EC2 C8i in Singapore: Faster AI, Smarter Compute Spend

A 2.5× jump in memory bandwidth changes the math on a surprising number of workloads—especially the ones that sit next to your AI systems but don’t look “AI” on the surface.

That’s why the new Amazon EC2 C8i and C8i-flex availability in Asia Pacific (Singapore) matters. It’s not just another instance launch. It’s a practical infrastructure upgrade for teams trying to run AI in cloud computing and data centers with tighter latency, more predictable performance, and better cost control.

If you’re supporting recommendation pipelines, real-time inference, retrieval services, feature stores, event streaming, web APIs, or caching layers in APAC, this release gives you a new default option: compute-optimized Intel instances with up to 15% better price-performance and up to 20% higher performance versus the previous generation—plus large gains for specific workloads like NGINX, Memcached, and deep learning recommendation models.

Why C8i in Singapore is a real AI infrastructure upgrade

Answer first: Putting higher-performance compute closer to users (and to your data) reduces end-to-end latency and makes AI systems cheaper to operate.

For many APAC teams, Singapore is the region that anchors production: it’s near major user populations and often sits at the center of multi-region architectures. When your model endpoints are “close enough” but your retrieval layer, feature serving, or API gateways are in a distant region, you feel it immediately—higher p95 latency, higher timeouts, bigger overprovisioning buffers.

C8i and C8i-flex arriving in Singapore helps in three concrete ways:

1. Lower latency by geography: AI-driven experiences are usually a chain: API → feature fetch → retrieval → model inference → cache write. Moving more of that chain into Singapore reduces transit time.
2. More throughput per node: If a node can serve more requests per second, you need fewer nodes to hit your SLOs.
3. Better resource allocation choices: C8i-flex is designed for workloads that don’t run “flat-out” on CPU 24/7. That’s most production systems.

In this series on AI-powered cloud optimization, the theme is consistent: the biggest wins come from matching infrastructure to real workload behavior, not from chasing peak benchmarks.

What’s different about C8i and C8i-flex (and what to choose)

Answer first: Pick C8i-flex when utilization is spiky or moderate; pick C8i when you need sustained high CPU, larger sizes, or memory-intensive performance.

Both instance families are powered by custom Intel Xeon 6 processors available only on AWS and are positioned as the highest performance / fastest memory bandwidth among comparable Intel processors in the cloud.

C8i-flex: the “default” for most compute-intensive services

AWS is explicit about the target: web and application servers, databases, caches, Kafka, Elasticsearch, enterprise apps. The key phrase is: “a great first choice for applications that don’t fully utilize all compute resources.”

That’s a polite way of describing reality. Most services have:

• daily peaks and troughs
• uneven traffic by endpoint
• background jobs that come and go
• CPU that oscillates between 20–60%

C8i-flex gives you a straightforward way to improve price-performance without redesigning the stack.

C8i: for sustained load, big sizes, and the “always-hot” tiers

C8i is where you go when your systems:

• run continuous high CPU usage
• need the largest instance sizes
• are sensitive to memory bandwidth and throughput

AWS notes 13 sizes, including two bare metal options and a new 96xlarge size. That matters if you’re consolidating large services, running big JVM estates, or operating high-throughput search/analytics services where fewer larger nodes can simplify operations.

Performance claims that matter for AI-driven architectures

Answer first: The most valuable improvements aren’t just “faster inference”—they’re faster supporting services that determine AI user experience.

AWS highlights a few headliners compared to C7i/C7i-flex:

• Up to 15% better price-performance
• 2.5× more memory bandwidth
• Up to 20% higher performance overall
• Up to 60% faster NGINX web applications
• Up to 40% faster AI deep learning recommendation models
• 35% faster Memcached stores

Here’s how those map to real AI-in-the-cloud systems.

NGINX faster = cheaper, tighter front doors for inference APIs

Many inference endpoints are gated by an API tier: TLS termination, routing, auth, rate limiting, A/B experiments. Even if the model is optimized, the front door can be the bottleneck.

If your NGINX layer is genuinely up to 60% faster, you can often:

• reduce the number of edge/API instances
• shrink latency variance under burst traffic
• stop overprovisioning “just in case”

That’s not glamorous, but it’s the difference between a model that works in staging and a model that stays fast on payday traffic.

Memcached faster = better feature serving and retrieval performance

Caching is the quiet workhorse of AI personalization: session features, user embeddings, ranking candidates, experiment configs. A 35% gain at the cache tier often turns into fewer cache nodes or more headroom for the same spend.

In practice, I’ve found caching layers get sized defensively because cache misses create cascading load downstream. If the cache tier is faster, you can reduce that “cascade risk,” and your whole system becomes easier to operate.

Deep learning recommendation models faster = more options per dollar

AWS calls out up to 40% faster deep learning recommendation models. Recommendation workloads frequently involve:

• dense feature preprocessing
• embedding lookups
• large batch inference for feeds
• frequent retraining/refresh cycles

When performance improves at the compute layer, you can choose your trade-off:

• Keep cost flat and serve more traffic
• Keep throughput flat and lower cost
• Keep cost flat and reduce latency

That flexibility is what “intelligent resource allocation” looks like in the real world.

A practical migration plan (that doesn’t blow up your quarter)

Answer first: Treat this as a controlled performance experiment: benchmark, right-size, and then commit with discounting once you’ve got numbers.

Here’s a migration approach that works for teams who want gains without drama.

1) Start with one tier that touches everything

Pick one of these:

• API gateway / NGINX fleet
• cache tier (Memcached)
• Kafka brokers (if you’re CPU-bound)
• retrieval or feature serving service

Why? Improvements in these tiers tend to show up quickly across multiple products.

2) Benchmark the way your customers feel it

Don’t benchmark with synthetic CPU loops and call it done. Track:

• p50/p95/p99 latency
• error rate and timeouts
• requests per second per node
• CPU utilization distribution over 24 hours
• cost per 1M requests (or per 1K inferences)

Then run a canary with C8i-flex or C8i, depending on utilization.

3) Right-size aggressively after you switch

A common failure mode: teams move to a faster instance type and keep the same sizing. You get a nice latency bump, but you don’t get the cost win.

Do a second pass:

• reduce instance count until you’re back to your target CPU headroom
• re-evaluate autoscaling thresholds (faster nodes change scaling behavior)
• confirm cache hit rate and eviction patterns (faster cache can change write/read ratios)

4) Choose the purchase model after the data

AWS offers On-Demand, Spot, and Savings Plans for these instances.

A sensible pattern:

1. On-Demand for initial testing
2. Spot for stateless batch jobs (ETL, offline ranking, training data prep) once stable
3. Savings Plans for steady-state tiers after 2–4 weeks of production metrics

This is also aligned with AI-era ops: steady services get commitments, bursty pipelines use elasticity.

Data center strategy: performance per watt is the new KPI

Answer first: Faster compute reduces the number of servers you need for the same work, which is one of the most practical paths to energy-efficient computing.

The cloud conversation in late 2025 is heavily shaped by capacity planning, energy constraints, and AI demand. Even when you can’t measure watts directly, you can manage a proxy that usually correlates: work completed per hour per dollar.

If C8i/C8i-flex truly deliver up to 15% better price-performance, there’s a direct operational implication:

If you can retire 15% of a fleet for the same throughput, you reduce cost, operational overhead, and likely power consumption.

This is the part many teams miss. AI optimization isn’t only about GPUs. It’s about the surrounding compute fabric that feeds GPUs, serves users, and keeps the pipeline moving.

Common questions teams ask before switching

Answer first: The right instance choice depends on utilization patterns, not labels like “AI” or “web.”

Should I pick C8i-flex or C8i for inference?

If your inference service is spiky—traffic peaks, batch jobs, uneven endpoints—C8i-flex is usually the safer first move. If your inference nodes run hot all day and you want larger options or sustained high CPU, C8i is the better fit.

Is memory bandwidth really that important for AI systems?

Yes, because many AI-adjacent services are memory-bound: feature stores, retrieval indexes, caching, embedding lookups, and parts of data preprocessing. A 2.5× memory bandwidth increase can turn a “CPU looks fine but latency is bad” situation into a stable service.

What’s the fastest path to proving ROI?

Move a tier that’s easy to measure (NGINX or cache), canary it, then right-size. If you can quantify a drop in nodes required for the same SLOs, you’ve got your business case.

Next steps: turn this release into measurable wins

The availability of EC2 C8i and C8i-flex instances in Singapore is a straightforward opportunity: improve performance, reduce overprovisioning, and tighten the latency of AI-driven experiences across APAC.

If you’re building on the AI in Cloud Computing & Data Centers playbook, use this as your reminder that “AI infrastructure” includes the boring parts—web servers, caches, data movers, and retrieval layers. They’re often where the easiest gains hide.

If you want help picking between C8i and C8i-flex, designing a benchmark, or building an intelligent resource allocation plan that reduces spend without risking SLOs, map out your top three CPU-heavy services and their utilization curves. Which one would you bet improves first with higher memory bandwidth and a closer region footprint?