azure Private MEC

Azure Private MEC targets private cellular campuses: ports, mines, factories, stadiums, hospitals. Azure services and Azure Arc–managed Kubernetes run on Azure Stack Edge or comparable hardware on site, paired with a private LTE or 5G core from a partner. Microsoft has since folded much of this story into Azure Private 5G Core and Azure Operator Nexus, so expect naming to shift as you research.

Pick this model when you control the spectrum and the radios. That control buys you coverage exactly where you need it, and it also means you own the consequences when coverage is patchy.

Google Distributed Cloud Edge

Google Distributed Cloud Edge puts Google-managed hardware — racks or servers — into your facility or a Google edge location, extending Google Cloud APIs and running containerized workloads and virtual machines. It connects back to a Google Cloud region over an interconnect or your existing WAN. It suits retail chains, manufacturers, and telcos that want cloud tooling on premises without running their own control plane.

Availability, carrier partners, and hardware options change often on all three platforms. Check current regional coverage and lead times before you design around any of them.

Latency Is Not One Number

What matters to a user is the round trip from tap to response. That includes the device, the radio, the edge node, and every service call behind it. A platform that removes 20 milliseconds of backhaul can still feel slow if your application makes thirty sequential calls once it arrives.

• User-to-compute hop. Wavelength wins for public 5G subscribers. Private MEC and Distributed Cloud Edge win when the device is on the same campus or inside the same building.
• Jitter and p99. Averages hide the pain. Radio scheduling, handovers, and shared uplinks create tail latency that a graph of p50 will never show you.
• The return path. Anything stateful — sessions, model weights, transaction logs — still talks to a region. That trip is often the real bottleneck.

Protocol overhead counts too. TLS handshakes, DNS lookups, service mesh sidecars, and cold caches can each add more than the network hop you just eliminated.

A one-millisecond hop is worthless if the application behind it needs twelve round trips to answer a single request.

What Operating Them Actually Costs You

Managed means the provider handles the control plane, not that the platform runs itself.

• Wavelength: AWS manages the infrastructure, but you inherit carrier dependencies — coverage maps, commercial agreements, and the reality that a network change at the operator can affect your deployment.
• Azure Private MEC: someone has to plan radio coverage, manage SIMs and devices, and physically secure the cabinet. That is either your team or a systems integrator on retainer.
• Google Distributed Cloud Edge: Google handles software lifecycle, you supply power, cooling, rack space, physical security, and a reliable uplink. Losing that uplink needs a defined behaviour.

Then there is the bill. Data moving between an edge location and a cloud region is charged, sometimes on both ends, and some deployments carry minimum commitments. Model the steady-state traffic before you commit, not just the pilot.

Matching Workloads to Platforms

Most projects sort themselves once you answer one question: whose radio is the device using?

• Cloud gaming or AR on consumer phones — Wavelength, because the audience arrives on public 5G and you cannot install anything at their location.
• Autonomous mobile robots in a warehouse — Azure Private MEC. You need deterministic coverage in a defined space and you can control the spectrum.
• Computer vision on a production line — Distributed Cloud Edge or Private MEC, depending on whether the cameras run on Wi-Fi, Ethernet, or private cellular.
• Store-level inference across a retail chain — Distributed Cloud Edge, where consistent hardware and remote management matter more than milliseconds.
• Connected vehicle telemetry on public roads — Wavelength, since the vehicles move through carrier coverage rather than staying on one site.

Where Teams Get It Wrong

Three mistakes show up again and again. The first is treating the edge node like a region — assuming durable local state, unlimited capacity, and the same redundancy you get in a data centre. Keep the edge stateless where you can, and treat the region as the source of truth.

The second is designing for one carrier or one site and discovering later that a third of your users never touch it. Build a fallback path to the region on day one, and test it by pulling the plug.

The third is optimising the network while ignoring the client. A mobile app that blocks the main thread for 80 milliseconds while parsing a response will feel slow no matter where the server sits.

A Practical Test Plan Before You Commit

1. Write down the end-to-end latency budget, including the client, and decide which percentile you will hold yourself to. p95 is a reasonable starting point for interactive apps.
2. Pick the worst realistic location and device, not the one next to your office router.
3. Deploy a small probe that measures real payload sizes with TLS enabled, and record p50, p95, and p99.
4. Run it for at least two weeks across peak hours so handovers and congestion show up.
5. Break it on purpose: cut the backhaul, restart the node, and watch what the application does.
6. Read the commercial terms — commitments, egress, hardware lead time, and support response times — before the architecture is frozen.

If your users arrive on public 5G, Wavelength is usually the shortest path. If you own the spectrum, Private MEC gives you control that no public network can. If you own the building and want cloud tooling inside it, Distributed Cloud Edge is the comfortable fit. Whichever you pick, measure before you commit — the numbers that matter are yours, not the ones on the slide.

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