Automatic Semantic Enrichment in OpenSearch, Explained

Search teams have a dirty secret: a huge chunk of “bad search” isn’t an algorithm problem—it’s a data prep problem. If your content isn’t enriched with the right signals (entities, topics, meaning), even a solid search stack ends up acting like a strict spellchecker.

Amazon OpenSearch Service just added automatic semantic enrichment for managed clusters (OpenSearch 2.19+). Practically, this means you can get semantic search—the kind that understands meaning, not just exact keywords—without standing up your own model pipeline. And in the context of our AI in Cloud Computing & Data Centers series, this is a clear pattern: cloud providers are pushing AI features down into the platform so search relevance improves while operational overhead drops.

This matters because search is no longer a side feature. It’s the front door to analytics, observability, knowledge bases, and internal developer portals. When search improves, teams ship faster, tickets drop, and expensive human “data detective work” gets replaced by better retrieval.

What “automatic semantic enrichment” really changes

Automatic semantic enrichment is semantic indexing performed during ingestion, managed by the service. Instead of you provisioning a model, generating embeddings, versioning them, and keeping a pipeline alive, the platform handles semantic processing for you.

Traditional lexical search matches tokens (exact terms and close variants). Semantic search matches intent and context. So a query like “eco-friendly transportation options” can return documents about “electric vehicles” or “public transit” even if that phrase never appears.

Here’s the bigger shift: enrichment at ingestion time turns semantic search from a fragile “project” into a default capability you can apply to more datasets.

Lexical vs semantic: the practical difference

Lexical search is still great at:

• Exact error codes
• Part numbers
• Proper nouns
• Short, precise queries (“HTTP 502 ALB idle timeout”)

Semantic search is better at:

• Long, natural language queries (“why are my dashboards slow after scaling?”)
• Synonyms and paraphrases
• Concepts and categories (“data retention policy exceptions”)
• Multilingual retrieval (when supported)

Most companies need both. Semantic enrichment makes adding the semantic layer far less painful.

What AWS is actually offering (the concrete details)

Amazon OpenSearch Service automatic semantic enrichment:

• Works on Amazon OpenSearch Service domains running OpenSearch 2.19 or later
• Supports English-only and multilingual variants across 15 languages (including Arabic, French, Hindi, Japanese, Korean, and others)
• Bills semantic processing during ingestion as OpenSearch Compute Unit (OCU) – Semantic Search (usage-based)
• Is currently available for non-VPC domains in these Regions:
  • US East (N. Virginia), US East (Ohio), US West (Oregon)
  • Asia Pacific (Mumbai, Singapore, Sydney, Tokyo)
  • Europe (Frankfurt, Ireland, Stockholm)

If you’ve been waiting for semantic search because you didn’t want to own model operations, this feature is aimed at you.

Why this is part of a bigger AI-in-cloud trend

Cloud AI features that “just work” are rarely about novelty. They’re about shifting operational burden from customers to the provider. Automatic semantic enrichment is a classic example of AI integrated into cloud infrastructure usability.

In data center terms, this is also a resource-allocation story:

• You avoid always-on model hosts.
• You reduce custom ingestion pipelines.
• You pay for semantic processing when you ingest, not because a GPU instance is idling.

The real win is governance and reliability: fewer bespoke moving parts means fewer 2 a.m. incidents tied to “the embedding job failed again.”

Bridge point: relevance is a performance feature

We talk about cloud optimization as CPU utilization, storage tiering, and network throughput. But relevance is also performance:

• Better relevance reduces re-queries (less cluster load).
• Better relevance reduces time-to-answer (less human cost).
• Better relevance increases self-service (fewer tickets and escalations).

If your search UX forces users into repeated retries, your cluster pays for it and your support team pays for it.

Where semantic enrichment helps most (realistic use cases)

Semantic enrichment shines when documents are verbose, inconsistent, or authored by many people. That’s basically every modern organization.

1) Observability and incident response

In incident response, people search in a hurry and they don’t use the “right” words. With semantic retrieval, queries like:

• “latency spike after deploy”
• “timeouts when scaling”
• “pods restarting random”

…can surface runbooks and past incidents that don’t share the exact phrasing.

Practical result: faster Mean Time to Resolution (MTTR). Even small improvements matter when the business is down.

2) Enterprise knowledge bases and internal portals

Knowledge bases fail when they become a maze of slightly different articles. Semantic enrichment helps consolidate retrieval across:

• Confluence-style docs
• PDFs and policy documents
• Engineering wikis
• Support macros

It also helps with “I know we wrote this somewhere” queries—which is most internal search.

3) Analytics catalogs and data governance

Teams increasingly treat search as the interface to data: dataset discovery, metric definitions, lineage, and access rules. Semantic enrichment improves:

• Dataset matching when names don’t align with business terms
• Policy discovery (“PII handling for exports”)
• Cross-team reuse (fewer duplicate datasets)

This is one of the most direct connections to AI in cloud computing: improved retrieval makes your data platform more usable without rebuilding it.

4) Multilingual support and global operations

If your operations or customer support spans regions, multilingual semantic search reduces the “English-only” tax. When language coverage is built in, global teams stop maintaining parallel KBs that drift out of sync.

Cost, control, and “what could go wrong?” (the stuff you should plan for)

Automatic semantic enrichment reduces setup work, but it doesn’t remove architecture decisions. If you want this to drive leads and outcomes (not just experimentation), plan for these four areas.

1) Ingestion-time billing changes the optimization game

Because semantic processing is billed during ingestion as OCU – Semantic Search, your cost drivers shift toward:

• Volume of ingested content
• Frequency of reindexing
• Backfills and reprocessing

My stance: treat reindexing like a budgeted event, not an afterthought. If your team reindexes “whenever,” you’ll be surprised.

Operational tips that usually pay off:

• Start with your highest-value indices (runbooks, tickets, top KB spaces)
• Avoid enriching low-signal logs by default
• Batch large backfills in planned windows
• Track ingestion throughput and semantic OCU usage as first-class metrics

2) Relevance still needs evaluation (semantic ≠ correct)

Semantic search can return plausible-but-wrong results if your corpus has near-duplicates or contradictory docs.

Do a lightweight relevance program:

1. Capture the top 50 real queries from users (search logs or tickets)
2. Define what “good” looks like for each query (the expected doc set)
3. Compare lexical-only vs semantic-enabled results
4. Iterate with ranking and filters (freshness, doc type, ownership)

Even a simple test harness beats vibes.

3) Security and network constraints (non-VPC limitation)

Right now, AWS notes the feature supports non-VPC domains in specific Regions. If you’re standardized on VPC-only domains (common in regulated environments), you’ll need a plan:

• Decide whether a non-VPC domain is acceptable for specific datasets
• Segment indices so sensitive content stays on your strictest footprint
• Track AWS updates for expanded support

Don’t force a security compromise just to get semantic search.

4) Data quality and duplication are the silent killers

Semantic enrichment doesn’t fix:

• Outdated runbooks
• Duplicated KB articles
• Conflicting policy docs

If your corpus is messy, semantic search will faithfully retrieve messy answers faster.

A simple governance pattern that works:

• Assign an owner per doc collection
• Add freshness signals (last reviewed date)
• Deprecate aggressively (redirect rather than duplicate)

Implementation approach: a sensible rollout plan

If you want this to produce measurable impact (and justify investment), roll it out like a production feature.

Phase 1: Choose one index with clear ROI

Good candidates:

• On-call runbooks
• Customer support tickets and macros
• Internal KB for top products

Define success metrics before you turn anything on:

• Search success rate (clicks without reformulation)
• Time-to-first-click
• Ticket deflection rate
• MTTR changes for common incident types

Phase 2: Add semantic enrichment and keep lexical fallback

In practice, the best user experience often combines:

• Lexical for precision
• Semantic for recall
• Filters for trust (doc type, service, team ownership)

Users want the right answer quickly, not a philosophical debate about embeddings.

Phase 3: Tune relevance using business signals

Semantic retrieval becomes far more useful when you incorporate:

• Recency (prefer updated runbooks)
• Authority (prefer owned docs)
• Usage (prefer docs that historically solved issues)

This is where AI meets cloud operations: you’re using platform intelligence plus operational metadata to improve outcomes.

People also ask (and the straight answers)

Does semantic enrichment replace my existing search setup?

No. It augments it. Most teams run hybrid approaches because exact matching still matters for many technical queries.

Is this only for English?

No. AWS states support for English-only and multilingual variants covering 15 languages.

Do I need to manage ML models?

No. The point of automatic semantic enrichment is that the service handles semantic processing so you don’t run your own embedding models.

Will it increase my OpenSearch costs?

It can, depending on ingestion volume and reindexing habits. The clean way to control spend is to start with a single high-value index and measure usage-based semantic OCU.

Where this fits in the “AI in Cloud Computing & Data Centers” series

A lot of AI-in-infrastructure talk gets stuck at “add GPUs” or “optimize cooling.” Useful, but incomplete. The quieter story is that cloud providers are embedding AI into core services—search, storage, networking, observability—so you get better outcomes with fewer bespoke systems.

Automatic semantic enrichment in Amazon OpenSearch Service is that quieter story. It’s AI applied to a practical constraint: teams need semantic search, but they don’t want to operate a model pipeline for it.

If you’re considering semantic search for your analytics platform, observability stack, or internal knowledge base, the next step is simple: pick one dataset with an obvious payoff, enable semantic enrichment, and measure whether people stop re-asking the same questions.

What’s the one internal search experience in your org that everyone complains about—but nobody owns? That’s usually the best place to start.