EC2 X2iedn in Zurich: Faster AI & memory-heavy workloads

Memory is the quiet bottleneck most teams don’t notice until production starts paging, latencies spike, and the bill becomes a weekly topic in leadership meetings. If you’re running SAP HANA, large in-memory analytics, or feature stores that keep growing, you’ve probably felt it: CPU isn’t the problem—RAM capacity and memory bandwidth are.

AWS just made a practical move for European enterprises building these systems: Amazon EC2 X2iedn instances are now available in the AWS Europe (Zurich) region (announced Dec 12, 2025). They’re memory-optimized, built on the AWS Nitro System, and powered by 3rd Gen Intel Xeon Scalable processors. The bigger deal isn’t “new instance types exist.” It’s that the right kind of memory-heavy compute is now available closer to Swiss and Central European workloads, which changes how you design for latency, compliance, and cost.

This post is part of our AI in Cloud Computing & Data Centers series, where we focus on how infrastructure choices—especially compute and regional placement—shape real AI and data outcomes.

Why Zurich availability matters for AI and data platforms

Answer first: Regional availability of memory-optimized compute in Zurich makes it easier to run AI-adjacent data systems (feature stores, vector search backends, in-memory analytics, SAP HANA) with lower latency and tighter data residency controls.

A lot of AI work isn’t GPU-first. Even teams doing heavy model training often get blocked earlier by “boring” layers:

• In-memory databases feeding applications and analytics
• ETL/ELT and data quality jobs that need big working sets
• Feature engineering and feature serving that benefit from large RAM caches
• Online inference systems where tail latency is driven by retrieval + memory pressure

When you can run these components in-region—especially in Zurich—you typically get:

• Lower and more predictable latency between app tiers, databases, and analytics services
• Simpler compliance postures for regulated industries that prefer Swiss/EU regional boundaries
• Cleaner architecture (fewer cross-region hops, fewer replication edge cases)

If you’ve ever tried to keep a transactional system in one geography while your “analytics brain” sits elsewhere, you know the trade: replication lag, operational overhead, and awkward incident response. Putting memory-heavy nodes where the business data lives reduces that friction.

What EC2 X2iedn instances are built for (and what they aren’t)

Answer first: X2iedn is a memory-optimized EC2 family intended for workloads where large RAM per node and strong price-performance matter more than raw GPU throughput.

AWS positions X2iedn as designed for memory-intensive workloads, with improvements in performance and cost per GiB of memory compared to the previous generation X1e. In practice, think of it as a better fit when you want to keep big datasets hot in RAM and avoid disk spill.

Workloads that fit X2iedn naturally

Here’s where I’d look first:

1. SAP HANA and SAP landscapes

   • In-memory architecture means RAM sizing drives everything.
   • AWS notes X2iedn is SAP-certified, including SAP S/4HANA and Business Suite on HANA.

2. Large in-memory analytics and OLAP engines

   • Systems that keep aggregates, indexes, or intermediate joins in memory.

3. Feature stores and real-time retrieval layers

   • If your “AI product” depends on low-latency feature retrieval, memory headroom is reliability.

4. Vector search backends and hybrid search caches

   • Not every vector workload needs a GPU. Many retrieval workloads are CPU + memory bound.

5. High-throughput caching and session/state layers

   • Especially where cache misses are expensive (database fan-out, cross-service calls).

Where X2iedn is not the best default

• GPU-heavy training: you’ll still want GPU instances for large-scale deep learning.
• Pure compute-bound batch jobs: general purpose or compute-optimized may be cheaper.
• Storage-heavy, scan-and-forget analytics: if the dataset won’t fit in memory anyway, you might prioritize I/O.

The point is to match the instance to the bottleneck. If your bottleneck is memory (capacity, bandwidth, or GC pressure), X2iedn belongs on your shortlist.

The AI-infrastructure angle: memory is the real enabler

Answer first: Most AI systems fail SLOs because of data-path latency and memory pressure, not model math; memory-optimized instances improve the data path.

Teams often talk about AI infrastructure like it’s a single choice: “Do we have GPUs?” But production AI is a pipeline:

• Data ingestion → cleaning → feature engineering
• Feature storage (offline + online)
• Retrieval at inference time
• Post-processing, ranking, and business rules

Even a modest model can look bad if retrieval is slow or inconsistent. Memory pressure causes:

• Paging and swap behavior that wrecks tail latency
• Longer GC pauses in JVM/managed runtimes
• Cache churn that makes performance unpredictable

A simple stance: If your data path can’t stay in memory, you’re paying an “AI tax” every millisecond. X2iedn instances are one of the more straightforward ways to buy that memory headroom without redesigning your entire stack.

A concrete scenario: AI personalization + SAP

A pattern I see in enterprise environments:

• Customer transactions and master data sit in SAP
• An analytics layer builds features (purchases, propensity signals, segment membership)
• Online applications request personalized recommendations

When the feature layer is under-provisioned on RAM, the system starts reading from slower tiers, and users feel it. Putting memory-optimized compute in Zurich can reduce round trips for Swiss-based digital channels and keep sensitive data closer to where policies want it.

Designing for Zurich: latency, residency, and operational simplicity

Answer first: If your users, data, or regulators are in Switzerland (or nearby), running memory-heavy tiers in Zurich reduces cross-region complexity and improves reliability.

Zurich is especially relevant for industries like finance, insurance, healthcare, and manufacturing HQs in Central Europe. The decision isn’t only performance; it’s architecture hygiene.

Three architecture wins you can usually expect

1. Fewer cross-region dependencies Cross-region calls are where “small” latencies become big incident reports. Keeping stateful, memory-heavy services close to apps is a reliability upgrade.

2. Cleaner failure domains When primary and critical dependencies sit in-region, your failover plan becomes more deterministic. Cross-region replication strategies are still useful, but they stop being the everyday baseline.

3. Better cost control for data movement Teams often underestimate the cost of moving data around—especially when analytics and inference touch large datasets frequently. Regional placement can cut that back.

A practical migration path (without a big-bang move)

If you’re already running in AWS but not in Zurich, consider a staged approach:

• Start with non-production in Zurich: performance testing, sizing, and cost baselines
• Move a single memory-heavy tier first (often cache, feature serving, or analytics workers)
• Keep replication and read-only patterns initially, then promote to primary when stable

The win is that you learn quickly whether your bottleneck is actually memory or something else (network topology, storage I/O, query patterns).

How to size X2iedn for memory-intensive workloads

Answer first: Start from peak working set + headroom, then validate with load tests that measure paging, GC, and 95th/99th percentile latency.

Memory sizing mistakes are expensive because they don’t show up as “CPU high.” They show up as jitter.

Here’s a checklist that tends to prevent bad surprises:

1) Measure the working set, not the dataset

• Working set = what must remain in RAM to hit your latency target.
• Dataset size is often irrelevant if access is skewed.

2) Add explicit headroom

I’ve found 20–40% headroom is a reasonable starting point for memory-heavy systems, depending on burstiness and GC behavior. If you’re running HANA, follow vendor sizing guidance and validate with realistic concurrency.

3) Monitor the right signals

For memory-bound services, CPU dashboards lie.

Track:

• Memory utilization and swap activity (paging is a red alarm)
• GC pause time (if applicable)
• 95th/99th percentile latency
• Cache hit rate or buffer pool hit rate

4) Validate price-performance, not just performance

AWS highlights improved price performance vs. X1e. Your job is to confirm it for your workload:

• Run the same load test on the old family and X2iedn
• Compare cost per request or cost per query, not just runtime

What this means for the “AI in Cloud Computing & Data Centers” story

Answer first: X2iedn in Zurich is another sign that cloud infrastructure is being tuned for data-heavy AI systems, not just model training.

Data centers and cloud platforms are being shaped by two pressures at once:

• AI adoption is pushing more real-time data paths into production systems.
• Enterprises want those systems to be regional, compliant, and predictable.

Expanding memory-optimized capacity in a strategic region like Zurich supports both. It’s infrastructure evolution in plain sight: the cloud is meeting AI where it actually hurts—data locality and memory.

If your AI roadmap only mentions models and GPUs, it’s incomplete. The systems that feed your models and serve your predictions are where reliability is won or lost.

Next steps: how to evaluate X2iedn in Zurich quickly

If you’re considering X2iedn instances in the AWS Zurich region, here’s a simple action plan for the next two weeks:

1. Pick one memory-sensitive workload (SAP HANA environment, feature store, analytics engine, cache layer).
2. Define a success metric (p95 latency, queries/min, batch duration, cost per run).
3. Reproduce production load (or as close as you can) and run a head-to-head test.
4. Decide based on unit economics: cost per transaction, cost per query, cost per training dataset build.

If you want a second opinion on which tier to move first—or how to avoid the usual migration traps—I’m happy to help map your architecture to a practical plan.

The forward-looking question worth asking is simple: If you could place your most memory-hungry systems exactly where your data and users are, what would you simplify first?