Faster Vector Indexing on OpenSearch with GPUs

Vector search is where a lot of AI projects quietly stall out. Not because embeddings are hard to generate—but because building and tuning the vector index becomes a time sink and a cost surprise.

AWS just addressed that bottleneck in Amazon OpenSearch Service with two practical improvements: serverless GPU acceleration for vector index builds and auto-optimization for vector indexes. The headline numbers are worth paying attention to: AWS reports you can build vector databases up to 10× faster and at about one-quarter of the indexing cost compared to non-GPU indexing, with observed benchmark speedups ranging from 6.4× to 13.8×.

This post is part of our AI in Cloud Computing & Data Centers series, so I’m going to frame this the way infrastructure and platform teams actually feel it: faster index builds, fewer tuning weeks, more predictable spend, and better resource allocation in the cloud.

Why vector indexing is the real scaling problem

Vector search performance lives or dies in the index. You can generate embeddings with a managed model, store vectors, and still fail at production scale if indexing can’t keep up with refresh cycles, re-embeddings, or data growth.

Here’s what typically breaks first:

• Index build time: You re-embed a catalog or knowledge base and suddenly indexing takes hours (or days). That blocks releases and slows experimentation.
• Cost blow-ups during ingestion: Index building is compute-heavy. If you run it on general-purpose instances, you pay for brute force.
• Manual tuning: The “best” configuration depends on your recall targets, latency SLOs, and memory limits. Teams can spend weeks tweaking parameters and still not be sure.

From a cloud data center perspective, this is classic inefficiency: heavy workloads running on the wrong hardware, plus humans doing repetitive optimization work that software could handle.

What AWS changed: GPU acceleration + auto-optimization

The upgrade is simple to describe and big in impact: OpenSearch Service can now use GPUs to accelerate vector index builds, and it can recommend index configurations automatically based on your needs.

GPU acceleration: faster builds without managing GPU fleets

GPU acceleration in OpenSearch Service targets the indexing path, not your application servers. When enabled, the service detects opportunities to accelerate vector indexing workloads and uses GPU-backed compute to build the vector data structures.

What matters operationally:

• No GPU provisioning: You don’t pick GPU instance types, manage capacity, or worry about idle GPU burn.
• Isolation stays intact: Accelerated workloads are isolated to your OpenSearch domain/collection VPC in your account.
• Pay for used acceleration: You’re charged for consumed OpenSearch Compute Units (OCU) for Vector Acceleration during indexing.

That “pay only for useful processing” part is the real cloud optimization story here. In data center terms, it’s closer to burstable specialized hardware than “buy more servers and hope.”

Auto-optimization: fewer weeks of index tuning

Auto-optimization aims at the part most companies get wrong: index parameter selection. In practice, teams either:

• leave defaults in place and accept mediocre recall/latency, or
• sink weeks into manual tuning and benchmarking.

OpenSearch Service now offers recommendations that balance:

• search quality (recall)
• latency targets (for example, p90)
• memory requirements and cost

The important nuance: these trade-offs are real. “High recall, low latency, low cost” isn’t a free combo. Auto-optimization helps you pick a good, explainable compromise quickly.

How this supports AI infrastructure optimization (beyond search)

Vector databases are infrastructure for AI applications. If indexing is slow or expensive, everything built on top of it becomes fragile.

Here’s how these capabilities map directly to the AI-in-cloud theme—AI-driven workload management and intelligent resource allocation.

Intelligent resource allocation: put the right silicon on the right task

Index building is mathematically heavy and parallelizable. GPUs are built for that. Running vector index builds on general CPUs is like pushing a data center cooling system to the limit because you refused to use variable-speed fans.

When OpenSearch Service bursts GPU acceleration during indexing:

• you shorten maintenance windows for re-indexing
• you reduce the compute-hours needed to reach a ready state
• you can re-embed more frequently (which improves relevance over time)

If you’re operating multi-tenant platforms or shared clusters, that can also reduce noisy-neighbor risk because indexing finishes faster.

Efficiency shows up as cost, but also as release velocity

AWS claims up to 10× faster builds and ~75% lower indexing cost (indexing cost reduced to a quarter). That doesn’t just help your bill.

It changes what teams attempt:

• weekly (or even daily) refreshes of product embeddings
• rapid A/B testing of embedding models (768-d today, maybe 1024-d tomorrow)
• rebuilding indexes after schema changes without planning a weekend outage

I’ve found this is where “AI infrastructure” stops being theory. Faster index rebuilds directly translate to more experiments shipped per month.

Auto-optimization is a form of operational AI

Auto-optimization is basically platform intelligence applied to a hard-to-staff specialty. Most teams don’t have deep vector indexing expertise in-house—and they shouldn’t need it to hit reasonable SLOs.

This is the direction modern cloud operations is heading: the platform absorbs complexity and exposes policy choices (“I need p90 latency around X” or “recall must be ≥ 0.9”), not obscure tuning knobs.

Practical ways to use this in real deployments

The AWS announcement focuses on the “what.” Let’s talk about the “how do I use this without creating a mess.”

Use case patterns that benefit immediately

You’ll feel the impact fastest if you have one of these patterns:

1. Large product catalogs (retail, marketplaces)

   • Frequent updates and lots of near-duplicates
   • Need fast re-indexing as products change

2. Enterprise knowledge bases for RAG

   • Bulk ingestion from document stores
   • Periodic re-embedding as you change chunking or embedding models

3. Recommendations and personalization

   • Vector indexing tied to user/event streams
   • Tight latency budgets where you can’t afford bloated memory configs

A simple “build pipeline” that matches how teams work

A pragmatic production flow looks like this:

• Land data in object storage (commonly parquet files)
• Run a managed ingestion job to generate embeddings and ingest
• Enable auto-optimization to pick an initial configuration
• Enable GPU acceleration to compress index build time and cost
• Validate recall/latency on a known test set before promoting

This is less about fancy architecture diagrams and more about repeatability. You want a pipeline you can re-run after embedding model updates or corpus changes.

Guardrails: what to measure so you don’t optimize the wrong thing

If you only measure “index build time,” you’ll miss the point. Track these instead:

• Index build duration (minutes/hours) and how it scales with corpus size
• Indexing cost per million vectors (normalize so you can compare runs)
• Recall at K (for example, recall@10) on a labeled validation set
• p90 query latency under realistic concurrency
• Memory footprint of the vector index (because it drives steady-state cost)

Auto-optimization helps you pick trade-offs, but you still need a scoreboard.

Snippet-worthy rule: Optimize vector search like an SRE problem—define SLOs first, then tune.

Where teams still need to be careful

Two common mistakes show up in vector projects even with better indexing:

• Treating “recall” as optional: If retrieval quality is weak, your RAG system will hallucinate with confidence. A fast wrong answer is still wrong.
• Ignoring re-index frequency: If your data changes daily but you index monthly, relevance decays. Faster GPU indexing makes frequent refreshes feasible—use that.

Implementation notes you can hand to your platform team

Enabling GPU acceleration is a settings change, not a redesign. OpenSearch Service allows enabling GPU acceleration when creating or updating a domain or a serverless collection.

At the index level, you can configure a vector index designed to build remotely for GPU processing by enabling:

• index.knn.remote_index_build.enabled: true

On the ingestion side, OpenSearch Service now supports a vector ingestion workflow that can:

• ingest documents from object storage
• generate vector embeddings
• apply auto-optimization recommendations
• build large-scale vector indexes quickly

One current constraint to plan around: auto-optimization is limited to one vector field during the automated job, and you add additional mappings after the job completes.

People also ask: quick answers

Does GPU acceleration speed up vector queries too?

This AWS capability is described as accelerating vector index builds (indexing and force-merge operations). Query performance will still depend on your chosen index configuration, recall targets, and steady-state resources.

Is auto-optimization “set it and forget it”?

It’s a strong starting point, not the end. Use it to get to an acceptable configuration quickly, then validate against your recall and latency SLOs.

When does this matter most?

When you rebuild indexes often or you’re heading toward hundreds of millions to billions of vectors. That’s where build time and tuning effort become the gating factors.

Where this is heading for AI in cloud data centers

Vector search is becoming baseline infrastructure for generative AI apps, and that means cloud platforms are under pressure to run it efficiently. GPU acceleration for indexing is a straightforward example of specialized hardware allocation: don’t waste CPU-hours on workloads GPUs finish faster.

Auto-optimization is the other half of the story: software that makes performance-cost trade-offs explicit and faster to reach. That’s exactly the direction AI-powered cloud operations should go—less manual tuning, more policy-driven performance.

If you’re planning your 2026 roadmap for RAG, search, or recommendations, a good question to ask internally is this: How often do we want to refresh embeddings, and what’s stopping us today—compute cost, tuning time, or operational risk?