OI2 Instances: Faster OpenSearch for AI Analytics

Amazon just gave search-heavy teams a very practical upgrade: new OI2 instances in Amazon OpenSearch Service. In AWS internal benchmarks, OI2 delivers up to 9% higher indexing throughput than OR2 and up to 33% higher than I8g. Those aren’t flashy numbers for the sake of it—they translate into something you feel quickly when you’re shoveling logs, traces, clickstreams, and vector-enriched documents into an OpenSearch domain.

In this AI in Cloud Computing & Data Centers series, we keep coming back to the same reality: AI features are only as good as the infrastructure feeding them. If your platform can’t ingest quickly, retain affordably, and query predictably, your “smart” dashboards and anomaly detectors turn into a backlog of delayed insights.

OI2 is interesting because it’s not merely “more compute.” It’s an infrastructure pattern: compute + Nitro SSD caching + S3-based managed storage designed for indexing-heavy workloads. That combination is exactly what modern AI-driven operations need—fast ingestion and durability without treating expensive local disks like a long-term archive.

What OI2 instances actually change (and why it matters)

OI2 changes the economics and performance of indexing by separating hot compute from durable storage. Instead of tying your long-term index storage to the same box that runs your indexing and query workloads, OI2 uses remote S3-based managed storage with 3rd generation AWS Nitro SSDs for caching.

Here’s why that matters for AI-driven platforms:

• AI observability is indexing-heavy by default. Logs, metrics, traces, LLM prompt/response logs, security events, and feature-store lookups can create sustained ingestion pressure.
• Indexing speed is a leading indicator for operational maturity. If ingestion lags, anomaly detection and incident response lag right behind it.
• Durability becomes non-negotiable. When your security or compliance team asks for 30/90/180 days of retention, your infrastructure must keep up without ballooning costs.

The reality? Most teams overspend because they design for peak ingestion using storage that’s priced like it’s always “hot.” OI2’s architecture is a direct push toward a more sensible model: keep what needs to be fast in cache, keep what needs to be durable in managed object storage.

The practical takeaway

If you’ve been scaling OpenSearch domains primarily by adding nodes “because indexing is slow,” OI2 gives you another lever: better indexing throughput per instance and a storage model that’s meant to stay durable without forcing you into the most expensive path.

OI2 vs. OR2 vs. I8g: how to think about the upgrade

The simplest way to evaluate OI2 is to treat it as an indexing-focused refresh in the optimized instance family. AWS states:

• Up to 9% higher indexing throughput than OR2
• Up to 33% higher indexing throughput than I8g

Those percentages matter most when indexing is a constant drumbeat (think: high-volume log analytics, clickstream ingestion, SIEM pipelines, or near-real-time product events). In those environments, even a single-digit improvement often means one of two outcomes:

1. You maintain SLAs with fewer nodes, or
2. You keep node count the same and absorb growth longer

Either way, this is infrastructure optimization—exactly the kind AI teams should care about. The model training and the dashboards get attention, but the bottleneck is frequently ingestion and indexing.

A quick “should I care?” checklist

OI2 is worth evaluating if any of these describe you:

• Your CPU spikes during ingest and stays high for long stretches.
• You’re adding shards or nodes mainly to keep up with indexing.
• You’re ingesting more high-cardinality telemetry (Kubernetes labels, trace attributes, feature flags, prompt metadata).
• You’ve started to store AI/LLM application telemetry, which tends to be verbose and bursty.
• You’re paying too much for storage just to meet retention.

Why OI2 fits the AI-in-data-centers story

AI-driven infrastructure optimization depends on stable, high-throughput data pipelines. That sounds abstract until you map it to the day-to-day:

• Your anomaly detector is only as good as the last few minutes of clean telemetry.
• Your capacity planner needs a reliable history of workload patterns.
• Your security analytics need complete, durable event trails.

OI2 helps here because the architecture matches how “intelligent” systems behave in production:

Faster ingestion supports real-time intelligence

When indexing throughput improves, you tighten the loop between:

• event happens → event indexed → insight generated → action taken

In operations, that loop is the difference between catching an incident early and spending the evening on a postmortem.

S3-based managed storage supports longer retention (without panic scaling)

AI ops and security teams increasingly want longer lookbacks:

• To spot slow-burning incidents
• To compare seasonal patterns (December traffic looks different than March)
• To measure drift or behavior changes after releases

OI2’s remote managed storage approach is aligned with that: treat durability and retention as first-class concerns, not an afterthought.

Nitro SSD caching is the “quiet hero”

Caching isn’t glamorous, but it’s often the difference between “works in staging” and “stable under peak.” Nitro SSD caching aims to keep hot data hot while letting managed storage do the long-term heavy lifting.

Snippet-worthy truth: Most search platforms aren’t limited by one resource—they’re limited by how well compute, cache, and storage cooperate under pressure.

A real-world scenario: AI operations + OpenSearch indexing pressure

Scenario: You run a platform team supporting an LLM-powered customer support product. Every user interaction generates:

• application logs
• prompt/response pairs (with redaction)
• latency traces
• retrieval and ranking signals
• safety filter events

During the holiday season, volume spikes. Support leaders want near-real-time dashboards. Security wants auditability. Engineering wants traces for regression detection.

This is where OpenSearch domains get stressed in familiar ways:

• indexing falls behind during bursts
• queues grow
• dashboards show stale data
• teams “solve it” by adding nodes and hoping costs don’t get flagged

OI2 is designed for this exact shape of problem: indexing-heavy, retention-heavy, operationally sensitive workloads. A 9% uplift over OR2 won’t change your architecture, but it can reduce how often you have to brute-force scale. A 33% uplift over I8g can be even more noticeable if you’re coming from that baseline.

How to evaluate OI2 without guesswork

You shouldn’t migrate instance families on vibes. Treat OI2 like any infrastructure change: define a workload, define success metrics, run a controlled test.

Step 1: Measure what’s actually hurting

Track these before and after:

• Indexing throughput (docs/sec or MB/sec)
• Indexing latency (time from event creation to searchable)
• CPU and memory headroom during sustained ingest
• Shard and segment behavior (are you oversharded?)
• Query latency under ingest (the “noisy neighbor” effect inside the cluster)

Step 2: Run a representative load test

Use a replay of production-like data:

• same field cardinality
• same document sizes
• same ingest burst patterns

If you’re testing with toy data, you’re testing nothing.

Step 3: Look for second-order wins

Indexing throughput gains are obvious, but the more valuable outcomes are often indirect:

• fewer ingestion backlogs during spikes
• less operational babysitting
• fewer emergency shard/replica changes
• more predictable cost per ingested GB

Step 4: Decide where OI2 fits in your domain strategy

I’ve found most teams do best when they intentionally separate workloads:

• Hot, high-ingest domains optimized for speed and freshness
• Longer-retention domains optimized for durability and cost

OI2 can support either, but it’s most compelling where indexing is the limiter.

Availability, sizing, and planning notes

OI2 instances are available in sizes from large through 24xlarge, offering compute, memory, and up to 22.5 TB storage per instance configuration. Pricing is offered via pay-as-you-go and reserved instances, with a simple hourly rate that includes NVMe storage as well as managed storage.

They’re available across 12 regions:

• US East (N. Virginia, Ohio)
• US West (Oregon)
• Canada (Central)
• Asia Pacific (Mumbai, Singapore, Sydney, Tokyo)
• Europe (Frankfurt, Ireland, London, Spain)

For platform teams, this matters because region availability often dictates where you can standardize. If you’re running multi-region observability or search, consistent instance options reduce operational drift.

People also ask: practical questions about OI2

Is OI2 only for AI workloads?

No. OI2 is for indexing-heavy OpenSearch workloads, and AI systems tend to create those workloads because they emit lots of telemetry and metadata. The fit is about ingest patterns, not whether you run a model.

Will OI2 make my searches faster?

Not automatically. AWS highlights indexing throughput improvements. Search performance depends on mappings, shard sizing, query patterns, cache hit rate, and cluster load. The win you’ll most reliably see is fresher data being searchable sooner, which users interpret as “faster.”

Do I still need to tune shards and mappings?

Yes. Instance improvements don’t fix oversharding, high-cardinality mapping explosions, or inefficient analyzers. OI2 helps when your fundamentals are solid and you’re hitting real infrastructure limits.

What to do next if you’re running OpenSearch for AI analytics

If your OpenSearch domain is the backbone for AI operations, security analytics, or real-time business intelligence, OI2 instances are worth a serious look—especially if indexing throughput is your chronic pain.

Here’s a sane next step plan:

1. Pick one indexing-heavy domain (observability, SIEM, clickstream) and baseline its ingest and query metrics.
2. Run a controlled test on OI2 with production-like replay data.
3. Quantify the result in dollars and latency, not just “it feels better.”
4. Decide on a rollout pattern (new domains first, then migrate existing ones during planned windows).

This series is about how AI changes cloud infrastructure priorities. OI2 is a good example of that shift: smarter systems generate more data, faster—and the infrastructure has to keep up without turning your budget into a bonfire.

If you’re planning your 2026 observability or AI ops roadmap, ask a pointed question: Are you optimizing for model performance—or for the pipeline that makes your models and operators effective?