AWS Direct Connect Hanoi: Faster AI Workloads, Less Risk

On December 16, 2025, AWS quietly made a big networking move: the first AWS Direct Connect location in Vietnam opened in Hanoi (CMC Tower). If your team is building AI systems that span on‑prem, colocation, and cloud, this isn’t a minor checkbox on a global infrastructure map. It’s a practical shift in how reliably you can move data—especially the kind that powers model training, real-time inference, and cross-region analytics.

Most companies treat connectivity as plumbing: buy a link, monitor it, forget it. That’s the wrong mental model for 2026. Network paths increasingly shape AI cost, latency, and even energy usage because AI workloads are data-hungry, bursty, and sensitive to jitter. A new private on-ramp in Hanoi changes the options for Vietnamese enterprises and regional operators who need predictable performance without sending critical traffic over the public internet.

This post breaks down what the Hanoi Direct Connect site offers, where it fits in modern AI in cloud computing & data centers, and how to use it as a foundation for AI-driven workload placement, smarter traffic management, and better resource allocation.

What the new AWS Direct Connect location in Hanoi actually adds

Answer first: The Hanoi site provides private, dedicated connectivity from Vietnam into AWS’s global footprint, including public AWS Regions (except China), AWS GovCloud Regions, and AWS Local Zones, with connection speeds up to 100 Gbps and optional MACsec encryption at higher speeds.

Here’s what AWS announced for Hanoi:

• Location: CMC Tower, Hanoi, Vietnam
• First Direct Connect location in Vietnam
• Dedicated port speeds: 1 Gbps, 10 Gbps, and 100 Gbps
• Security option: MACsec available for 10 Gbps and 100 Gbps connections
• Reach: Access to all public AWS Regions (except those in China), plus AWS GovCloud and AWS Local Zones

If you’ve been relying on IPSec VPNs over the internet (or a patchwork of regional circuits), this matters because Direct Connect is a physical private path. That gives you more consistent throughput and latency characteristics—less “it was fine yesterday” behavior.

Why this matters more for AI than for classic web apps

AI workloads punish uncertainty.

• Training and fine-tuning move large datasets and checkpoints repeatedly.
• Inference is often latency-sensitive, especially for retrieval-augmented generation (RAG), fraud scoring, personalization, and agentic workflows.
• Data pipelines (streaming, ETL/ELT, lakehouse sync) hate jitter and random packet loss.

A stable private link makes performance more predictable, but it also improves planning: once the network is consistent, you can automate placement and scaling decisions with fewer “unknowns.” That’s where AI-driven infrastructure optimization starts to pay off.

Hanoi is a connectivity milestone—but the strategic story is resource allocation

Answer first: This expansion isn’t just about adding bandwidth in Vietnam; it’s about enabling smarter global workload placement and more efficient use of compute and storage—which is exactly where AI is being applied in modern cloud operations.

In the “AI in Cloud Computing & Data Centers” series, we keep coming back to one theme: AI optimization needs controllable inputs. Compute is one input. Storage is another. The network is the third leg of the stool—and it’s often the messiest.

When you introduce a Direct Connect site into an emerging, high-growth market, three optimization opportunities show up quickly:

1. Lower and more consistent latency enables tighter feedback loops (real-time scoring, interactive copilots, low-latency vector search).
2. Predictable throughput improves batch scheduling (training jobs, replication windows, backup and recovery).
3. Reduced network variability improves the quality of ML-driven routing and placement decisions.

A blunt but useful line I’ve found true: you can’t optimize what you can’t predict. Direct Connect makes the network more predictable.

A simple example: “Where should inference run?” becomes answerable

Many teams end up running inference in a single region because networking is unreliable elsewhere. That’s easy, but it’s expensive in hidden ways—latency, user experience, and overprovisioning.

With a stable private connection from Hanoi:

• You can keep sensitive data sources on-prem or in a local colo, while running inference in AWS with controlled egress/ingress.
• You can separate feature stores, vector databases, and application front ends based on latency and cost targets.
• You can move from “one place for everything” to policy-based placement (latency SLOs, cost ceilings, compliance constraints).

That’s the foundation for AI-assisted resource allocation: systems that automatically pick the best place to run a workload based on real measurements.

Direct Connect + AI workloads: where you’ll feel the difference

Answer first: The biggest wins show up in three areas—data gravity, real-time inference, and hybrid governance—because those are the places where public-internet variability causes the most operational pain.

1) Data gravity: keeping large datasets close while still using cloud AI

A lot of Vietnamese and regional organizations (banks, telcos, manufacturers, marketplaces) have real constraints:

• Data can’t freely move everywhere.
• Some systems still live on-prem for latency or regulatory reasons.
• Migration is gradual, not a “big bang.”

Direct Connect gives you a cleaner model: keep bulk data where it needs to be, and move only what’s required across a private circuit—especially when you’re syncing:

• transaction logs
• clickstream events
• sensor/IoT data
• document corpora for RAG
• embeddings and feature snapshots

When throughput is stable, you can schedule transfers during low-cost windows and reduce retries. That’s not glamorous, but it directly cuts AI pipeline cost.

2) Real-time inference: jitter is the silent killer

If you’re running inference behind an app (risk scoring, recommendations, dynamic pricing, contact center copilots), users don’t complain about “packet loss.” They complain that the app feels slow.

The combination of private connectivity and high-capacity ports (up to 100 Gbps) makes it realistic to:

• keep request latency tighter for Hanoi-based users and operations teams
• reduce variance (p95/p99) that makes autoscaling expensive
• support multi-service inference chains (RAG retrieval → reranker → generator) without death-by-a-thousand-network-hops

If you track user experience, don’t just look at average latency. Watch the tails. A more consistent network path often shows up as improved p95/p99 response time even when averages barely move.

3) Hybrid governance: security and compliance without duct tape

The announcement notes MACsec encryption for 10 Gbps and 100 Gbps connections. Practically, that gives you another option for securing data-in-transit on the link itself.

For teams under strict internal controls, it’s often easier to get approval for:

• private circuits
• deterministic routing
• clear boundary points (colo handoff, port ownership)

…than it is to justify complex internet overlays.

None of this removes the need for application-layer encryption and identity controls, but it reduces the number of “moving parts” in the connectivity story. And fewer moving parts usually means fewer late-night incidents.

How AI-driven networking turns a private link into an optimization engine

Answer first: Direct Connect is the physical foundation; AI-driven networking is how you convert that foundation into better cost, performance, and energy outcomes through measurement and automated decisions.

A stable circuit enables you to collect cleaner signals—latency, jitter, packet loss, throughput—and feed them into models or policy engines. You don’t need science-fiction automation to benefit. Start with decision rules, then graduate to ML.

Practical pattern: traffic-aware workload placement

Here’s a realistic approach I’ve seen work in production:

1. Measure: continuously record link utilization and end-to-end latency between your Vietnam edge/colo and key AWS services.
2. Predict: forecast utilization (hourly/daily patterns) and latency risk (congestion periods).
3. Decide: shift workloads accordingly:
   • run training jobs during off-peak transfer hours
   • pre-warm inference capacity when latency risk rises
   • replicate embeddings/features on a schedule that matches business demand

The important part is the decision loop. AI ops isn’t only about models—it’s about closing the loop from telemetry → decision → action.

Energy and cost: the overlooked link between networking and efficiency

Data centers and cloud operators increasingly optimize for energy, not just compute cost. The network plays a role because:

• inefficient transfers create retries and duplicate processing
• unpredictable latency encourages overprovisioning
• scattershot routing can increase cross-region traffic

Better private connectivity supports more deterministic scheduling. That enables simpler (and often greener) operations: fewer re-runs, fewer always-on buffers, fewer “just in case” replicas.

Implementation checklist: how to evaluate Direct Connect in Hanoi

Answer first: Treat the Hanoi Direct Connect site as a design option for hybrid AI architecture, then validate it with a short pilot focused on latency tails, throughput stability, and operational simplicity.

If you’re considering this location, run a structured evaluation:

Step 1: Map AI data flows (not just applications)

List the top 5–10 flows by business value and volume:

• training dataset sync
• feature store refresh
• embedding generation and replication
• event streaming into analytics
• model artifact distribution

If you can’t name the flows, you’ll struggle to justify the circuit.

Step 2: Define SLOs that reflect AI reality

For AI systems, “uptime” isn’t enough. Set targets like:

• p95/p99 latency for inference calls
• max tolerated jitter for streaming
• replication completion windows (e.g., nightly embeddings finished by 6 a.m.)
• acceptable data freshness for RAG corpora

Step 3: Start with a pilot that forces the hard problems

Pick one workload that’s currently painful:

• RAG with large document updates
• real-time risk scoring
• multi-site analytics replication

Then compare before/after across:

• p95/p99 latency
• transfer retries
• operational time spent on incidents
• throughput stability during peak hours

Step 4: Plan security from day one

If you need link-layer encryption, evaluate MACsec on eligible ports (10/100 Gbps). Also align:

• identity and access controls
• segmentation (VLANs, routing boundaries)
• monitoring and alerting ownership between network and cloud teams

Step 5: Build the “AI optimization hook” into operations

Don’t stop at “the link works.” Add automation goals:

• forecast utilization
• set routing/placement policies
• schedule transfers intelligently
• trigger scale events based on link signals

This is where the expansion becomes more than connectivity—it becomes a platform for continuous optimization.

Snippet-worthy truth: A private circuit isn’t the finish line. It’s the sensor and control plane for smarter cloud operations.

What this signals for Vietnam and the region in 2026

Answer first: The Hanoi Direct Connect location is a strong indicator that AI and cloud growth in Vietnam is entering the “operational excellence” phase, where reliability, governance, and optimization matter as much as raw adoption.

We’re seeing a broader pattern across cloud infrastructure: as AI workloads mature, companies stop asking “Can we run this in the cloud?” and start asking “Can we run it predictably, securely, and efficiently at scale?” Networking determines whether the answer is yes.

If you’re building AI platforms in Vietnam or supporting customers there, the new Hanoi on-ramp reduces friction for hybrid architectures and makes it easier to justify production-grade AI systems that depend on stable data movement.

The forward-looking question I’d be asking internally is simple: now that the network path is more controllable, what decisions can we automate—placement, scaling, replication, or all three?