EC2 I7i Expansion: Faster Storage for AI Workloads

A lot of cloud teams still treat “region availability” like a procurement checkbox: Is it in my region yet? But for AI-driven systems—especially the ones that read and write constantly—region expansion changes the architecture math.

AWS just expanded EC2 I7i storage-optimized instances into Asia Pacific (Singapore, Jakarta) and Europe (Stockholm). These instances bring up to 23% better compute performance and more than 10% better price performance versus the prior-generation I4i. On the storage side, I7i runs 3rd generation AWS Nitro SSDs with up to 45 TB of local NVMe, plus claims of up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower latency variability.

This post is part of our “AI in Cloud Computing & Data Centers” series, so I’m going to connect the dots: what this expansion means for latency-sensitive platforms, how AI-driven workload management benefits from more region options, and how to decide if I7i is a smart move for your data-heavy workloads.

What I7i regional expansion actually changes

Answer first: More I7i regions means you can place high-IO workloads closer to users and data sources without giving up predictable NVMe performance.

When storage latency is your bottleneck, you don’t “optimize” your way out with clever caching forever. You eventually need better locality (distance matters) and better storage behavior (variance matters). Expanding I7i into Singapore, Jakarta, and Stockholm gives teams more options to keep hot datasets and write-heavy pipelines near where traffic and data originate.

Latency isn’t just about speed—it’s about consistency

For many AI and analytics systems, average latency is less painful than spiky latency. Spikes create queue buildup, tail latency, and unpredictable job completion times. AWS is explicitly calling out lower latency variability, which is a big deal for:

• Feature stores that serve online inference (lots of small reads)
• Vector search and retrieval workloads that do many random I/Os
• Streaming pipelines that are sensitive to backpressure
• High-frequency transactional systems that feed ML signals

If you’ve ever had an inference service that looks fine at p50 and miserable at p99, you already know why variance matters.

Regional expansion is a workload-management tool

The hidden value of “more regions” is operational: you get more flexibility in placement, failover, and traffic shaping. Modern platforms increasingly rely on automation (and yes, AI) to decide where work should run.

With more regions offering I7i, it’s easier to:

• Keep data residency constraints while still using high-IO compute
• Run active-active or warm standby setups for critical services
• Route latency-sensitive traffic to the closest region with the right instance supply

In other words, I7i in more places doesn’t just improve one server—it improves the system’s decision space.

Why I7i fits AI workloads that are storage-bound

Answer first: I7i is built for workloads where random IOPS, tight latency, and local NVMe throughput determine system performance more than raw CPU.

AI is often described as “GPU-first,” but a lot of AI pain is actually storage pain. GPUs can sit idle waiting on data. CPUs can stall on random reads. Distributed training and retrieval pipelines can become coordination problems caused by inconsistent I/O.

I7i targets a specific class of problems: small-to-medium objects, huge query volume, and low tolerance for jitter.

Common AI-adjacent patterns where I7i shines

Here are a few “I7i-shaped” scenarios I see most often:

1. Vector retrieval and RAG infrastructure
   If you’re running embeddings search with frequent updates, random reads are constant. Low latency variability helps keep retrieval predictable, which keeps end-to-end AI response times steady.

2. Real-time feature serving
   Online inference depends on fast feature lookups. If your feature store is local-NVMe backed (or uses local NVMe for caching/compaction), you care about consistent small-block performance.

3. High-ingest observability + anomaly detection
   Logs/metrics/traces platforms do relentless writes plus query bursts. If you’re doing near-real-time detection, ingestion delays directly reduce detection quality.

4. High-throughput ETL feeding model training
   Training pipelines can be “CPU and storage first” before they’re “GPU first.” Local NVMe can make preprocessing and shuffling much faster.

The torn write prevention detail is not trivia

AWS notes torn write prevention with up to 16 KB block sizes. If you’re running databases or storage engines that commit small blocks, torn writes can be a nasty source of corruption risk or performance overhead (because you compensate with extra logging and barriers).

A practical stance: if your workload has frequent small-block writes and you’re currently paying a performance tax to stay safe, this feature can translate into real throughput gains—or simpler operational posture.

The “AI behind the curtain”: how providers scale instance capacity across regions

Answer first: AI helps cloud providers forecast demand, place capacity, and reduce waste—so new instance types can land in new regions without constant overprovisioning.

Region expansion isn’t just “ship hardware to a building.” It’s capacity planning, supply chain timing, rack-level power and cooling, network design, fleet health, and live operations. In 2025, a lot of that work is increasingly guided by ML models.

Where AI actually helps (and where it doesn’t)

Cloud infrastructure teams use predictive models for things like:

• Demand forecasting: anticipating which instance families will be adopted in each region and industry segment
• Capacity placement: deciding how much inventory to allocate across regions to reduce stockouts
• Failure prediction: spotting SSD or host degradation early to migrate customers before incidents
• Energy-aware scheduling: shifting flexible jobs to reduce peak strain while keeping SLAs

But AI doesn’t magically eliminate constraints. The hard limit is still physics and logistics: power availability, delivery lead times, and regional build-out schedules. The win is that AI makes those constraints less painful by reducing guesswork.

Why this matters to your architecture

As customers, you benefit when the provider’s “placement intelligence” improves because you see:

• More stable availability of the instance family you standardize on
• Better consistency in performance profiles (less noisy neighbor behavior, fewer surprise throttles)
• Faster time-to-region for new hardware generations

If you’re building AI services with global users, provider-side intelligence becomes part of your reliability story.

Practical guidance: when to choose I7i (and when not to)

Answer first: Choose I7i when local NVMe latency and IOPS drive your SLA; don’t choose it when your bottleneck is network-bound storage, GPU compute, or infrequent batch I/O.

I7i offers up to 100 Gbps network bandwidth and up to 60 Gbps EBS bandwidth, and it comes in 11 sizes (up to 48xlarge, plus two bare metal options). That’s plenty of headroom—but you still want to match the instance to the real constraint.

Choose I7i if you’re dealing with these constraints

• Your workload is random-read heavy (indexes, vectors, key-value lookups)
• You’re chasing p95/p99 latency, not just throughput
• You have multi-terabyte hot datasets that benefit from local NVMe
• You run databases that hit small-block write bottlenecks

Think twice if these are your dominant constraints

• You primarily need GPU acceleration (training/inference where GPU is the limiter)
• Your data lives mostly on object storage and is accessed sequentially in batch
• You’re limited by cross-region replication latency (no instance fixes distance)
• You can’t operationally handle local NVMe lifecycle (ephemeral storage planning)

A simple migration plan that avoids surprises

If you’re currently on I4i (or another storage-optimized family) and considering I7i, here’s what works:

1. Benchmark the right metric: focus on p95/p99 latency and queue depth, not just average IOPS.
2. Test realistic concurrency: many storage systems look great until thread count rises.
3. Measure end-to-end impact: track application-level SLIs (request latency, job completion time).
4. Plan for local NVMe behavior: treat it as fast-and-local, not “durable forever.”
5. Validate cost per outcome: compare $/query, $/ingested GB, $/training epoch—something tied to value.

A stance I’ll defend: if you don’t tie the evaluation to a business-facing SLI, you’ll end up debating instance specs instead of shipping improvements.

People also ask: quick answers for teams evaluating I7i

Is I7i only for databases?

No. Databases are a strong fit, but any system needing predictable random I/O—feature stores, vector indexes, streaming state stores—can benefit.

Does adding more regions help AI workload management?

Yes. More regions with the same high-performance instance family improves placement options for latency, residency, and resilience. That makes automated scheduling and policy-driven routing more effective.

What’s the biggest performance risk when switching instance families?

It’s usually not peak throughput—it’s tail latency under realistic concurrency. Benchmark with production-like workloads and traffic patterns.

What to do next if you’re building AI systems on global cloud infrastructure

EC2 I7i expanding into Singapore, Jakarta, and Stockholm is a practical sign of where infrastructure is heading: more specialized instances in more locations, and more opportunity for AI-driven routing and capacity decisions.

If you’re running AI-enabled products that depend on fast retrieval, online feature access, or write-heavy pipelines, this is a good moment to revisit two design choices:

• Where should the hot data live so your p99 stays predictable?
• Which workloads can be scheduled intelligently (by policy or AI) across regions without breaking your SLA?

If your team wants a second set of eyes, I can help you map your workload to the right storage and region strategy—benchmarks, migration plan, and a cost model tied to real SLIs. What’s the one latency-sensitive component in your stack that you’d most like to make boring and predictable?